ArticleJAMIA open2025
Aligning prediction models with clinical information needs: infant sepsis case study.
Article in JAMIA open, 2025. 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
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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.
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Who cites it
1 citing paper in PubMed.
- Transforming neonatal care through informatics: A review of artificial intelligence, data, and implementation considerations.Seminars in perinatology · 2025Review
Corrections and comments
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Authors and funding
10 authors.
Funding
Abstract
Objective: Sepsis recognition among infants in the Neonatal Intensive Care Unit (NICU) is challenging and delays in recognition can result in devastating consequences. Although predictive models may improve sepsis outcomes, clinical adoption has been limited. Our focus was to align model behavior with clinician information needs by developing a machine learning (ML) pipeline with two components: (1) a model to predict baseline sepsis risk and (2) a model to detect evolving (dynamic) sepsis risk due to physiologic changes. We then compared the performance of this two-component pipeline to a single model that combines all features reflecting both baseline risk and evolving risk. Materials and Methods: We developed prediction models (two-stage pipeline and a single model) using logistic regression and XGBoost trained on electronic healthcare record data of an NICU cohort (1706 observations from 1094 patients, with a 1:1 ratio of cases to controls). We used nested 10-fold cross-validation to evaluate model performance on predictions made 1 h ( Results: The single model (XGBoost) achieved the best performance with a sensitivity of 0.77 (0.74, 0.80), specificity of 0.83 (0.80, 0.85), and positive predictive value (PPV) of 0.82 (0.79, 0.84), at 1 h prior to clinical sepsis recognition ( Discussion: Our findings highlight the challenges of aligning machine learning with NICU clinical decision-making processes. The two-stage pipeline, designed to mirror clinicians' reasoning, underperformed compared to the single model. Future work should explore integrating continuous physiological data to enhance real-time risk assessment. Conclusion: Although a pipeline model that separately estimates baseline and dynamic sepsis risk aligns with clinical information needs, at similar levels of specificity the observed sensitivity of the pipeline is inferior to that of a single model. Additional research is needed to better align model outputs with clinician information needs.
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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.