ArticleAMIA ... Annual Symposium proceedings. AMIA Symposium2018
Phenotyping through Semi-Supervised Tensor Factorization (PSST).
Article in AMIA ... Annual Symposium proceedings. AMIA Symposium, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed, 23 citations in OpenAlex.
- Advancing the Use of Longitudinal Electronic Health Records: Tutorial for Uncovering Real-World Evidence in Chronic Disease Outcomes.Journal of medical Internet research · 2025Article
- PheWJAMIA open · 2024Article
- A novel method leveraging time series data to improve subphenotyping and application in critically ill patients with COVID-19.Artificial intelligence in medicine · 2024Article
- Generate Analysis-Ready Data for Real-world Evidence: Tutorial for Harnessing Electronic Health Records With Advanced Informatic Technologies.Journal of medical Internet research · 2023Article
- Machine learning approaches for electronic health records phenotyping: a methodical review.Journal of the American Medical Informatics Association : JAMIA · 2023Article
- Probabilistic Machine Learning for Healthcare.Annual review of biomedical data science · 2021Review
- Generalized and transferable patient language representation for phenotyping with limited data.Journal of biomedical informatics · 2021Article
- Similarity-based health risk prediction using Domain Fusion and electronic health records data.Journal of biomedical informatics · 2021Article
- A Survey of Challenges and Opportunities in Sensing and Analytics for Risk Factors of Cardiovascular Disorders.ACM transactions on computing for healthcare · 2021Article
- Privacy-preserving Sequential Pattern Mining in distributed EHRs for Predicting Cardiovascular Disease.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational ScienceArticle
Corrections and comments
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Authors and funding
7 authors at 4 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
A computational phenotype is a set of clinically relevant and interesting characteristics that describe patients with a given condition. Various machine learning methods have been proposed to derive phenotypes in an automatic, high-throughput manner. Among these methods, computational phenotyping through tensor factorization has been shown to produce clinically interesting phenotypes. However, few of these methods incorporate auxiliary patient information into the phenotype derivation process. In this work, we introduce Phenotyping through Semi-Supervised Tensor Factorization (PSST), a method that leverages disease status knowledge about subsets of patients to generate computational phenotypes from tensors constructed from the electronic health records of patients. We demonstrate the potential of PSST to uncover predictive and clinically interesting computational phenotypes through case studies focusing on type-2 diabetes and resistant hypertension. PSST yields more discriminative phenotypes compared to the unsupervised methods and more meaningful phenotypes compared to a supervised method.
Indexed as
Identifiers
30815097PMC6371355W2941840911What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.