Identifying a Trial Population for Clinical Studies on Diabetes Drug Testing with Neural Networks

Abstract

This project aims to model an end-to-end workflow of implementing different Artificial Intelligence (AI) tools for a clinical environment. A possible use case, such as the selection process of patients for a novel treatment, will be conducted by estimating the hospitalization time with a Neural Network on an Electronic Health Record (EHR) of diabetes. Then, Explainable AI (XAI) methods are computed for models trained with a Random Forest to evaluate the predictions. The diabetes readmission EHR dataset from the University of California, Irvine (UCI) Diabetes is used for this project. The trial population is selected by predicting the expected days for a person being hospitalized. An arbitrary boundary is set for choosing whether or not a patient shall be included into the trial. If so, a clear explanation of how the prediction is calculated and additional possible risk factors will be given in order to make the workflow explainable. This project shows that given a proper explanatory approach, AI can be a useful tool for the modern clinical environment. The workflow finally reveals that AI can be a beneficial support tool for doctors in the patient selection process.