readme

readme.md

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+# NEURAL NETWORK PREDICTION OF PATIENT JOURNEY - patient diagnoses, procedures, services and Rx
+
+***
+
+## Description :-
+
+When patients have medical encounters at the doctor's office or hospitals or pharmacies, the major encounters are diagnoses, 
+procedures, services and Rx. These encounters lie over the entire gamut of cost - from cheap to expensive. So, it is beneficial
+to get a sense of what encounters a patient may likely experience in future - so that it can possibly be dealt with in advance - 
+either by accounting in the budget or by member-intervention or accounting for the risk. Knowing versus not knowing can be a 
+huge difference in ROI. 
+
+The solution presented here, shows how a new encounter of BETA BLOCKER use by a patient can be predicted - Altough the solution 
+can be used to predict any one of 160 different encounters including diagnoses, procedures, service and Rx.
+
+As you will see, this tool benefits the following entities : -
+(1) Provider-end (hospitals, doctors, physicians, nurses, clinicians),
+(2) Payer-end (risk managers, clinical managers, actuary, medical economics, finance). 
+
+The prediction gives a quick sense of severity of a patient (member). This is valuable in following scenarios :-
+(1) Effective and Efficient Member Intervention,
+(2) Budgeting,
+(3) Accurate Risk Assessment
+(4) Reduce Costs - Efficiently and targeted member intervention reduces costs,
+(5) Reduce financial burden of most expensive procedures,
+(6) Substantially grow ROI.
+
+In real world, this would not be the only tool at disposal. An well-informed decision would have to made by weighing predictions 
+of this model with other tools at disposal. For ex: Let's say a clinical team at the payer-end already has been using medical charts
+for selecting patients to contact for member intervention.
+After analyzing medical charts (also see https://github.com/nsb700/pdf-medical-charts-reader-demo), if the clinical team is able to find say 
+100 members, this tool can further help to focus on members of interest based on high cost items. It can reduce the member set from 100
+to way fewer members resulting in more ROI.
+
+Coding is done using Python and Keras. Intermediate outputs are printed in the notebook for clarity.
+
+Files are arranged as below - 
+(1) Data preparation - Step_01_Data_Preparation_(for_Prediction_of_Patient_New_Beta_Blocker_Use).ipynb
+    Output of Data Preparation - step_01_output.zip
+
+(2) Prediction with Neural Network - Step_02_Neural_Network_(for_Prediction_of_Patient_New_Beta_Blocker_Use).ipynb
+    Output - printed within the notebook.
+
+(3) Prediction with Naive Bayes - Step_02_NaiveBayesClassifier_(for_Prediction_of_Patient_New_Beta_Blocker_Use).ipynb
+    Output - printed within the notebook and model saved in directory - step_02_output_keras_model/
+
+(4) Real-world prediction - Step_03_Real-world_Prediction_of_Patient_New_Beta_Blocker_Use.ipynb
+
+***
+