ArticleLearning health systems2018
Desiderata for sharable computable biomedical knowledge for learning health systems.
Article in Learning health systems, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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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.
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Who cites it
9 citing papers in PubMed.
- Evaluating health organization readiness for implementing a learning health system: a scoping review.Health research policy and systems · 2025Article
- A scoping review, novel taxonomy and catalogue of implementation frameworks for clinical decision support systems.BMC medical informatics and decision making · 2024Article
- Ten simple rules to make computable knowledge shareable and reusable.PLoS computational biology · 2024Article
- Socio-technical infrastructure for a learning health system.Learning health systems · 2024Article
- Applicability Area: A novel utility-based approach for evaluating predictive models, beyond discrimination.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2023Article
- Categorizing metadata to help mobilize computable biomedical knowledge.Learning health systems · 2022Article
- Computable knowledge: An imperative for Learning Health Systems.Learning health systems · 2019Article
- Artificial Intelligence in Clinical Decision Support: Challenges for Evaluating AI and Practical Implications.Yearbook of medical informatics · 2019Review
- Desiderata for sharable computable biomedical knowledge for learning health systems.Learning health systems · 2018Article
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In this commentary, we work out the specific desired functions required for sharing knowledge objects (based on statistical models) presumably to be used for clinical decision support derived from a learning health system, and, in so doing, discuss the implications for novel knowledge architectures. We will demonstrate how decision models, implemented as influence diagrams, satisfy the desiderata. The desiderata include locally validate discrimination, locally validate calibration, locally recalculate thresholds by incorporating local preferences, provide explanation, enable monitoring, enable debiasing, account for generalizability, account for semantic uncertainty, shall be findable, and others as necessary and proper. We demonstrate how formal decision models, especially when implemented as influence diagrams based on Bayesian networks, support both the knowledge artifact itself (the "primary decision") and the "meta-decision" of whether to deploy the knowledge artifact. We close with a research and development agenda to put this framework into place.
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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.