Evidence map›Paper›PMID 40417533›Full record

ArticleAMIA ... Annual Symposium proceedings. AMIA Symposium2024

Automating assignment of HIV+ patients into phenogroups from demography bound phenotype attack rates.

Nick Williams

Abstract read
In one paragraph

Article in AMIA ... Annual Symposium proceedings. AMIA Symposium, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from 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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

1 author.

Nick WilliamsThe Lister Hill National Center for Biomedical Communications, National Library of Medicine, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Evidence based medicine and health data for policy should update statistical data modeling methods to take advantage of at-scale data. One challenge with at-scale data is information segmentation for clinical presentation discovery and cohort assignment. We use gradient boosting machine (GBM) to segment 94,379,175,015 diagnostic clinical events attributable to 283,632,789 Centers for Medicare and Medicaid Services beneficiaries over 22 observation years. Diagnostic events were aggregated into attack rates by demography and Phenome-wide association studies (PheWas) codes. Resulting attack rates were then segmented within a user defined clinical status of interest, in this case HIV status. 1,753,647 HIV+ beneficiaries were considered. The GBM model assigned 19,651,408 PheWas attack rates from 69,133,296 ICD attack rates into phenogroups/nodes.

Indexed as

HIV InfectionsHumansPhenotypeUnited States

Identifiers

PMID40417533
PMCPMC12099429

What OpenQuestion holds

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Registered trials

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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.