ArticleArtificial intelligence in medicine2020
Using topological data analysis and pseudo time series to infer temporal phenotypes from electronic health records.
Article in Artificial intelligence in medicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 1 of them a synthesis that pooled it.
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Who cites it
15 citing papers in PubMed, 1 synthesis or guideline pooled it, 31 citations in OpenAlex.
- Multi-omics time-series analysis in microbiome research: a systematic review.Briefings in bioinformatics · 2025Pooled it
- The chronic heart failure evolutions: Different fates and routes.ESC heart failure · 2025Article
- Integrated Pleiotropic Gene Set Unveils Comorbidity Insights across Digestive Cancers and Other Diseases.Genes · 2024Article
- Unraveling cradle-to-grave disease trajectories from multilayer comorbidity networks.NPJ digital medicine · 2024Article
- Opportunities and challenges for biomarker discovery using electronic health record data.Trends in molecular medicine · 2023Review
- Stratification of diabetes in the context of comorbidities, using representation learning and topological data analysis.Scientific reports · 2023Article
- The high-dimensional space of human diseases built from diagnosis records and mapped to genetic loci.Nature computational science · 2023Article
- A progression analysis of motor features in Parkinson's disease based on the mapper algorithm.Frontiers in aging neuroscience · 2023Article
- Topological data analysis in biomedicine: A review.Journal of biomedical informatics · 2022Review
- Dictionary learning allows model-free pseudotime estimation of transcriptomic data.BMC genomics · 2022Article
- Analysis on Time-Series Data from Movie Using MF-DCCA Method and Recurrent Neural Network Model Under the Internet of Things.Computational intelligence and neuroscience · 2022Article
- A Process Mining Pipeline to Characterize COVID-19 Patients' Trajectories and Identify Relevant Temporal Phenotypes From EHR Data.Frontiers in public health · 2022Article
- Inferring temporal dynamics from cross-sectional data using Langevin dynamics.Royal Society open science · 2021Article
- A visual analytic approach for the identification of ICU patient subpopulations using ICD diagnostic codes.PeerJ. Computer science · 2021Article
- Proteomics and Informatics for Understanding Phases and Identifying Biomarkers in COVID-19 Disease.Journal of proteome research · 2020Review
Corrections and comments
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Authors and funding
8 authors at 5 institutions in 3 countries.
Funding
Abstract
Temporal phenotyping enables clinicians to better understand observable characteristics of a disease as it progresses. Modelling disease progression that captures interactions between phenotypes is inherently challenging. Temporal models that capture change in disease over time can identify the key features that characterize disease subtypes that underpin these trajectories. These models will enable clinicians to identify early warning signs of progression in specific sub-types and therefore to make informed decisions tailored to individual patients. In this paper, we explore two approaches to building temporal phenotypes based on the topology of data: topological data analysis and pseudo time-series. Using type 2 diabetes data, we show that the topological data analysis approach is able to identify disease trajectories and that pseudo time-series can infer a state space model characterized by transitions between hidden states that represent distinct temporal phenotypes. Both approaches highlight lipid profiles as key factors in distinguishing the phenotypes.
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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.