Business Intelligence & Analytics solutions enable healthcare service providers to build sustainable competitive advantage with the help of insights derived from their existing operations and patient data.

1. INNOVATE INTEGRATE TRANSFORM
March 31, 2016
PREDICITIVE ANALYTICS IN
HEALTHCARE

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 HIMSS describes healthcare analytics as the “systematic use of data and
related clinical and business (C&B) insights developed through applied
analytical disciplines such as statistical, contextual, quantitative, predictive,
and cognitive spectrums to drive fact-based decision making for planning,
management, measurement and learning
 Objectives
o Healthcare providers are improving the clinical outcomes of patients
via treatments and protocols
o Promotion of wellness and disease management
Overview
 The key objective of healthcare analytics is to gain insight for making
informed healthcare decisions.

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Patient Re-admission within 30 days – A case study
Business Problem
A healthcare facility wants to identify a patient's chance of
getting re-admitted upon discharge within 30 days.
Benefits
Clinicians can be prepared to provide better post-discharge
care for patients who are likely to get re-admitted and
hospitals can avail benefits from Government.

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Patient admission
If a patient is hospitalized for more than 24 hrs, it is considered as Patient
admission.
Re-Admission
Patient gets admitted for more than 24 hrs within 30 days of the last
discharge date. If a patient comes back to the hospital after 30 days, it is
not considered as Re-Admission.
Definitions

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Data Analysis Process
The below figure shows the typical processes of Data analysis of a Dataset.
Receive the
Datasets (.csv)
Process the
Datasets for
Analysis
Analyse the
Datasets
Build the Model
Visualize the
Analysed data

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In order to predict the re-admission, following data fields/predictors
were considered.
 Demographics – Age, Sex
 Lab data – Includes lab tests
 Vitals – Includes BP, Sugar, Weight, etc.
 Visit types – Emergency, In-patient, Outpatient
 Diagnosis – Diseases/ailments – Heart,Pnuemonia
 Previous hospital visit
 Length of stay
The Predictors – Predictive Analytics

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 The data was received as a set of .csv files which gave the complete
details of Demographics, Admission, vitals, lab tests of selected sample
of patients over a period of time.
 The processing of the data included the following activities:
o Removing commas, uploading .csv files to HDFS (Horton works)
o The required DDL scripts were written in Hive
o The necessary joins were written
o The result was refined datasets
 The refined datasets are passed on to Data Analysis team for analysis
Data Processing …

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Predictive Analysis Process
Use the Module – Analyse the dataset
Identify the Suitable Algorithm
Build the model
Evaluate/Deploy the model
Monitor/Refactor the model

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Datasets
The refined datasets are divided into train and test datasets in order to build the Model
30%
70%
Train Test

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The best model is arrived at by testing the data under different classifiers
and precision, recall and F1 score metrics calculated for each classifier.
 Gradient Boosting
 Random Forest
 Support Vector Machines
 Logistic Regression
 K-Nearest Neighbor
 Ridge
Evaluate Models

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Model - Process
ModelTraining
Dataset
Test
Dataset
Model
Final
Analyzed
Dataset

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Model – Fine tuning K- Fold Means
For tuning parameters and model selection, k-Fold
cross validation was used where data was split into K
equal partitions. 1 fold was used for testing and the
remaining for training. This was repeated K( K=4)
times and using the average testing accuracy.
Dataset

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Accuracy
Accuracy is measured by area under the ROC curve as shown below
0.77 accuracy is achieved by Random Forest as shown in the below curve

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Process – Data Analysis
The below figure shows the typical processes of Data analysis of a dataset.
1.Offline Processed
data is dumped into
Staging Data Mart
Rest Client
CAF
Analytics
Engine
Rest API
1. Builds Model running
Python scripts
2. Scores model

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Data Visualization – Report on the Model

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Actual Report on a Dataset

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Screen shots

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