Evidence map›Paper›PMID 29888065›Full record

ArticleAMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science2018

Accurate and interpretable intensive care risk adjustment for fused clinical data with generalized additive models.

Ben J Marafino, R Adams Dudley, Nigam H Shah, Jonathan H Chen

Registry-linked trialAbstract read
PubMed Full text at PMC
In one paragraph

Article in AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT07788898 (AI-enabled Communication to Advance Research Engagement), which is not on this map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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.

NCT07788898 nanot yet recruitingnot on this mapstarted 2026, after this paper: background citation

AI-enabled Communication to Advance Research Engagement

TypeinterventionalSponsorAlbert Einstein College of MedicineRan2026 to 2028Enrolled250ConditionsAI Literacy, Research CommunicationArmsCommunity co-created AI summaries, Experimental: Researcher prompt engineering, Experimental: Standard AI prompt, AI literacy training and co-design
3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
  2. Review
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

4 authors.

Ben J MarafinoBiomedical Informatics Training Program, Stanford University, Stanford, CA.
R Adams DudleyCenter for Healthcare Value, Philip R. Lee Institute for Health Policy Studies, and Department of Pulmonary and Critical Care Medicine, University of California, San Francisco, San Francisco, CA.
Nigam H ShahStanford Center for Biomedical Informatics Research, Stanford University, Stanford, CA.
Jonathan H ChenStanford Center for Biomedical Informatics Research, Stanford University, Stanford, CA.

Funding

POSTDOCTORAL TRAINING IN MEDICAL INFORMATION SCIENCEST15LM007033 · NLM · STANFORD UNIVERSITY · PI SYLVIA KATINA PLEVRITIS · 1985 to 2026
$25.5M
Data-Mining Clinical Decision Support from Electronic Health RecordsK01ES026837 · NIEHS · STANFORD UNIVERSITY · PI CHEN, JONATHAN H. · 2015 to 2019
$893k
NIEHS NIH HHS K01 ES026837NLM NIH HHS T15 LM007033
6 · The paper itself

Abstract

Risk adjustment models for intensive care outcomes have yet to realize the full potential of data unlocked by the increasing adoption of EHRs. In particular, they fail to fully leverage the information present in longitudinal, structured clinical data - including laboratory test results and vital signs - nor can they infer patient state from unstructured clinical narratives without lengthy manual abstraction. A fully electronic ICU risk model fusing these two types of data sources may yield improved accuracy and more personalized risk estimates, and in obviating manual abstraction, could also be used for real-time decision-making. As a first step towards fully "electronic" ICU models based on fused data, we present results of generalized additive modeling applied to a sample of over 36,000 ICU patients. Our approach outperforms those based on the SAPS and OASIS systems (A UC: 0.908 vs. 0.794 and 0.874), and appears to yield more granular and easily visualized risk estimates.

Identifiers

PMID29888065
PMCPMC5961794

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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.