ArticleJournal of the American Medical Informatics Association : JAMIA2020
Recommendations for patient similarity classes: results of the AMIA 2019 workshop on defining patient similarity.
Article in Journal of the American Medical Informatics Association : JAMIA, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed.
- A multicenter, randomized, non-comparative, phase II study of nivolumab ± ipilimumab for patients with metastatic sarcoma (Alliance A091401): expansion cohorts and correlative analyses.Journal for immunotherapy of cancer · 2024Trial
- Convergence of machine learning and genomics for precision oncology.Nature reviews. Cancer · 2026Review
- Learning Patient Similarity from Genomics for Precision Oncology.medRxiv : the preprint server for health sciences · 2025Article
- Uncovering the Understanding of the Concept of Patient Similarity in Cancer Research and Treatment: Scoping Review.Journal of medical Internet research · 2025Article
- Palliative care pathways in Amyotrophic Lateral Sclerosis (ALS): a sequence analysis of health claims data.BMC palliative care · 2025Article
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- A large-scale dataset of patient summaries for retrieval-based clinical decision support systems.Scientific data · 2023Article
- Gene partners of the EWSR1 fusion may represent molecularly distinct entities.Translational oncology · 2023Article
- Comparison of variants in TPMT and NUDT15 between sequencing and genotyping methods in a multistate pediatric institution.Clinical and translational science · 2023Article
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- Using similar patients to predict complication in patients with diabetes, hypertension, and lipid disorder: a domain knowledge-infused convolutional neural network approach.Journal of the American Medical Informatics Association : JAMIA · 2023Article
- Can Machine Learning from Real-World Data Support Drug Treatment Decisions? A Prediction Modeling Case for Direct Oral Anticoagulants.Medical decision making : an international journal of the Society for Medical Decision Making · 2022Article
- A Novel Patient Similarity Network (PSN) Framework Based on Multi-Model Deep Learning for Precision Medicine.Journal of personalized medicine · 2022Article
- Drivers of genomic loss of heterozygosity in leiomyosarcoma are distinct from carcinomas.NPJ precision oncology · 2022Article
- Using Domain Knowledge and Data-Driven Insights for Patient Similarity Analytics.Journal of personalized medicine · 2021Article
- Multiscale-omic assessment of EWSR1-NFATc2 fusion positive sarcomas identifies the mTOR pathway as a potential therapeutic target.NPJ precision oncology · 2021Article
- OSIRIS: A Minimum Data Set for Data Sharing and Interoperability in Oncology.JCO clinical cancer informatics · 2021Article
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Authors and funding
18 authors.
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Abstract
Defining patient-to-patient similarity is essential for the development of precision medicine in clinical care and research. Conceptually, the identification of similar patient cohorts appears straightforward; however, universally accepted definitions remain elusive. Simultaneously, an explosion of vendors and published algorithms have emerged and all provide varied levels of functionality in identifying patient similarity categories. To provide clarity and a common framework for patient similarity, a workshop at the American Medical Informatics Association 2019 Annual Meeting was convened. This workshop included invited discussants from academics, the biotechnology industry, the FDA, and private practice oncology groups. Drawing from a broad range of backgrounds, workshop participants were able to coalesce around 4 major patient similarity classes: (1) feature, (2) outcome, (3) exposure, and (4) mixed-class. This perspective expands into these 4 subtypes more critically and offers the medical informatics community a means of communicating their work on this important topic.
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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.