diff --git a/api/_images/tutorials_nihcxr_monitor_api_10_1.png b/api/_images/tutorials_nihcxr_monitor_api_10_1.png index 495d30903..07b2b9ce7 100644 Binary files a/api/_images/tutorials_nihcxr_monitor_api_10_1.png and b/api/_images/tutorials_nihcxr_monitor_api_10_1.png differ diff --git a/api/_images/tutorials_nihcxr_monitor_api_12_0.png b/api/_images/tutorials_nihcxr_monitor_api_12_0.png index f4dab633a..f5cd4037a 100644 Binary files a/api/_images/tutorials_nihcxr_monitor_api_12_0.png and b/api/_images/tutorials_nihcxr_monitor_api_12_0.png differ diff --git a/api/_images/tutorials_nihcxr_monitor_api_6_0.png b/api/_images/tutorials_nihcxr_monitor_api_6_0.png index aff4a6ebe..a84d09528 100644 Binary files a/api/_images/tutorials_nihcxr_monitor_api_6_0.png and b/api/_images/tutorials_nihcxr_monitor_api_6_0.png differ diff --git a/api/_images/tutorials_nihcxr_monitor_api_8_0.png b/api/_images/tutorials_nihcxr_monitor_api_8_0.png index e3ecb6e6c..457e8a836 100644 Binary files a/api/_images/tutorials_nihcxr_monitor_api_8_0.png and b/api/_images/tutorials_nihcxr_monitor_api_8_0.png differ diff --git a/api/_sources/intro.rst.txt b/api/_sources/intro.rst.txt index 125c6e38c..36c728c5a 100644 --- a/api/_sources/intro.rst.txt +++ b/api/_sources/intro.rst.txt @@ -25,7 +25,7 @@ models for healthcare. It provides a few high-level APIs namely: - ``evaluate`` - Evaluate models on clinical prediction tasks - ``monitor`` - Detect dataset shift relevant for clinical use cases - ``report`` - Create `model report - cards `__ + cards `__ for clinical ML models ``cyclops`` also provides a library of end-to-end use cases on clinical @@ -73,7 +73,7 @@ Multiple extras could also be combined, for example to install with both python3 -m pip install 'pycyclops[report,models]' 🧑🏿‍💻 Developing -============= +======================= Using poetry ------------ diff --git a/api/_sources/tutorials/kaggle/heart_failure_prediction.ipynb.txt b/api/_sources/tutorials/kaggle/heart_failure_prediction.ipynb.txt index b921ff863..40aad9a51 100644 --- a/api/_sources/tutorials/kaggle/heart_failure_prediction.ipynb.txt +++ b/api/_sources/tutorials/kaggle/heart_failure_prediction.ipynb.txt @@ -1211,7 +1211,7 @@ "tags": [] }, "source": [ - "You can view the generated HTML [report](./model_card.html)." + "You can view the generated HTML [report](./heart_failure_report_periodic.html)." ] } ], diff --git a/api/_sources/tutorials/nihcxr/cxr_classification.ipynb.txt b/api/_sources/tutorials/nihcxr/cxr_classification.ipynb.txt index 921311294..1c1dbaaad 100644 --- a/api/_sources/tutorials/nihcxr/cxr_classification.ipynb.txt +++ b/api/_sources/tutorials/nihcxr/cxr_classification.ipynb.txt @@ -626,7 +626,7 @@ "id": "d7545d39", "metadata": {}, "source": [ - "You can view the generated HTML [report](./model_card.html)." + "You can view the generated HTML [report](./nihcxr_report_periodic.html)." ] } ], diff --git a/api/_sources/tutorials/synthea/los_prediction.ipynb.txt b/api/_sources/tutorials/synthea/los_prediction.ipynb.txt index 4eacc6c15..ba6c7a496 100644 --- a/api/_sources/tutorials/synthea/los_prediction.ipynb.txt +++ b/api/_sources/tutorials/synthea/los_prediction.ipynb.txt @@ -1489,7 +1489,7 @@ "id": "0d953f1b-845d-424e-b8b6-d782973d9e84", "metadata": {}, "source": [ - "You can view the generated HTML [report](./model_card.html)." + "You can view the generated HTML [report](./length_of_stay_report_periodic.html)." ] } ], diff --git a/api/intro.html b/api/intro.html index c37b85157..edc274a14 100644 --- a/api/intro.html +++ b/api/intro.html @@ -196,7 +196,7 @@

  • evaluate - Evaluate models on clinical prediction tasks

  • monitor - Detect dataset shift relevant for clinical use cases

    • -

  • report - Create model report +

  • report - Create model report cards for clinical ML models

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Generate Source/Target Dataset for Experiments (1-2)": [[134, "Example-1.-Generate-Source/Target-Dataset-for-Experiments-(1-2)"]], "Example 2. Sensitivity test experiment with 3 dimensionality reduction techniques": [[134, "Example-2.-Sensitivity-test-experiment-with-3-dimensionality-reduction-techniques"]], "Example 3. Sensitivity test experiment with models trained on different datasets": [[134, "Example-3.-Sensitivity-test-experiment-with-models-trained-on-different-datasets"]], "Example 4. Sensitivity test experiment with different clinical shifts": [[134, "Example-4.-Sensitivity-test-experiment-with-different-clinical-shifts"]], "Example 5. 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"module-cyclops.data.features"]], "cyclops.evaluate": [[127, "module-cyclops.evaluate"]], "cyclops.evaluate.fairness": [[127, "module-cyclops.evaluate.fairness"]], "cyclops.evaluate.metrics": [[127, "module-cyclops.evaluate.metrics"]], "cyclops.evaluate.metrics.functional": [[127, "module-cyclops.evaluate.metrics.functional"]], "cyclops.monitor": [[128, "module-cyclops.monitor"]], "cyclops.report": [[129, "module-cyclops.report"]], "cyclops.tasks": [[130, "module-cyclops.tasks"]]}}) \ No newline at end of file diff --git a/api/tutorials/kaggle/heart_failure_prediction.html b/api/tutorials/kaggle/heart_failure_prediction.html index 1490ebf08..2cb628226 100644 --- a/api/tutorials/kaggle/heart_failure_prediction.html +++ b/api/tutorials/kaggle/heart_failure_prediction.html @@ -548,7 +548,7 @@

    Data Loading 
    -2023-11-07 13:00:22,223 INFO cyclops.utils.file - Loading DataFrame from ./data/heart.csv
+2023-11-07 17:09:48,018 INFO cyclops.utils.file - Loading DataFrame from ./data/heart.csv
    @@ -630,9 +630,9 @@

    Sex values

    -
    + + + + + +     + +
    + + + + +
    +

    How is your model doing?


    +

    A quick glance of your most important metrics.

    + + + + + + + + + + + + + + + + + + + + + + + +
    +
    + +
    + Accuracy +
    + The proportion of all instances that are correctly predicted. +
    +
    +
    +     +
    + + 0.58 + + + + ▼ + + + + 0.7
    minimum
    threshold     +     + + +
    + +
    + +
    + + +
    +
    +
    + + + + + +
    +
    + +
    + Precision +
    + The proportion of predicted positive instances that are correctly predicted. +
    +
    +
    +     +
    + + 0.97 + + + + ▲ + + + + 0.7
    minimum
    threshold     +     + + +
    + +
    + +
    + + +
    +
    +
    + + + + + +
    +
    + +
    + Recall +
    + The proportion of actual positive instances that are correctly predicted. Also known as recall or true positive rate. +
    +
    +
    +     +
    + + 0.98 + + + + ▲ + + + + 0.7
    minimum
    threshold     +     + + +
    + +
    + +
    + + +
    +
    +
    + + + + + +
    +
    + +
    + F1 Score +
    + The harmonic mean of precision and recall. +
    +
    +
    +     +
    + + 0.8 + + + + ▲ + + + + 0.7
    minimum
    threshold     +     + + +
    + +
    + +
    + + +
    +
    +
    + + + + + +
    +
    + +
    + AUROC +
    + The area under the receiver operating characteristic curve (AUROC) is a measure of the performance of a binary classification model. +
    +
    +
    +     +
    + + 0.81 + + + + ▲ + + + + 0.7
    minimum
    threshold     +     + + +
    + +
    + +
    + + +
    +
    +
    + + + +
    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    +

    How is your model doing over time?


    +

    See how your model is performing over several metrics and subgroups over time.

    +
    +
    +

    Metrics

    +
    + + +
    + +
    + The proportion of all instances that are correctly predicted. +
    +
    +
    + + + + + +
    + +
    + The proportion of predicted positive instances that are correctly predicted. +
    +
    +
    + + + + + +
    + +
    + The proportion of actual positive instances that are correctly predicted. Also known as recall or true positive rate. +
    +
    +
    + + + + + +
    + +
    + The harmonic mean of precision and recall. +
    +
    +
    + + + + + +
    + +
    + The area under the receiver operating characteristic curve (AUROC) is a measure of the performance of a binary classification model. +
    +
    +
    + + +
    +
    + +
    +

    Age

    +
    + + + + + + + + + +
    +
    + +
    +
    +
    + + +
    +
    +
    + + + + + + + + + +
    +

    Datasets

    + + + + + + +
    +

    Description

    + This dataset was created by combining different datasets + already available independently but not combined before. In this dataset, + 5 heart datasets are combined over 11 common features. Every dataset used + can be found under the Index of heart disease datasets from UCI + Machine Learning Repository on the following link: + https://archive.ics.uci.edu/ml/machine-learning-databases/heart-disease/. +
    + + + + + + + + + + + + + + + + + + + + +
    +

    Version

    +
      + +
      + + +
    • + + + + + Version 1 + + +
      + + + + + + +
      • + + +
      + +
    +
    + + + + + +
    +

    Features

    + +
      + +
      + + +
    • + Age +
      • + + +
      + +
    + +
      + +
      + + +
    • + ChestPainType +
      • + + +
      + +
    + +
      + +
      + + +
    • + Cholesterol +
      • + + +
      + +
    + +
      + +
      + + +
    • + ExerciseAngina +
      • + + +
      + +
    + +
      + +
      + + +
    • + FastingBS +
      • + + +
      + +
    + +
      + +
      + + +
    • + MaxHR +
      • + + +
      + +
    + +
      + +
      + + +
    • + Oldpeak +
      • + + +
      + +
    + +
      + +
      + + +
    • + RestingBP +
      • + + +
      + +
    + +
      + +
      + + +
    • + RestingECG +
      • + + +
      + +
    + +
      + +
      + + +
    • + ST_Slope +
      • + + +
      + +
    + +
      + +
      + + +
    • + Sex +
      • + + +
      + +
    + +
    + + + + + + + + + + + + + + + + + + + + +
    +

    Sensitive Data

    +
      + +
      + + +
    • + + + + + + + Sensitive Data Used: ['Sex', 'Age'] + + +
      + + + + + + Justification: Demographic information like age and gender often have a strong correlation with health outcomes. For example, older patients are more likely to have a higher risk of heart disease. + + +
      + + +
      • + + +
      + +
    +
    + + + + + +
    +

    Reference

    + +
    + + + + + +
    +

    Citation

    +
      + +
      + +
    • + + + @misc{fedesoriano, + title={Heart Failure Prediction Dataset.}, + author={Fedesoriano, F}, + year={2021}, + publisher={Kaggle} +} + + +
      • + +
      + +
    +
    + + + + + +
    +

    License

    +
      + +
      + + +
    • + + + + + Identifier: CC0-1.0 + + +
      + + + + + + +
      • + + +
      + +
    +
    + + + + + + + +
    +

    Graphics

    + +
    + + +
    + + + +
    + +
    + +
    + + + +
    + +
    + +
    + + + +
    + +
    + +
    + + +
    + + +
    + +
    + + +
    + + + + + + + + +
    +

    Quantitative Analysis

    + + + + + + + + + + + + + + + + + + + + + + + + +
    +
    + +
    + Accuracy +
    + The proportion of all instances that are correctly predicted. +
    +
    +
    +     +
    + + 0.58 + + + + ▼ + + + + 0.7
    minimum
    threshold     +     + + +
    + +
    + +
    + + +
    +
    +
    + + + + + +
    +
    + +
    + Precision +
    + The proportion of predicted positive instances that are correctly predicted. +
    +
    +
    +     +
    + + 0.97 + + + + ▲ + + + + 0.7
    minimum
    threshold     +     + + +
    + +
    + +
    + + +
    +
    +
    + + + + + +
    +
    + +
    + Recall +
    + The proportion of actual positive instances that are correctly predicted. Also known as recall or true positive rate. +
    +
    +
    +     +
    + + 0.98 + + + + ▲ + + + + 0.7
    minimum
    threshold     +     + + +
    + +
    + +
    + + +
    +
    +
    + + + + + +
    +
    + +
    + F1 Score +
    + The harmonic mean of precision and recall. +
    +
    +
    +     +
    + + 0.8 + + + + ▲ + + + + 0.7
    minimum
    threshold     +     + + +
    + +
    + +
    + + +
    +
    +
    + + + + + +
    +
    + +
    + AUROC +
    + The area under the receiver operating characteristic curve (AUROC) is a measure of the performance of a binary classification model. +
    +
    +
    +     +
    + + 0.81 + + + + ▲ + + + + 0.7
    minimum
    threshold     +     + + +
    + +
    + +
    + + +
    +
    +
    + + + + +
    + + + + + + + + + + + + + + +
    +

    Fairness Analysis

    + + + + + + + + + +
    +

    Graphics

    +
      + +
      + + +
      + + + +
      + +
      + +
      + + +
      + + +
      + +
    +
    + + + + +
    + + + + + + + + +
    +

    Model Details

    + + + + + +
    +

    Description

    + The model was trained on the Kaggle Heart Failure Prediction Dataset to predict risk of heart failure. +
    + + + + + +
    +

    Version

    +
      + +
      + + +
    • + + + + + + + Date: 2023-11-07 + + +
      + + + + + Initial Release + +
      + + + + + + Version: 0.0.1 + + +
      + + +
      • + + +
      + +
    +
    + + + + + +
    +

    Owners

    + +
      + +
      + + +
    • + + + + + Name: CyclOps Team + + +
      + + + + + + Contact: vectorinstitute.github.io/cyclops/ + + +
      + + + + + + + + Email: cyclops@vectorinstitute.ai + + +
      + + +
      • + + +
      + +
    + +
    + + + + + +
    +

    Licenses

    + +
      + +
      + + +
    • + + + + + Identifier: Apache-2.0 + + +
      + + + + + + +
      • + + +
      + +
    + +
    + + + + + + + + + + +
    +

    References

    + + + +
    + + + + + + + + + + + + + + + +
    +

    Name

    + Heart Failure Prediction Model +
    + + + + + +
    + + + + + + + + +
    +

    Model Parameters

    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    +

    Learning_rate

    + adaptive +
    + + + + + +
    +

    Early_stopping

    + True +
    + + + + + + + + + + +
    +

    Verbose

    + 0 +
    + + + + + +
    +

    Random_state

    + 123 +
    + + + + + +
    +

    Shuffle

    + True +
    + + + + + +
    +

    Epsilon

    + 0.1 +
    + + + + + +
    +

    Validation_fraction

    + 0.1 +
    + + + + + +
    +

    Class_weight

    + balanced +
    + + + + + +
    +

    Power_t

    + 0.5 +
    + + + + + +
    +

    Tol

    + 0.001 +
    + + + + + +
    +

    Average

    + False +
    + + + + + +
    +

    Penalty

    + l2 +
    + + + + + +
    +

    N_iter_no_change

    + 5 +
    + + + + + +
    +

    Warm_start

    + False +
    + + + + + +
    +

    Loss

    + log_loss +
    + + + + + +
    +

    Alpha

    + 0.001 +
    + + + + + +
    +

    L1_ratio

    + 0.15 +
    + + + + + +
    +

    Eta0

    + 0.01 +
    + + + + + +
    +

    Fit_intercept

    + True +
    + + + + + +
    +

    Max_iter

    + 1000 +
    + + + + + +
    + + + + + + + + +
    +

    Considerations

    + + + + + +
    +

    Users

    + +
      + +
      + +
    • + + + Hospitals + + +
      • + +
      + +
    + +
      + +
      + +
    • + + + Clinicians + + +
      • + +
      + +
    + +
      + +
      + +
    • + + + ML Engineers + + +
      • + +
      + +
    + +
    + + + + + +
    +

    Use Cases

    + +
      + +
      + + +
    • + + + + Predicting risk of heart failure. + +
      + + + + + + Kind: primary + + +
      + + +
      • + + +
      + +
    + +
      + +
      + + +
    • + + + + Predicting risk of pathologies and conditions other than heart failure. + +
      + + + + + + Kind: out-of-scope + + +
      + + +
      • + + +
      + +
    + +
    + + + + + +
    +

    Fairness Assessment

    + +
      + +
      + + +
    • + + + + + Affected Group: sex, age + + +
      + + + + + + Benefits: Improved health outcomes for patients. + + +
      + + + + + + Harms: Biased predictions for patients in certain groups (e.g. older patients) may lead to worse health outcomes. + + +
      + + + + + + We will monitor the performance of the model on these groups and retrain the model if the performance drops below a certain threshold. + + +
      + + +
      • + + +
      + +
    + +
    + + + + + +
    +

    Ethical Considerations

    + +
      + +
      + + +
    • + + + + + + + The model should be continuously monitored for performance and retrained if the performance drops below a certain threshold. + + +
      + + + + + + Risk: The model may be used to make decisions that affect the health of patients. + + +
      + + +
      • + + +
      + +
    + +
    + + + + + +
    + + + + +
    + + + \ No newline at end of file diff --git a/api/tutorials/nihcxr/cxr_classification.html b/api/tutorials/nihcxr/cxr_classification.html index 38daf73f9..4f362ca73 100644 --- a/api/tutorials/nihcxr/cxr_classification.html +++ b/api/tutorials/nihcxr/cxr_classification.html @@ -504,73 +504,73 @@

    Generate Historical Reports 
     Flattening the indices: 100%|████████| 1000/1000 [00:59<00:00, 16.68 examples/s]
-Flattening the indices: 100%|████| 1000/1000 [00:00<00:00, 574719.65 examples/s]
-Filter: 100%|████████████████████| 1000/1000 [00:00<00:00, 232848.72 examples/s]
-Map: 100%|███████████████████████████| 400/400 [00:00<00:00, 1883.80 examples/s]
-Filter -> Patient Gender:M: 100%|███| 400/400 [00:00<00:00, 43209.07 examples/s]
-Filter -> Patient Gender:F: 100%|███| 400/400 [00:00<00:00, 42582.85 examples/s]
-Filter -> overall: 100%|████████████| 400/400 [00:00<00:00, 43274.82 examples/s]
-Filter -> Patient Age:[19 - 35]: 100%|█| 400/400 [00:00<00:00, 44368.91 examples
-Filter -> Patient Age:[35 - 65]: 100%|█| 400/400 [00:00<00:00, 45186.29 examples
-Filter -> Patient Age:[65 - 100]: 100%|█| 400/400 [00:00<00:00, 43544.38 example
+Flattening the indices: 100%|████| 1000/1000 [00:00<00:00, 471641.07 examples/s]
+Filter: 100%|████████████████████| 1000/1000 [00:00<00:00, 196123.82 examples/s]
+Map: 100%|███████████████████████████| 400/400 [00:00<00:00, 1553.25 examples/s]
+Filter -> Patient Gender:M: 100%|███| 400/400 [00:00<00:00, 34617.18 examples/s]
+Filter -> Patient Gender:F: 100%|███| 400/400 [00:00<00:00, 36283.69 examples/s]
+Filter -> overall: 100%|████████████| 400/400 [00:00<00:00, 36704.40 examples/s]
+Filter -> Patient Age:[19 - 35]: 100%|█| 400/400 [00:00<00:00, 42325.02 examples
+Filter -> Patient Age:[35 - 65]: 100%|█| 400/400 [00:00<00:00, 42406.33 examples
+Filter -> Patient Age:[65 - 100]: 100%|█| 400/400 [00:00<00:00, 41329.30 example
 Filter -> Patient Age:[19 - 35]&Patient Gender:M: 100%|█| 400/400 [00:00<00:00,
 Filter -> Patient Age:[19 - 35]&Patient Gender:F: 100%|█| 400/400 [00:00<00:00,
 Filter -> Patient Age:[35 - 65]&Patient Gender:M: 100%|█| 400/400 [00:00<00:00,
 Filter -> Patient Age:[35 - 65]&Patient Gender:F: 100%|█| 400/400 [00:00<00:00,
 Filter -> Patient Age:[65 - 100]&Patient Gender:M: 100%|█| 400/400 [00:00<00:00,
 Filter -> Patient Age:[65 - 100]&Patient Gender:F: 100%|█| 400/400 [00:00<00:00,
-Filter -> overall: 100%|████████████| 400/400 [00:00<00:00, 47416.04 examples/s]
-Flattening the indices: 100%|████████| 1000/1000 [00:58<00:00, 17.06 examples/s]
-Flattening the indices: 100%|████| 1000/1000 [00:00<00:00, 556126.23 examples/s]
-Filter: 100%|████████████████████| 1000/1000 [00:00<00:00, 226266.60 examples/s]
-Map: 100%|███████████████████████████| 396/396 [00:00<00:00, 1785.00 examples/s]
-Filter -> Patient Gender:M: 100%|███| 396/396 [00:00<00:00, 41782.66 examples/s]
-Filter -> Patient Gender:F: 100%|███| 396/396 [00:00<00:00, 41657.96 examples/s]
-Filter -> overall: 100%|████████████| 396/396 [00:00<00:00, 42470.71 examples/s]
-Filter -> Patient Age:[19 - 35]: 100%|█| 396/396 [00:00<00:00, 43107.82 examples
-Filter -> Patient Age:[35 - 65]: 100%|█| 396/396 [00:00<00:00, 44339.15 examples
-Filter -> Patient Age:[65 - 100]: 100%|█| 396/396 [00:00<00:00, 42220.24 example
+Filter -> overall: 100%|████████████| 400/400 [00:00<00:00, 45408.87 examples/s]
+Flattening the indices: 100%|████████| 1000/1000 [00:58<00:00, 16.97 examples/s]
+Flattening the indices: 100%|████| 1000/1000 [00:00<00:00, 397639.74 examples/s]
+Filter: 100%|████████████████████| 1000/1000 [00:00<00:00, 166328.43 examples/s]
+Map: 100%|███████████████████████████| 396/396 [00:00<00:00, 1829.29 examples/s]
+Filter -> Patient Gender:M: 100%|███| 396/396 [00:00<00:00, 42569.76 examples/s]
+Filter -> Patient Gender:F: 100%|███| 396/396 [00:00<00:00, 41911.29 examples/s]
+Filter -> overall: 100%|████████████| 396/396 [00:00<00:00, 41668.41 examples/s]
+Filter -> Patient Age:[19 - 35]: 100%|█| 396/396 [00:00<00:00, 40855.62 examples
+Filter -> Patient Age:[35 - 65]: 100%|█| 396/396 [00:00<00:00, 37720.45 examples
+Filter -> Patient Age:[65 - 100]: 100%|█| 396/396 [00:00<00:00, 41795.28 example
 Filter -> Patient Age:[19 - 35]&Patient Gender:M: 100%|█| 396/396 [00:00<00:00,
 Filter -> Patient Age:[19 - 35]&Patient Gender:F: 100%|█| 396/396 [00:00<00:00,
 Filter -> Patient Age:[35 - 65]&Patient Gender:M: 100%|█| 396/396 [00:00<00:00,
 Filter -> Patient Age:[35 - 65]&Patient Gender:F: 100%|█| 396/396 [00:00<00:00,
 Filter -> Patient Age:[65 - 100]&Patient Gender:M: 100%|█| 396/396 [00:00<00:00,
 Filter -> Patient Age:[65 - 100]&Patient Gender:F: 100%|█| 396/396 [00:00<00:00,
-Filter -> overall: 100%|████████████| 396/396 [00:00<00:00, 43116.77 examples/s]
-Flattening the indices: 100%|████████| 1000/1000 [01:01<00:00, 16.21 examples/s]
-Flattening the indices: 100%|████| 1000/1000 [00:00<00:00, 570808.93 examples/s]
-Filter: 100%|████████████████████| 1000/1000 [00:00<00:00, 235595.35 examples/s]
-Map: 100%|███████████████████████████| 383/383 [00:00<00:00, 1812.05 examples/s]
-Filter -> Patient Gender:M: 100%|███| 383/383 [00:00<00:00, 42574.43 examples/s]
-Filter -> Patient Gender:F: 100%|███| 383/383 [00:00<00:00, 41979.21 examples/s]
-Filter -> overall: 100%|████████████| 383/383 [00:00<00:00, 44208.89 examples/s]
-Filter -> Patient Age:[19 - 35]: 100%|█| 383/383 [00:00<00:00, 43326.55 examples
-Filter -> Patient Age:[35 - 65]: 100%|█| 383/383 [00:00<00:00, 43806.23 examples
-Filter -> Patient Age:[65 - 100]: 100%|█| 383/383 [00:00<00:00, 42846.97 example
+Filter -> overall: 100%|████████████| 396/396 [00:00<00:00, 29059.84 examples/s]
+Flattening the indices: 100%|████████| 1000/1000 [01:05<00:00, 15.36 examples/s]
+Flattening the indices: 100%|████| 1000/1000 [00:00<00:00, 557382.59 examples/s]
+Filter: 100%|████████████████████| 1000/1000 [00:00<00:00, 234031.02 examples/s]
+Map: 100%|███████████████████████████| 383/383 [00:00<00:00, 1699.90 examples/s]
+Filter -> Patient Gender:M: 100%|███| 383/383 [00:00<00:00, 41947.42 examples/s]
+Filter -> Patient Gender:F: 100%|███| 383/383 [00:00<00:00, 32378.98 examples/s]
+Filter -> overall: 100%|████████████| 383/383 [00:00<00:00, 43611.20 examples/s]
+Filter -> Patient Age:[19 - 35]: 100%|█| 383/383 [00:00<00:00, 38869.04 examples
+Filter -> Patient Age:[35 - 65]: 100%|█| 383/383 [00:00<00:00, 43790.71 examples
+Filter -> Patient Age:[65 - 100]: 100%|█| 383/383 [00:00<00:00, 39049.50 example
 Filter -> Patient Age:[19 - 35]&Patient Gender:M: 100%|█| 383/383 [00:00<00:00,
 Filter -> Patient Age:[19 - 35]&Patient Gender:F: 100%|█| 383/383 [00:00<00:00,
 Filter -> Patient Age:[35 - 65]&Patient Gender:M: 100%|█| 383/383 [00:00<00:00,
 Filter -> Patient Age:[35 - 65]&Patient Gender:F: 100%|█| 383/383 [00:00<00:00,
 Filter -> Patient Age:[65 - 100]&Patient Gender:M: 100%|█| 383/383 [00:00<00:00,
 Filter -> Patient Age:[65 - 100]&Patient Gender:F: 100%|█| 383/383 [00:00<00:00,
-Filter -> overall: 100%|████████████| 383/383 [00:00<00:00, 44972.52 examples/s]
-Flattening the indices: 100%|████████| 1000/1000 [01:00<00:00, 16.56 examples/s]
-Flattening the indices: 100%|████| 1000/1000 [00:00<00:00, 550144.81 examples/s]
-Filter: 100%|████████████████████| 1000/1000 [00:00<00:00, 229447.70 examples/s]
-Map: 100%|███████████████████████████| 411/411 [00:00<00:00, 1845.84 examples/s]
-Filter -> Patient Gender:M: 100%|███| 411/411 [00:00<00:00, 42709.95 examples/s]
-Filter -> Patient Gender:F: 100%|███| 411/411 [00:00<00:00, 41488.78 examples/s]
-Filter -> overall: 100%|████████████| 411/411 [00:00<00:00, 42321.98 examples/s]
-Filter -> Patient Age:[19 - 35]: 100%|█| 411/411 [00:00<00:00, 43213.15 examples
-Filter -> Patient Age:[35 - 65]: 100%|█| 411/411 [00:00<00:00, 42614.92 examples
-Filter -> Patient Age:[65 - 100]: 100%|█| 411/411 [00:00<00:00, 43044.82 example
+Filter -> overall: 100%|████████████| 383/383 [00:00<00:00, 41690.50 examples/s]
+Flattening the indices: 100%|████████| 1000/1000 [00:59<00:00, 16.92 examples/s]
+Flattening the indices: 100%|████| 1000/1000 [00:00<00:00, 584734.98 examples/s]
+Filter: 100%|████████████████████| 1000/1000 [00:00<00:00, 237060.08 examples/s]
+Map: 100%|███████████████████████████| 411/411 [00:00<00:00, 1888.92 examples/s]
+Filter -> Patient Gender:M: 100%|███| 411/411 [00:00<00:00, 44822.13 examples/s]
+Filter -> Patient Gender:F: 100%|███| 411/411 [00:00<00:00, 44078.32 examples/s]
+Filter -> overall: 100%|████████████| 411/411 [00:00<00:00, 45648.21 examples/s]
+Filter -> Patient Age:[19 - 35]: 100%|█| 411/411 [00:00<00:00, 44647.99 examples
+Filter -> Patient Age:[35 - 65]: 100%|█| 411/411 [00:00<00:00, 46762.67 examples
+Filter -> Patient Age:[65 - 100]: 100%|█| 411/411 [00:00<00:00, 45729.34 example
 Filter -> Patient Age:[19 - 35]&Patient Gender:M: 100%|█| 411/411 [00:00<00:00,
 Filter -> Patient Age:[19 - 35]&Patient Gender:F: 100%|█| 411/411 [00:00<00:00,
 Filter -> Patient Age:[35 - 65]&Patient Gender:M: 100%|█| 411/411 [00:00<00:00,
 Filter -> Patient Age:[35 - 65]&Patient Gender:F: 100%|█| 411/411 [00:00<00:00,
 Filter -> Patient Age:[65 - 100]&Patient Gender:M: 100%|█| 411/411 [00:00<00:00,
 Filter -> Patient Age:[65 - 100]&Patient Gender:F: 100%|█| 411/411 [00:00<00:00,
-Filter -> overall: 100%|████████████| 411/411 [00:00<00:00, 44622.57 examples/s]
+Filter -> overall: 100%|████████████| 411/411 [00:00<00:00, 46829.99 examples/s]

    CyclOps offers a package for documentation of the model through a model card. The ModelCardReport class is used to populate and generate the model card as an HTML file. The model card has the following sections: - Model Details: This section contains descriptive metadata about the model such as the owners, version, license, etc. - Model Parameters: This section contains the technical details of the model such as the model architecture, training parameters, etc. - Considerations: This section @@ -659,8 +659,8 @@

    Load Model and get Predictions 
    -Filter: 100%|██████████| 1000/1000 [00:00<00:00, 113614.43 examples/s]
-Map: 100%|██████████| 661/661 [00:00<00:00, 3288.78 examples/s]
+Filter: 100%|██████████| 1000/1000 [00:00<00:00, 100069.28 examples/s]
+Map: 100%|██████████| 661/661 [00:00<00:00, 3260.05 examples/s]
    @@ -786,9 +786,9 @@

    Multilabel AUROC by Pathology and Sex 
    -Filter -> Patient Gender:M: 100%|██████████| 661/661 [00:00<00:00, 43129.93 examples/s]
-Filter -> Patient Gender:F: 100%|██████████| 661/661 [00:00<00:00, 33066.59 examples/s]
-Filter -> overall: 100%|██████████| 661/661 [00:00<00:00, 44105.62 examples/s]
+Filter -> Patient Gender:M: 100%|██████████| 661/661 [00:00<00:00, 45122.06 examples/s]
+Filter -> Patient Gender:F: 100%|██████████| 661/661 [00:00<00:00, 26820.50 examples/s]
+Filter -> overall: 100%|██████████| 661/661 [00:00<00:00, 47389.62 examples/s]

    @@ -849,16 +849,16 @@

    Multilabel AUROC by Pathology and Age 
    -Filter -> Patient Age:[19 - 35]: 100%|██████████| 661/661 [00:00<00:00, 47537.51 examples/s]
-Filter -> Patient Age:[35 - 65]: 100%|██████████| 661/661 [00:00<00:00, 45093.44 examples/s]
-Filter -> Patient Age:[65 - 100]: 100%|██████████| 661/661 [00:00<00:00, 45813.26 examples/s]
-Filter -> Patient Age:[19 - 35]&Patient Gender:M: 100%|██████████| 661/661 [00:00<00:00, 38930.49 examples/s]
-Filter -> Patient Age:[19 - 35]&Patient Gender:F: 100%|██████████| 661/661 [00:00<00:00, 45141.90 examples/s]
-Filter -> Patient Age:[35 - 65]&Patient Gender:M: 100%|██████████| 661/661 [00:00<00:00, 45507.20 examples/s]
-Filter -> Patient Age:[35 - 65]&Patient Gender:F: 100%|██████████| 661/661 [00:00<00:00, 44554.29 examples/s]
-Filter -> Patient Age:[65 - 100]&Patient Gender:M: 100%|██████████| 661/661 [00:00<00:00, 39816.10 examples/s]
-Filter -> Patient Age:[65 - 100]&Patient Gender:F: 100%|██████████| 661/661 [00:00<00:00, 46799.26 examples/s]
-Filter -> overall: 100%|██████████| 661/661 [00:00<00:00, 46125.76 examples/s]
+Filter -> Patient Age:[19 - 35]: 100%|██████████| 661/661 [00:00<00:00, 47628.97 examples/s]
+Filter -> Patient Age:[35 - 65]: 100%|██████████| 661/661 [00:00<00:00, 45781.48 examples/s]
+Filter -> Patient Age:[65 - 100]: 100%|██████████| 661/661 [00:00<00:00, 44662.67 examples/s]
+Filter -> Patient Age:[19 - 35]&Patient Gender:M: 100%|██████████| 661/661 [00:00<00:00, 43334.19 examples/s]
+Filter -> Patient Age:[19 - 35]&Patient Gender:F: 100%|██████████| 661/661 [00:00<00:00, 43588.32 examples/s]
+Filter -> Patient Age:[35 - 65]&Patient Gender:M: 100%|██████████| 661/661 [00:00<00:00, 44460.69 examples/s]
+Filter -> Patient Age:[35 - 65]&Patient Gender:F: 100%|██████████| 661/661 [00:00<00:00, 45663.10 examples/s]
+Filter -> Patient Age:[65 - 100]&Patient Gender:M: 100%|██████████| 661/661 [00:00<00:00, 46002.54 examples/s]
+Filter -> Patient Age:[65 - 100]&Patient Gender:F: 100%|██████████| 661/661 [00:00<00:00, 47340.26 examples/s]
+Filter -> overall: 100%|██████████| 661/661 [00:00<00:00, 48005.04 examples/s]
    @@ -915,9 +915,9 @@

    Multilabel AUROC by Pathology and Age

    -
    + + + + + +     + +
    + + + + +
    +

    How is your model doing?


    +

    A quick glance of your most important metrics.

    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    +
    + +
    + Positive Predictive Value (PPV) +
    + The proportion of correctly predicted positive instances among all instances predicted as positive. Also known as precision. +
    +
    +
    +     +
    + + 0.14 + + + + ▼ + + + + 0.7
    minimum
    threshold     +     + + +
    + +
    + +
    + + +
    +
    +
    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    +
    + +
    + Negative Predictive Value (NPV) +
    + The proportion of correctly predicted negative instances among all instances predicted as negative. +
    +
    +
    +     +
    + + 0.93 + + + + ▲ + + + + 0.7
    minimum
    threshold     +     + + +
    + +
    + +
    + + +
    +
    +
    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    +
    + +
    + Sensitivity +
    + The proportion of actual positive instances that are correctly predicted. Also known as recall or true positive rate. +
    +
    +
    +     +
    + + 0.83 + + + + ▲ + + + + 0.7
    minimum
    threshold     +     + + +
    + +
    + +
    + + +
    +
    +
    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    +
    + +
    + Specificity +
    + The proportion of actual negative instances that are correctly predicted. +
    +
    +
    +     +
    + + 0.33 + + + + ▼ + + + + 0.7
    minimum
    threshold     +     + + +
    + +
    + +
    + + +
    +
    +
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+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    +

    How is your model doing over time?


    +

    See how your model is performing over several metrics and subgroups over time.

    +
    +
    +

    Metrics

    +
    + + + +
    + +
    + The proportion of correctly predicted positive instances among all instances predicted as positive. Also known as precision. +
    +
    +
    + + + + + + +
    + +
    + The proportion of correctly predicted negative instances among all instances predicted as negative. +
    +
    +
    + + + + + +
    + +
    + The proportion of actual positive instances that are correctly predicted. Also known as recall or true positive rate. +
    +
    +
    + + + + + +
    + +
    + The proportion of actual negative instances that are correctly predicted. +
    +
    +
    + + +
    +
    + +
    +

    Patient Age

    +
    + + + + + + + + + + + + +
    +
    + +
    +

    Pathology

    +
    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    +
    + +
    +

    Patient Gender

    +
    + + + + + + + + + +
    +
    + +
    +
    +
    + + +
    +
    +
    + + + + + + + + + +
    +

    Datasets

    + + + + +
    +

    Graphics

    + +
    + + +
    + + + +
    + +
    + +
    + + + +
    + +
    + +
    + + + +
    + +
    + +
    + + +
    + + +
    + +
    + + +
    + + + + + + + + +
    +

    Quantitative Analysis

    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    +
    + +
    + Positive Predictive Value (PPV) +
    + The proportion of correctly predicted positive instances among all instances predicted as positive. Also known as precision. +
    +
    +
    +     +
    + + 0.14 + + + + ▼ + + + + 0.7
    minimum
    threshold     +     + + +
    + +
    + +
    + + +
    +
    +
    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    +
    + +
    + Negative Predictive Value (NPV) +
    + The proportion of correctly predicted negative instances among all instances predicted as negative. +
    +
    +
    +     +
    + + 0.93 + + + + ▲ + + + + 0.7
    minimum
    threshold     +     + + +
    + +
    + +
    + + +
    +
    +
    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    +
    + +
    + Sensitivity +
    + The proportion of actual positive instances that are correctly predicted. Also known as recall or true positive rate. +
    +
    +
    +     +
    + + 0.83 + + + + ▲ + + + + 0.7
    minimum
    threshold     +     + + +
    + +
    + +
    + + +
    +
    +
    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    +
    + +
    + Specificity +
    + The proportion of actual negative instances that are correctly predicted. +
    +
    +
    +     +
    + + 0.33 + + + + ▼ + + + + 0.7
    minimum
    threshold     +     + + +
    + +
    + +
    + + +
    +
    +
    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    + + + + + + + + + + + + + + + + + + + + +
    +

    Model Details

    + + + + + +
    +

    Description

    + This model is a DenseNet121 model trained on the NIH Chest X-Ray dataset, which contains 112,120 frontal-view X-ray images of 30,805 unique patients with the fourteen text-mined disease labels from the associated radiological reports. The labels are Atelectasis, Cardiomegaly, Effusion, Infiltration, Mass, Nodule, Pneumonia, Pneumothorax, Consolidation, Edema, Emphysema, Fibrosis, Pleural Thickening, and Hernia. The model was trained on 80% of the data and evaluated on the remaining 20%. +
    + + + + + + + + + + +
    +

    Owners

    + +
      + +
      + + +
    • + + + + + Name: Machine Learning and Medicine Lab + + +
      + + + + + + Contact: mlmed.org + + +
      + + + + + + + + Email: joseph@josephpcohen.com + + +
      + + +
      • + + +
      + +
    + +
    + + + + + + + + + + +
    +

    Citations

    + +
      + +
      + +
    • + + + @inproceedings{Cohen2022xrv, + title = {{TorchXRayVision: A library of chest X-ray datasets and models}}, + author = {Cohen, Joseph Paul and Viviano, Joseph D. and Bertin, Paul and Morrison,Paul and Torabian, Parsa and Guarrera, Matteo and Lungren, Matthew P and Chaudhari, Akshay and Brooks, Rupert and Hashir, Mohammad and Bertrand, Hadrien}, + booktitle = {Medical Imaging with Deep Learning}, + url = {https://github.com/mlmed/torchxrayvision}, + arxivId = {2111.00595}, + year = {2022} + } + + +
      • + +
      + +
    + +
      + +
      + +
    • + + + @inproceedings{cohen2020limits, + title={On the limits of cross-domain generalization in automated X-ray prediction}, + author={Cohen, Joseph Paul and Hashir, Mohammad and Brooks, Rupert and Bertrand, Hadrien}, + booktitle={Medical Imaging with Deep Learning}, + year={2020}, + url={https://arxiv.org/abs/2002.02497} + } + + +
      • + +
      + +
    + +
    + + + + + +
    +

    References

    + + + +
    + + + + + + + + + + + + + + + +
    +

    Name

    + NIH Chest X-Ray Multi-label Classification Model +
    + + + + + +
    + + + + + + + + + + + + + + +
    +

    Considerations

    + + + + + +
    +

    Users

    + +
      + +
      + +
    • + + + Radiologists + + +
      • + +
      + +
    + +
      + +
      + +
    • + + + Data Scientists + + +
      • + +
      + +
    + +
    + + + + + +
    +

    Use Cases

    + +
      + +
      + + +
    • + + + + The model can be used to predict the presence of 14 pathologies in chest X-ray images. + +
      + + + + + + Kind: primary + + +
      + + +
      • + + +
      + +
    + +
    + + + + + +
    +

    Fairness Assessment

    + +
      + +
      + + +
    • + + + + + Affected Group: Patients with rare pathologies + + +
      + + + + + + Benefits: The model can help radiologists to detect pathologies in chest X-ray images. + + +
      + + + + + + Harms: The model may not generalize well to populations that are not well-represented in the training data. + + +
      + + + + + + A mitigation strategy for this risk is to ensure that the training data is diverse and representative of the population. + + +
      + + +
      • + + +
      + +
    + +
    + + + + + +
    +

    Ethical Considerations

    + +
      + +
      + + +
    • + + + + + + + A mitigation strategy for this risk is to ensure that the training data is diverse and representative of the population that the model will be used on. Additionally, the model should be regularly evaluated and updated to ensure that it continues to perform well on diverse populations. Finally, the model should be used in conjunction with human expertise to ensure that any biases or limitations are identified and addressed. + + +
      + + + + + + Risk: One ethical risk of the model is that it may not generalize well to populations that are not well-represented in the training data, such as patients from different geographic regions or with different demographics. + + +
      + + +
      • + + +
      + +
    + +
    + + + + + +
    +

    Limitations

    + +
      + +
      + + +
    • + + + + The limitations of this model include its inability to detect pathologies that are not included in the 14 labels of the NIH Chest X-Ray dataset. Additionally, the model may not perform well on images that are of poor quality or that contain artifacts. Finally, the model may not generalize well to populations that are not well-represented in the training data, such as patients from different geographic regions or with different demographics. + +
      + + +
      • + + +
      + +
    + +
    + + + + + +
    +

    Tradeoffs

    + +
      + +
      + + +
    • + + + + The model can help radiologists to detect pathologies in chest X-ray images, but it may not generalize well to populations that are not well-represented in the training data. + +
      + + +
      • + + +
      + +
    + +
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    + + + + +
    + + + \ No newline at end of file diff --git a/api/tutorials/synthea/length_of_stay_report_periodic.html b/api/tutorials/synthea/length_of_stay_report_periodic.html new file mode 100644 index 000000000..c0c2ee900 --- /dev/null +++ b/api/tutorials/synthea/length_of_stay_report_periodic.html @@ -0,0 +1,2254 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    + + + + +
    +

    How is your model doing?


    +

    A quick glance of your most important metrics.

    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    +
    + +
    + Accuracy +
    + The proportion of all instances that are correctly predicted. +
    +
    +
    +     +
    + + 0.76 + + + + ▲ + + + + 0.7
    minimum
    threshold     +     + + +
    + +
    + +
    + + +
    +
    +
    + + + + + +
    +
    + +
    + Precision +
    + The proportion of predicted positive instances that are correctly predicted. +
    +
    +
    +     +
    + + 0.94 + + + + ▲ + + + + 0.7
    minimum
    threshold     +     + + +
    + +
    + +
    + + +
    +
    +
    + + + + + +
    +
    + +
    + Recall +
    + The proportion of actual positive instances that are correctly predicted. Also known as recall or true positive rate. +
    +
    +
    +     +
    + + 0.82 + + + + ▲ + + + + 0.7
    minimum
    threshold     +     + + +
    + +
    + +
    + + +
    +
    +
    + + + + + +
    +
    + +
    + F1 Score +
    + The harmonic mean of precision and recall. +
    +
    +
    +     +
    + + 1.0 + + + + ▲ + + + + 0.7
    minimum
    threshold     +     + + +
    + +
    + +
    + + +
    +
    +
    + + + + + +
    +
    + +
    + AUROC +
    + The area under the receiver operating characteristic curve (AUROC) is a measure of the performance of a binary classification model. +
    +
    +
    +     +
    + + 0.97 + + + + ▲ + + + + 0.7
    minimum
    threshold     +     + + +
    + +
    + +
    + + +
    +
    +
    + + + +
    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    +

    How is your model doing over time?


    +

    See how your model is performing over several metrics and subgroups over time.

    +
    +
    +

    Metrics

    +
    + + +
    + +
    + The proportion of all instances that are correctly predicted. +
    +
    +
    + + + + + +
    + +
    + The proportion of predicted positive instances that are correctly predicted. +
    +
    +
    + + + + + +
    + +
    + The proportion of actual positive instances that are correctly predicted. Also known as recall or true positive rate. +
    +
    +
    + + + + + +
    + +
    + The harmonic mean of precision and recall. +
    +
    +
    + + + + + +
    + +
    + The area under the receiver operating characteristic curve (AUROC) is a measure of the performance of a binary classification model. +
    +
    +
    + + +
    +
    + +
    +

    Age

    +
    + + + + + + + + + +
    +
    + +
    +

    Gender

    +
    + + + + + + + + + +
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    + +
    +
    +
    + + +
    +
    +
    + + + + + + + + + +
    +

    Datasets

    + + + + +
    +

    Graphics

    + +
    + + +
    + + + +
    + +
    + +
    + + + +
    + +
    + +
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    + + + +
    + +
    + +
    + + +
    + + +
    + +
    + + +
    + + + + + + + + +
    +

    Quantitative Analysis

    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    +
    + +
    + Accuracy +
    + The proportion of all instances that are correctly predicted. +
    +
    +
    +     +
    + + 0.76 + + + + ▲ + + + + 0.7
    minimum
    threshold     +     + + +
    + +
    + +
    + + +
    +
    +
    + + + + + +
    +
    + +
    + Precision +
    + The proportion of predicted positive instances that are correctly predicted. +
    +
    +
    +     +
    + + 0.94 + + + + ▲ + + + + 0.7
    minimum
    threshold     +     + + +
    + +
    + +
    + + +
    +
    +
    + + + + + +
    +
    + +
    + Recall +
    + The proportion of actual positive instances that are correctly predicted. Also known as recall or true positive rate. +
    +
    +
    +     +
    + + 0.82 + + + + ▲ + + + + 0.7
    minimum
    threshold     +     + + +
    + +
    + +
    + + +
    +
    +
    + + + + + +
    +
    + +
    + F1 Score +
    + The harmonic mean of precision and recall. +
    +
    +
    +     +
    + + 1.0 + + + + ▲ + + + + 0.7
    minimum
    threshold     +     + + +
    + +
    + +
    + + +
    +
    +
    + + + + + +
    +
    + +
    + AUROC +
    + The area under the receiver operating characteristic curve (AUROC) is a measure of the performance of a binary classification model. +
    +
    +
    +     +
    + + 0.97 + + + + ▲ + + + + 0.7
    minimum
    threshold     +     + + +
    + +
    + +
    + + +
    +
    +
    + + + + +
    + + + + + + + + + + + + + + +
    +

    Fairness Analysis

    + + + + + + + + + +
    +

    Graphics

    +
      + +
      + + +
      + + + +
      + +
      + +
      + + +
      + + +
      + +
    +
    + + + + +
    + + + + + + + + +
    +

    Model Details

    + + + + + +
    +

    Description

    + The model was trained on the Synthea synthetic dataset to predict prolonged stay in the hospital. +
    + + + + + +
    +

    Version

    +
      + +
      + + +
    • + + + + + + + Date: 2023-11-07 + + +
      + + + + + Initial Release + +
      + + + + + + Version: 0.0.1 + + +
      + + +
      • + + +
      + +
    +
    + + + + + +
    +

    Owners

    + +
      + +
      + + +
    • + + + + + Name: CyclOps Team + + +
      + + + + + + Contact: vectorinstitute.github.io/cyclops/ + + +
      + + + + + + + + Email: cyclops@vectorinstitute.ai + + +
      + + +
      • + + +
      + +
    + +
    + + + + + +
    +

    Licenses

    + +
      + +
      + + +
    • + + + + + Identifier: Apache-2.0 + + +
      + + + + + + +
      • + + +
      + +
    + +
    + + + + + + + + + + +
    +

    References

    + + + +
    + + + + + + + + + + + + + + + +
    +

    Name

    + Prolonged Length of Stay Prediction Model +
    + + + + + +
    + + + + + + + + +
    +

    Model Parameters

    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    +

    N_estimators

    + 500 +
    + + + + + + + + + + + + + + + +
    +

    Seed

    + 123 +
    + + + + + + + + + + + + + + + +
    +

    Random_state

    + 123 +
    + + + + + + + + + + + + + + + +
    +

    Min_child_weight

    + 3 +
    + + + + + + + + + + +
    +

    Reg_lambda

    + 0 +
    + + + + + +
    +

    Missing

    + nan +
    + + + + + + + + + + +
    +

    Gamma

    + 2 +
    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    +

    Learning_rate

    + 0.1 +
    + + + + + +
    +

    Max_depth

    + 2 +
    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    +

    Enable_categorical

    + False +
    + + + + + + + + + + +
    +

    Objective

    + binary:logistic +
    + + + + + + + + + + +
    +

    Colsample_bytree

    + 1 +
    + + + + + +
    +

    Eval_metric

    + logloss +
    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    + + + + + + + + +
    +

    Considerations

    + + + + + +
    +

    Users

    + +
      + +
      + +
    • + + + Hospitals + + +
      • + +
      + +
    + +
      + +
      + +
    • + + + Clinicians + + +
      • + +
      + +
    + +
      + +
      + +
    • + + + ML Engineers + + +
      • + +
      + +
    + +
    + + + + + +
    +

    Use Cases

    + +
      + +
      + + +
    • + + + + Predicting prolonged length of stay + +
      + + + + + + Kind: primary + + +
      + + +
      • + + +
      + +
    + +
    + + + + + +
    +

    Fairness Assessment

    + +
      + +
      + + +
    • + + + + + Affected Group: sex, age + + +
      + + + + + + Benefits: Improved health outcomes for patients. + + +
      + + + + + + Harms: Biased predictions for patients in certain groups (e.g. older patients) may lead to worse health outcomes. + + +
      + + + + + + We will monitor the performance of the model on these groups and retrain the model if the performance drops below a certain threshold. + + +
      + + +
      • + + +
      + +
    + +
    + + + + + +
    +

    Ethical Considerations

    + +
      + +
      + + +
    • + + + + + + + The model should be continuously monitored for performance and retrained if the performance drops below a certain threshold. + + +
      + + + + + + Risk: The model may be used to make decisions that affect the health of patients. + + +
      + + +
      • + + +
      + +
    + +
    + + + + + +
    + + + + +
    + + + \ No newline at end of file diff --git a/api/tutorials/synthea/los_prediction.html b/api/tutorials/synthea/los_prediction.html index 2d5042474..399a77eb8 100644 --- a/api/tutorials/synthea/los_prediction.html +++ b/api/tutorials/synthea/los_prediction.html @@ -673,17 +673,17 @@

    Compute length of stay (labels) 
    -2023-11-07 13:08:13,639 INFO cycquery.orm    - Database setup, ready to run queries!
-2023-11-07 13:08:17,733 INFO cycquery.orm    - Query returned successfully!
-2023-11-07 13:08:17,733 INFO cycquery.utils.profile - Finished executing function run_query in 3.570902 s
-2023-11-07 13:08:19,542 INFO cycquery.orm    - Query returned successfully!
-2023-11-07 13:08:19,544 INFO cycquery.utils.profile - Finished executing function run_query in 1.809629 s
-2023-11-07 13:08:20,584 INFO cycquery.orm    - Query returned successfully!
-2023-11-07 13:08:20,585 INFO cycquery.utils.profile - Finished executing function run_query in 0.167578 s
-2023-11-07 13:08:20,815 INFO cycquery.orm    - Query returned successfully!
-2023-11-07 13:08:20,816 INFO cycquery.utils.profile - Finished executing function run_query in 0.225515 s
-2023-11-07 13:08:20,900 INFO cycquery.orm    - Query returned successfully!
-2023-11-07 13:08:20,901 INFO cycquery.utils.profile - Finished executing function run_query in 0.085219 s
+2023-11-07 17:17:40,963 INFO cycquery.orm    - Database setup, ready to run queries!
+2023-11-07 17:17:45,825 INFO cycquery.orm    - Query returned successfully!
+2023-11-07 17:17:45,826 INFO cycquery.utils.profile - Finished executing function run_query in 3.615795 s
+2023-11-07 17:17:47,759 INFO cycquery.orm    - Query returned successfully!
+2023-11-07 17:17:47,761 INFO cycquery.utils.profile - Finished executing function run_query in 1.933158 s
+2023-11-07 17:17:48,954 INFO cycquery.orm    - Query returned successfully!
+2023-11-07 17:17:48,955 INFO cycquery.utils.profile - Finished executing function run_query in 0.332908 s
+2023-11-07 17:17:49,379 INFO cycquery.orm    - Query returned successfully!
+2023-11-07 17:17:49,381 INFO cycquery.utils.profile - Finished executing function run_query in 0.421673 s
+2023-11-07 17:17:49,473 INFO cycquery.orm    - Query returned successfully!
+2023-11-07 17:17:49,474 INFO cycquery.utils.profile - Finished executing function run_query in 0.092155 s

    @@ -770,9 +770,9 @@

    Drop NaNs based on the
    -