ArticleStudies in health technology and informatics2013
Automatic identification of comparative effectiveness research from medline citations to support clinicians' treatment information needs.
Article in Studies in health technology and informatics, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 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
3 citing papers in PubMed.
- Artificial Intelligence Clinical Evidence Engine for Automatic Identification, Prioritization, and Extraction of Relevant Clinical Oncology Research.JCO clinical cancer informatics · 2021Article
- Evaluation of an artificial intelligence clinical trial matching system in Australian lung cancer patients.JAMIA open · 2020Article
- Formative evaluation of a patient-specific clinical knowledge summarization tool.International journal of medical informatics · 2016Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors.
Funding
Abstract
unlabelledOnline knowledge resources such as Medline can address most clinicians' patient care information needs. Yet, significant barriers, notably lack of time, limit the use of these sources at the point of care. The most common information needs raised by clinicians are treatment-related. Comparative effectiveness studies allow clinicians to consider multiple treatment alternatives for a particular problem. Still, solutions are needed to enable efficient and effective consumption of comparative effectiveness research at the point of care.
objectiveDesign and assess an algorithm for automatically identifying comparative effectiveness studies and extracting the interventions investigated in these studies.
methodsThe algorithm combines semantic natural language processing, Medline citation metadata, and machine learning techniques. We assessed the algorithm in a case study of treatment alternatives for depression.
resultsBoth precision and recall for identifying comparative studies was 0.83. A total of 86% of the interventions extracted perfectly or partially matched the gold standard.
conclusionOverall, the algorithm achieved reasonable performance. The method provides building blocks for the automatic summarization of comparative effectiveness research to inform point of care decision-making.
Indexed as
Identifiers
23920677PMC3940695What 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.