diff --git a/README.md b/README.md index 2533695..eae1f2a 100644 --- a/README.md +++ b/README.md @@ -17,11 +17,11 @@ For cases, Identify a fever episode temp >= 38, look up to 6 hours back, extract ## Introduction ### Background -Fever can provide valuable information for diagnosis and prognosis of various diseases such as pneumonia, dengue, sepsis, etc., therefore, predicting fever early can help in the effectiveness of treatment options and expediting the treatment process. The aim of this project is to develop novel algorithms that can accurately predict fever onset in critically ill patients by applying machine learning technique on continuous physiological data. We have maded a model which can predict the occurence of fever, hours before it actaully occurs. This will provide doctors to take contingency actions early, and will decrease mortality rates significantly. +Fever can provide valuable information for diagnosis and prognosis of various diseases such as pneumonia, dengue, sepsis, etc., therefore, predicting fever early can help in the effectiveness of treatment options and expediting the treatment process. The aim of this project is to develop novel algorithms that can accurately predict fever onset in critically ill patients by applying machine learning technique on continuous physiological data. We have made a model which can predict the occurence of fever, hours before it actually occurs. This will provide doctors to take contingency actions early, and will decrease mortality rates significantly. ### Dataset -We hace used vitialPeriodic dataset which is provided by the eICU Collaborative Research Database. It contains continuous physiological data collected every 5-minute from a cohort of over200,000 critically ill patients admitted to an Intensive Care Unit (ICU) over a 2-year period. -

Physiological Variabels

+We have used vitialPeriodic dataset which is provided by the eICU Collaborative Research Database. It contains continuous physiological data collected every 5-minute from a cohort of over 200,000 critically ill patients admitted to an Intensive Care Unit (ICU) over a 2-year period. +

Physiological Variables

  1. Temperature : Patient’s temperature value in celsius
  2. saO2 : Patient’s saO2 value e.g.: 99, 94, 98
    3. @@ -38,7 +38,7 @@ We hace used vitialPeriodic dataset which is provided by the eICU Collaborative ### Feature Extraction For the feature extraction process, we need to introduce the concept of time windows and time before true onset. Preprocessing is done is such a way that the time window, i.e the amount of data in a time period required to train the model is kept constant at 10 hours. So, we always train the model using 10hrs worth of data. Time before true onset means how early do we want to predict sepsis. This parameter has been varied in steps of 2 hours to get a better understanding of how your accuracy drops off as the time difference increases. For this experiment, we have used time priors of 2, 4, 6 and 8 hours. Even the time window has sub window of 0-2 hours, 0-4 hours, 0-6 hours, 0-8 hours and 0-10 hours, the sub windows were created so that our model could get temporal idea also.
    -Then we have preprocessed the entire dataframe according to each of these time differences. So we have processed data for 2 hours before sepsis with 6 hours of training data, 4 hours before with 6 hours of training data and so on so forth. We have seven physiological variables data streams for 5 diffenet sub window. We then extracted 7 statistical features from each of the original 7*5 data streams.
    +Then we have preprocessed the entire dataframe according to each of these time differences. So we have processed data for 2 hours before sepsis with 6 hours of training data, 4 hours before with 6 hours of training data and so on so forth. We have seven physiological variables data streams for 5 different sub window. We then extracted 7 statistical features from each of the original 7*5 data streams.
    They are: