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Home > Proceedings / Proceedings of the AAAI Conference on Artificial Intelligence, 32

Early Prediction of Diabetes Complications from Electronic Health Records: A Multi-Task Survival Analysis Approach

March 15, 2023

Authors

Bin Liu

IBM Thomas J. Watson Research Center


Ying Li

IBM Thomas J. Watson Research Center


Zhaonan Sun

IBM Thomas J. Watson Research Center


Soumya Ghosh

IBM Thomas J. Watson Research Center


Kenney Ng

IBM Thomas J. Watson Research Center


Published:

2018-02-08

Proceedings:

Proceedings of the AAAI Conference on Artificial Intelligence, 32

Volume

Issue:

Thirty-Second AAAI Conference on Artificial Intelligence 2018

Track:

AAAI Technical Track: Applications

Downloads:

Download PDF

Abstract:

Type 2 diabetes mellitus (T2DM) is a chronic disease that usually results in multiple complications. Early identification of individuals at risk for complications after being diagnosed with T2DM is of significant clinical value. In this paper, we present a new data-driven predictive approach to predict when a patient will develop complications after the initial T2DM diagnosis. We propose a novel survival analysis method to model the time-to-event of T2DM complications designed to simultaneously achieve two important metrics: 1) accurate prediction of event times, and 2) good ranking of the relative risks of two patients. Moreover, to better capture the correlations of time-to-events of the multiple complications, we further develop a multi-task version of the survival model. To assess the performance of these approaches, we perform extensive experiments on patient level data extracted from a large electronic health record claims database. The results show that our new proposed survival analysis approach consistently outperforms traditional survival models and demonstrate the effectiveness of the multi-task framework over modeling each complication independently.

DOI:

10.1609/aaai.v32i1.11290


AAAI

Thirty-Second AAAI Conference on Artificial Intelligence 2018


ISSN 2374-3468 (Online) ISSN 2159-5399 (Print)


Published by AAAI Press, Palo Alto, California USA Copyright © 2018, Association for the Advancement of Artificial Intelligence All Rights Reserved.

Topics: AAAI

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