1. Diagnosing Diabetes and Predicting Complications
Priya Sarkar
Lily Zerihun
Anqi Zhang
Mentor: Elizabeth Lorenzi
Advisor: Ricardo Henao, PhD
13 Diabetes Outcomes & Dataset Prevalence
Electronic Health Records Data
From Duke Hospital ( )
16, 604 Diabetic Patients
Demographic Information
Age (4% children)
Sex (59.86% female)
Race (56.29% POC)
Smoker (13.89%)
Cholesterol (total + HDL)
Blood pressure
Health Information
Medications
Laboratory Tests
Procedures/Diagnoses
Goals
Use exploratory clustering and visualization to identify patient subsets with related characteristics.
K-Means Clustering
TSNE High Dimension Visualization
Build cluster-specific models to improve performance in the prediction of diabetes complications.

2. Exploratory Clustering
Goal: Explore medications, labs, diagnoses, outcomes, and demographics. Identify meaningful clusters of similar patients. Explore the sources of similarities.
Example of Clustering Method
Exploring Top Medications
Clustering Lab Tests
TSNE Reduction
Cluster 1
Cluster 2
Amlodopine
(Hypertension)
Kidney and Heart Disease 2 1
Clustering “Sickest” Pt
With > 100 Diagnoses
Patient Clustering: K-means
Cluster 5
Cluster 3 8 2 9 7 5 4 1 6 3 10
Blood Pressure in 10 Clusters
Cluster 1:
Lung Complications
Mycosis Fungoides
Pancreatic Cancer
Sprain and Strain
Cluster 2:
Lung Cancer
Prostate Cancer
Renal Failure
Joint/Shoulder Pain 1 2

3. Comparing ROC Curves for 13 Complications
Predictive Modelling
Goal: Create and test the accuracy of a model to predict diabetes complications based on medications, lab tests, and diagnoses. .
Comparing AUC values for outcomes predicted with medications, laboratory tests, diagnoses, & combined models.
Comparing ROC Curves for 13 Complications
ROC Curve Plots Sensitivity and Specificity of the outcome prediction. It plots the true positive rate against the false positive rate.
The AUC (area under curve) specifies the accuracy of our model in predicting patients who will have a certain complication based on their medications, laboratory tests, and/or diagnosis data.