ArticleJournal of biomedical informatics2023
APLUS: A Python library for usefulness simulations of machine learning models in healthcare.
Article in Journal of biomedical informatics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
13 citing papers in PubMed, 26 citations in OpenAlex.
- Computable Phenotype for Identifying Undiagnosed Hypermobile Ehlers-Danlos Syndrome: Protocol for a Development and Validation Study.JMIR research protocols · 2026Article
- Review
- Prognostication and clinical opportunities with AI for coronary artery calcium: a scoping review.BMJ digital health & AI · 2026Article
- Hybrid modelling using simulation and machine learning in healthcare.Computers & operations research · 2026Review
- Combined Applications of Artificial Intelligence and Simulation for Healthcare Process Optimization: A Systematic Review.Healthcare (Basel, Switzerland) · 2025Review
- In Silico Evaluation of Algorithm-Based Clinical Decision Support Systems: Protocol for a Scoping Review.JMIR research protocols · 2025Article
- Developing a Research Center for Artificial Intelligence in Medicine.Mayo Clinic proceedings. Digital health · 2024Article
- Ensuring useful adoption of generative artificial intelligence in healthcare.Journal of the American Medical Informatics Association : JAMIA · 2024Review
- Thick Data Analytics (TDA): An Iterative and Inductive Framework for Algorithmic Improvement.The American statistician · 2024Article
- Machine learning in transfusion medicine: A scoping review.Transfusion · 2024Article
- The shaky foundations of large language models and foundation models for electronic health records.NPJ digital medicine · 2023Review
- Applicability Area: A novel utility-based approach for evaluating predictive models, beyond discrimination.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2023Article
- Precision medicine and Patient Blood Management - A good pairing.Blood transfusion = Trasfusione del sangueArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 3 institutions in 1 country.
Funding
Abstract
Despite the creation of thousands of machine learning (ML) models, the promise of improving patient care with ML remains largely unrealized. Adoption into clinical practice is lagging, in large part due to disconnects between how ML practitioners evaluate models and what is required for their successful integration into care delivery. Models are just one component of care delivery workflows whose constraints determine clinicians' abilities to act on models' outputs. However, methods to evaluate the usefulness of models in the context of their corresponding workflows are currently limited. To bridge this gap we developed APLUS, a reusable framework for quantitatively assessing via simulation the utility gained from integrating a model into a clinical workflow. We describe the APLUS simulation engine and workflow specification language, and apply it to evaluate a novel ML-based screening pathway for detecting peripheral artery disease at Stanford Health Care.
Indexed as
Identifiers
What 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.