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Precision medicine that enables personalized treatment decision support has become an increasingly important research topic in chronic disease care. The main challenges in designing a treatment algorithm include modeling individual disease progression dynamics and designing adaptive treatment selection strategy. This study aims to develop an adaptive treatment selection framework tailored to an individual patient’s disease progression pattern and treatment response. We propose a Partially Observable Collaborative Model (POCM) to capture the individual variations in a heterogeneous population and optimize treatment outcomes in three stages. The POCM first infers the disease progression models by subgroup patterns using population data in stage one and then fine-tunes the models for individual patients with a small number of treatment trials in stage two. In stage three, we show how the treatment policies based on the Partially Observable Markov Decision Process (POMDP) can be tailored to individual patients by utilizing the disease models learned from the POCM. Using a simulated population of chronic depression patients, we show that the POCM can more accurately estimate the personal disease progression than the traditional method of solving a hidden Markov model. We also compare the POMDP treatment policies with other heuristic policies and demonstrate that the POCM-based policies give the highest net monetary benefits in majority of parameter settings. To conclude, the POCM method is a promising approach to model the chronic disease progression process and recommend a personalized treatment plan for individual patients in a heterogeneous population.
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Bayesian mixed-effect higher-order hidden Markov models with applications to predictive healthcare using electronic health records
The disease progression dynamics observed in electronic health records often reflect patients’ health condition evolution, holding the promise of enabling the development of clinical predictive models. These dynamics, however, generally display significant variability among patients, due to some critical factors (e.g., gender and age) and patient-level heterogeneity. Moreover, future health state may not only depend on the current state, but also more distant history states due to the complicated disease progression. To capture this complex transition behavior and address mixed effects in clinical prediction problems, we propose a novel and flexible Bayesian Mixed-Effect Higher-Order Hidden Markov Model (MHOHMM), and develop a classifier based on MHOHMMs. A range of MHOHMMs are designed to capture different data structures and the optimal one is identified by using the k-fold cross-validation approach. An effective two-stage Markov chain Monte Carlo (MCMC) sampling algorithm is designed for model inference. A simulation study is conducted to evaluate the performance of the proposed sampling algorithm and the MHOHMM-based classification method. The practical utility of the proposed framework is demonstrated by a case study on the acute hypotensive episode prediction for intensive care unit patients. Our results show that the MHOHMM-based framework provides good prediction performance.
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- Award ID(s):
- 2311216
- PAR ID:
- 10527399
- Editor(s):
- Yu_Ding_Georgia_Institute_of_Technology
- Publisher / Repository:
- Taylor and Francis
- Date Published:
- Journal Name:
- IISE Transactions
- ISSN:
- 2472-5854
- Page Range / eLocation ID:
- 1 to 13
- Subject(s) / Keyword(s):
- Clinical predictionhigher-order hidden Markov modelMCMC samplingmixed effects
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1080/24725854.2024.2302368
