ArticleNursing outlook
Comparing the influence of social risk factors on machine learning model performance across racial and ethnic groups in home healthcare.
Article in Nursing outlook. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
This study examined the impact of social risk factors on machine learning model performance for predicting hospitalization and emergency department visits in home healthcare. Using retrospective data from one U.S. home healthcare agency, four models were developed with unstructured social information documented in clinical notes. Performance was compared with and without social factors. A subgroup analyses was conducted by race and ethnicity to assess for fairness. LightGBM performed best overall. Social factors had a modest effect, but findings highlight the feasibility of integrating unstructured social information into machine learning models and the importance of fairness evaluation in home healthcare.
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Registered trials
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