Diagnosing Diabetes and Predicting Complications

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Presentation transcript:

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 (2007-2011) 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.

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

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.