Evidence map›Paper›PMID 37066102›Full record

ArticleLearning health systems2023

CBK model composition using paired web services and executable functions: A demonstration for individualizing preventive services.

Allen Flynn, Glen Taksler, Tanner Caverly, Adam Beck, Peter Boisvert, Philip Boonstra, Nate Gittlen, George Meng, Brooke Raths, Charles P Friedman

Open access · goldAbstract read
In one paragraph

Article in Learning health systems, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
1.6field-weighted citation impact, top 11% of its field
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

7 citing papers in PubMed, 6 citations in OpenAlex.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors at 2 institutions in 1 country.

Allen FlynnDepartment of Learning Health Sciences Medical School University of Michigan Ann Arbor Michigan USA.ORCID https://orcid.org/0000-0002-3471-3063
Glen TakslerCleveland Clinic, Internal Medicine and Geriatrics Cleveland Ohio USA.
Tanner CaverlyDepartment of Learning Health Sciences Medical School University of Michigan Ann Arbor Michigan USA.
Adam BeckDepartment of Learning Health Sciences Medical School University of Michigan Ann Arbor Michigan USA.
Peter BoisvertDepartment of Learning Health Sciences Medical School University of Michigan Ann Arbor Michigan USA.
Philip BoonstraSchool of Public Health University of Michigan Ann Arbor Michigan USA.
Nate GittlenDepartment of Learning Health Sciences Medical School University of Michigan Ann Arbor Michigan USA.
George MengDepartment of Learning Health Sciences Medical School University of Michigan Ann Arbor Michigan USA.
Brooke RathsDepartment of Learning Health Sciences Medical School University of Michigan Ann Arbor Michigan USA.
Charles P FriedmanDepartment of Learning Health Sciences Medical School University of Michigan Ann Arbor Michigan USA.
University of Michigan · USCleveland Clinic · US

Funding

Testing the Effectiveness of Individualized Disease Prevention for Middle-Aged AdultsR01AG059979 · NIA · CLEVELAND CLINIC LERNER COM-CWRU · PI TAKSLER, GLEN · 2019 to 2023
$3.4M
AHRQ HHS R21 HS026257NIA NIH HHS R01 AG059979
6 · The paper itself

Abstract

Introduction: Learning health systems are challenged to combine computable biomedical knowledge (CBK) models. Using common technical capabilities of the World Wide Web (WWW), digital objects called Knowledge Objects, and a new pattern of activating CBK models brought forth here, we aim to show that it is possible to compose CBK models in more highly standardized and potentially easier, more useful ways. Methods: Using previously specified compound digital objects called Knowledge Objects, CBK models are packaged with metadata, API descriptions, and runtime requirements. Using open-source runtimes and a tool we developed called the KGrid Activator, CBK models can be instantiated inside runtimes and made accessible via RESTful APIs by the KGrid Activator. The KGrid Activator then serves as a gateway and provides a means to interconnect CBK model outputs and inputs, thereby establishing a CBK model composition method. Results: To demonstrate our model composition method, we developed a complex composite CBK model from 42 CBK submodels. The resulting model called CM-IPP is used to compute life-gain estimates for individuals based their personal characteristics. Our result is an externalized, highly modularized CM-IPP implementation that can be distributed and made runnable in any common server environment. Discussion: CBK model composition using compound digital objects and the distributed computing technologies is feasible. Our method of model composition might be usefully extended to bring about large ecosystems of distinct CBK models that can be fitted and re-fitted in various ways to form new composites. Remaining challenges related to the design of composite models include identifying appropriate model boundaries and organizing submodels to separate computational concerns while optimizing reuse potential. Conclusion: Learning health systems need methods for combining CBK models from a variety of sources to create more complex and useful composite models. It is feasible to leverage Knowledge Objects and common API methods in combination to compose CBK models into complex composite models.

Indexed as

computable biomedical knowledgedecentralized web technologymodel composition

Identifiers

PMID37066102
PMCPMC10091204
OpenAlexW4289711564

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.