corrections

Step_03_Real-world_Prediction_of_Patient_New_Beta_Blocker_Use.ipynb

@@ -5,26 +5,7 @@
    "id": "4e9cfb78-408c-40dd-9d0f-edb7291dd04e",
    "metadata": {},
    "source": [
-    "# STEP # 3 - REAL-WORLD PREDICTION OF NEW BETA BLOCKER USE\n",
-    "\n",
-    "## As we will see this notebook, this tool benefits the following entities : - \n",
-    "## (1) Provider-end (hospitals, doctors, physicians, nurses, clinicians),\n",
-    "## (2) Payer-end (risk managers, clinical managers, actuary, medical economics, finance) \n",
-    "\n",
-    "## The prediction gives a quick sense of severity of a patient (member). This is valuable in following scenarios :-\n",
-    "## (1) Effective and Efficient Member Intervention,\n",
-    "## (2) Budgeting, \n",
-    "## (3) Accurate Risk Assessment\n",
-    "## (4) Reduce Costs - Efficiently and targeted member intervention reduces costs,\n",
-    "## (5) Reduce financial burden of most expensive procedures,\n",
-    "## (6) Essentially reduce ROI...\n",
-    "\n",
-    "## 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. \n",
-    "\n",
-    "## 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 - \n",
-    "## ( By the way, we have also built a tool for PDF medical charts which reads and analyzes them and outputs an index of diagnoses found within it. This tool not only reduced burden and time of reading medical charts, but has also reduced costs in millions of dollars. ) \n",
-    "\n",
-    "## After analyzing medical charts, 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 with more ROI.  "
+    "# STEP # 3 - REAL-WORLD PREDICTION OF NEW BETA BLOCKER USE"
    ]
   },
   {

readme.md

@@ -14,15 +14,23 @@ The solution presented here, shows how a new encounter of BETA BLOCKER use by a
 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,
+
+(4) Reduce Costs - Efficient 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 
@@ -35,13 +43,17 @@ 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