Using an optimized generative model to infer the progression of complications in type 2 diabetes patients
Year: 2022
Type: article
Abstract: BACKGROUND: People live a long time in pre-diabetes/early diabetes without a formal diagnosis or management. Heterogeneity of progression coupled with deficiencies in electronic health records related to incomplete data, discrete events, and irregular event intervals make identification of pre-diabetes and critical points of diabetes progression challenging. METHODS: We utilized longitudinal electronic health records of 9298 patients with type 2 diabetes or prediabetes from 2005 to 2016 from a large regional healthcare delivery network in China. We optimized a generative Markov-Bayesian-based model to generate 5000 synthetic illness trajectories. The synthetic data were manually reviewed by endocrinologists. RESULTS: We build an optimized generative progression model for type 2 diabetes using anchor information to reduce the number of parameters learning in the third layer of the model from [Formula: see text] to [Formula: see text], where [Formula: see text] is the number of clinical findings, [Formula: see text] is the number of complications, [Formula: see text] is the number of anchors. Based on this model, we infer the relationships between progression stages, the onset of complication categories, and the associated diagnoses during the whole progression of type 2 diabetes using electronic health records. DISCUSSION: Our findings indicate that 55.3% of single complications and 31.8% of complication patterns could be predicted early and managed appropriately to potentially delay (as it is a progressive disease) or prevented (by lifestyle modifications that keep patient from developing/triggering diabetes in the first place). CONCLUSIONS: The full type 2 diabetes patient trajectories generated by the chronic disease progression model can counter a lack of real-world evidence of desired longitudinal timeframe while facilitating population health management.
Institutions: Fudan University, Northwest Normal University, West China Hospital of Sichuan University, Shanghai Jiao Tong University, Suzhou Kowloon Hospital, Shanghai University of Traditional Chinese Medicine, Yueyang Hospital, Brigham and Women's Hospital, Harvard University, Mass General Brigham
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,490
OpenAlex ID: W4283765020