A particular challenge for disease progression modeling is the heterogeneity
of a disease and its manifestations in the patients. Existing approaches often
assume the presence of a single disease progression characteristics which is
unlikely for neurodegenerative disorders such as Parkinson's disease. In this
paper, we propose a hierarchical time-series model that can discover multiple
disease progression dynamics. The proposed model is an extension of an
input-output hidden Markov model that takes into account the clinical
assessments of patients' health status and prescribed medications. We
illustrate the benefits of our model using a synthetically generated dataset
and a real-world longitudinal dataset for Parkinson's disease.

提出了一种层次时间序列模型，该模型可发现多种疾病进展动态，可用于帕金森病患者的临床评估和处方药物，并在合成数据集和帕金森病的现实临床数据中得到了证明。

混合输入输出隐马尔可夫模型用于异质性疾病进展建模

Mixture of Input-Output Hidden Markov Models for Heterogeneous Disease Progression Modeling

Personalized predictive medicine necessitates the modeling of patient illness
and care processes, which inherently have long-term temporal dependencies.
Healthcare observations, recorded in electronic medical records, are episodic
and irregular in time. We introduce DeepCare, an end-to-end deep dynamic neural
network that reads medical records, stores previous illness history, infers
current illness states and predicts future medical outcomes. At the data level,
DeepCare represents care episodes as vectors in space, models patient health
state trajectories through explicit memory of historical records. Built on Long
Short-Term Memory (LSTM), DeepCare introduces time parameterizations to handle
irregular timed events by moderating the forgetting and consolidation of memory
cells. DeepCare also incorporates medical interventions that change the course
of illness and shape future medical risk. Moving up to the health state level,
historical and present health states are then aggregated through multiscale
temporal pooling, before passing through a neural network that estimates future
outcomes. We demonstrate the efficacy of DeepCare for disease progression
modeling, intervention recommendation, and future risk prediction. On two
important cohorts with heavy social and economic burden -- diabetes and mental
health -- the results show improved modeling and risk prediction accuracy.

本文介绍了一种名为 DeepCare 的深度动态神经网络，用于预测医疗结果，其基于医疗记录建模，包括以往病史、当前病情以及将来的分析，表现出了良好的疾病进展建模、干预建议和风险预测的效果。