Mixture of Input-Output Hidden Markov Models for Heterogeneous Disease Progression Modeling
원문 초록
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.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
A Review of the Long Horizon Forecasting Problem in Time Series Analysis
The interplay of robustness and generalization in quantum machine learning
TimePoint: Accelerated Time Series Alignment via Self-Supervised Keypoint and Descriptor Learning
LASSO-ODE: A framework for mechanistic model identifiability and selection in disease transmission modeling
MONAQ: Multi-Objective Neural Architecture Querying for Time-Series Analysis on Resource-Constrained Devices
A Better Alternative to Piecewise Linear Time Series Segmentation
토론
이 논문과 연결된 포럼 글이 아직 없습니다.