ArticleStudies in health technology and informatics2019
Interactive Machine Learning for Laboratory Data Integration.
Article in Studies in health technology and informatics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 1 of them a synthesis that pooled 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.
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
7 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A Review of Recent Deep Learning Approaches in Human-Centered Machine Learning.Sensors (Basel, Switzerland) · 2021Pooled it
- Entity-aware pathology workflow informatics: Structured evidence mapping and a reference architecture for governed orchestration.Journal of pathology informatics · 2026Review
- Applications of machine learning in routine laboratory medicine: Current state and future directions.Clinical biochemistry · 2022Review
- Lessons learned from an enterprise-wide clinical datathon.Journal of clinical and translational science · 2022Article
- Logical Observation Identifiers Names and Codes (LOINCDiagnostics (Basel, Switzerland) · 2021Article
- Has the Flood Entered the Basement? A Systematic Literature Review about Machine Learning in Laboratory Medicine.Diagnostics (Basel, Switzerland) · 2021Review
- Natural Language Processing of Serum Protein Electrophoresis Reports in the Veterans Affairs Health Care System.JCO clinical cancer informatics · 2020Article
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.
Funding
Abstract
Laboratory data collected in the electronic health record as part of routine care can be used in secondary research. For example, the US Department of Veterans Affairs maintains a data warehouse covering over 20 million individuals and 6.6 billion lab tests. However, data aggregation in such a data warehouse can be difficult. In order to retrieve all or nearly all of one type of lab result with a high degree of precision, we perform clinical concept adjudication, which is the process of an expert determining which database records correspond to a target clinical concept. In this work, we develop an interactive machine learning tool to "extend the reach" of expert laboratory test adjudicators. Our tool provides access to automatic laboratory classification in a user-facing front end that covers all steps in an adjudication workflow, in order to lower barriers to collaboration, increase transparency of adjudication, and to promote efficiencies and data reuse.
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.