人工智能
体质指数
健康档案
计算机科学
病历
医学
计算机断层摄影术
人口统计学的
2型糖尿病
医学影像学
分割
机器学习
糖尿病
放射科
内科学
医疗保健
经济
人口学
社会学
内分泌学
经济增长
作者
Yucheng Tang,Riqiang Gao,Ho Hin Lee,Quinn S. Wells,Ashley Spann,James G. Terry,J. Jeffrey Carr,Yuankai Huo,Shunxing Bao,Bennett A. Landman
标识
DOI:10.1007/978-3-030-60946-7_2
摘要
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.
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