# A Hidden Absorbing Semi-Markov Model for Informatively Censored Temporal Data: Learning and Inference

arXiv: https://arxiv.org/abs/1612.06007 · HTML: https://mlsanity.com/paper/1612.06007/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Dec 2016.

## Built on by (most cited first, 6 of 6)

- [Clairvoyance: A Pipeline Toolkit for Medical Time Series](https://mlsanity.com/paper/2310.18688/) (2023)
- [TemporAI: Facilitating Machine Learning Innovation in Time Domain Tasks for Medicine](https://mlsanity.com/paper/2301.12260/) (2023)
- [Unscrambling disease progression at scale: fast inference of event permutations with optimal transport](https://mlsanity.com/paper/2410.14388/) (2024)
- [A Foundational Framework and Methodology for Personalized Early and Timely Diagnosis](https://mlsanity.com/paper/2311.16195/) (2023)
- [Predicting Clinical Deterioration in Hospitals](https://mlsanity.com/paper/2102.05856/) (2021)
- [Forward-Backward Latent State Inference for Hidden Continuous-Time semi-Markov Chains](https://mlsanity.com/paper/2210.09058/) (2022)

## Abstract (arXiv)

Modeling continuous-time physiological processes that manifest a patient's evolving clinical states is a key step in approaching many problems in healthcare. In this paper, we develop the Hidden Absorbing Semi-Markov Model (HASMM): a versatile probabilistic model that is capable of capturing the modern electronic health record (EHR) data. Unlike exist- ing models, an HASMM accommodates irregularly sampled, temporally correlated, and informatively censored physiological data, and can describe non-stationary clinical state transitions. Learning an HASMM from the EHR data is achieved via a novel forward- filtering backward-sampling Monte-Carlo EM algorithm that exploits the knowledge of the end-point clinical outcomes (informative censoring) in the EHR data, and implements the E-step by sequentially sampling the patients' clinical states in the reverse-time direction while conditioning on the future states. Real-time inferences are drawn via a forward- filtering algorithm that operates on a virtually constructed discrete-time embedded Markov chain that mirrors the patient's continuous-time state trajectory. We demonstrate the di- agnostic and prognostic utility of the HASMM in a critical care prognosis setting using a real-world dataset for patients admitted to the Ronald Reagan UCLA Medical Center.