EvidenceMap: a three-level knowledge representation for medical evidence computation and comprehension.
J Am Med Inform Assoc
; 30(6): 1022-1031, 2023 05 19.
Article
in English
| MEDLINE | ID: covidwho-2265425
ABSTRACT
OBJECTIVE:
To develop a computable representation for medical evidence and to contribute a gold standard dataset of annotated randomized controlled trial (RCT) abstracts, along with a natural language processing (NLP) pipeline for transforming free-text RCT evidence in PubMed into the structured representation. MATERIALS ANDMETHODS:
Our representation, EvidenceMap, consists of 3 levels of abstraction Medical Evidence Entity, Proposition and Map, to represent the hierarchical structure of medical evidence composition. Randomly selected RCT abstracts were annotated following EvidenceMap based on the consensus of 2 independent annotators to train an NLP pipeline. Via a user study, we measured how the EvidenceMap improved evidence comprehension and analyzed its representative capacity by comparing the evidence annotation with EvidenceMap representation and without following any specific guidelines.RESULTS:
Two corpora including 229 disease-agnostic and 80 COVID-19 RCT abstracts were annotated, yielding 12 725 entities and 1602 propositions. EvidenceMap saves users 51.9% of the time compared to reading raw-text abstracts. Most evidence elements identified during the freeform annotation were successfully represented by EvidenceMap, and users gave the enrollment, study design, and study Results sections mean 5-scale Likert ratings of 4.85, 4.70, and 4.20, respectively. The end-to-end evaluations of the pipeline show that the evidence proposition formulation achieves F1 scores of 0.84 and 0.86 in the adjusted random index score.CONCLUSIONS:
EvidenceMap extends the participant, intervention, comparator, and outcome framework into 3 levels of abstraction for transforming free-text evidence from the clinical literature into a computable structure. It can be used as an interoperable format for better evidence retrieval and synthesis and an interpretable representation to efficiently comprehend RCT findings.Keywords
Full text:
Available
Collection:
International databases
Database:
MEDLINE
Main subject:
Comprehension
/
COVID-19
Type of study:
Experimental Studies
/
Prognostic study
/
Randomized controlled trials
/
Reviews
Limits:
Humans
Language:
English
Journal:
J Am Med Inform Assoc
Journal subject:
Medical Informatics
Year:
2023
Document Type:
Article
Affiliation country:
Jamia
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