ArticleAMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science2020
Automated Analysis of Public Health Laboratory Test Results.
Article in AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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
1 citing paper in PubMed.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This study investigates the use of machine learning methods for classifying and extracting structured information from laboratory reports stored as semi-structured point-form English text. This is a novel data format that has not been evaluated in conjunction with machine learning classifiers in previous literature. Our classifiers achieve human-level predictive accuracy on the binary Test Performed and 4-class Test Outcome labels. We consider symbolic approaches for predicting the highly multi-class Organism Genus and Organism Species labels. Results are discussed from the viewpoint of interpretability and generalizability to new incoming laboratory reports. Code has been made public at https://github.com/enchainingrealm/UbcDssgBccdc-Research/tree/master/src.
Identifiers
32477660PMC7233052What 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.