Prediction of Type II Diabetes Onset with Computed Tomography and Electronic Medical Records

• Published in: Lecture notes in computer science Vol. 12445; pp. 13 - 23
• Main Authors: Tang, Yucheng, Gao, Riqiang, Lee, Ho Hin, Wells, Quinn Stanton, Spann, Ashley, Terry, James G., Carr, John J., Huo, Yuankai, Bao, Shunxing, Landman, Bennett A.
• Format: Book Chapter Journal Article
• Language: English
• Published: Switzerland Springer International Publishing AG 01.01.2020; Springer International Publishing
• Series: Lecture Notes in Computer Science
• Subjects: Computed tomography; Disease onset prediction; Electronic medical records; Metabolic syndrome; Type II diabetes; Type II diabetes; Disease onset prediction; Computed tomography; Metabolic syndrome; Electronic medical records
• ISBN: 9783030609450; 3030609456
• ISSN: 0302-9743; 1611-3349; 1611-3349
• DOI: 10.1007/978-3-030-60946-7_2

Type II diabetes mellitus (T2DM) is a significant public health concern with multiple known risk factors (e.g., body mass index (BMI), body fat distribution, glucose levels). Improved prediction or prognosis would enable earlier intervention before possibly irreversible damage has occurred. Meanwhile, abdominal computed tomography (CT) is a relatively common imaging technique. Herein, we explore secondary use of the CT imaging data to refine the risk profile of future diagnosis of T2DM. In this work, we delineate quantitative information and imaging slices of patient history to predict onset T2DM retrieved from ICD-9 codes at least one year in the future. Furthermore, we investigate the role of five different types of electronic medical records (EMR), specifically 1) demographics; 2) pancreas volume; 3) visceral/subcutaneous fat volumes in L2 region of interest; 4) abdominal body fat distribution and 5) glucose lab tests in prediction. Next, we build a deep neural network to predict onset T2DM with pancreas imaging slices. Finally, motivated by multi-modal machine learning, we construct a merged framework to combine CT imaging slices with EMR information to refine the prediction. We empirically demonstrate our proposed joint analysis involving images and EMR leads to 4.25% and 6.93% AUC increase in predicting T2DM compared with only using images or EMR. In this study, we used case-control dataset of 997 subjects with CT scans and contextual EMR scores. To the best of our knowledge, this is the first work to show the ability to prognose T2DM using the patients’ contextual and imaging history. We believe this study has promising potential for heterogeneous data analysis and multi-modal medical applications.