ArticleNPJ digital medicine2025
Predicting clinical pathways of traumatic brain injuries (TBIs) through process mining.
Article in NPJ digital medicine, 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.
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The quality of healthcare services is influenced by a multitude of unpredictable events. Changes in patient clinical conditions and challenges in service organization are only some of the vivid examples that can make the management in healthcare difficult. Estimating patient journeys, known as clinical pathways (CPs), can support care providers in resource planning and enhancing service efficiency. This study presents a decision support system to assist clinicians in predicting CPs and outcomes for patients with traumatic brain injuries (TBIs). This machine learning framework employs an optimal decision tree next to a Markov-based trace clustering as predictive model components. A Shapely value approach extract knowledge of features contribution at both individual and population levels. The proposed approach is validated through a real-life event data, demonstrating high accuracy and providing insights into the rationale behind specific CP predictions which facilitate the adoption of machine learning models in clinical settings.
Identifiers
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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.