ArticleNPJ digital medicine2024
Zero shot health trajectory prediction using transformer.
Article in NPJ digital medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
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
24 citing papers in PubMed.
- AI-based multimodal integration of genomics and electronic health records.Nature reviews. Genetics · 2026Review
- Artificial Intelligence in Cardiovascular Risk Prediction: An Up-to-Date Narrative Review on the Emerging Role of Lipid Profile-Based Models.Journal of clinical medicine · 2026Review
- Article
- Evaluation and Comparison of Latent Health Risk Prediction Models for Clinical Triage: Protocol for a Mixed Methods Study.JMIR research protocols · 2026Article
- From Integrated Care to Learning Systems.Healthcare (Basel, Switzerland) · 2026Review
- Toward scalable early cancer detection: evaluating EHR-based predictive models against traditional screening criteria.NPJ precision oncology · 2026Article
- Foundation models in healthcare: a comprehensive review from technical advances to clinical translation.Journal of translational medicine · 2026Review
- SurvivEHR: a competing risks, time-to-event foundation model for multiple long-term conditions from primary care electronic health records.NPJ digital medicine · 2026Article
- Scalable discovery and validation of order-specific electronic health record event trajectories for interpretable adverse-outcome risk estimation.Journal of biomedical informatics · 2026Article
- The transformative potential of artificial intelligence in pediatric medicine: Current applications, methodological challenges, and future directions.Pediatric investigation · 2026Review
- How to interpret 'zero-shot' results from generative EHR models.Nature medicine · 2026Article
- Large language models in emergency and critical care medicine: a comprehensive review of applications, challenges, and future directions.Burns & trauma · 2026Review
- A spatiotemporal state-inference framework for adaptive immunotherapy in glioblastoma.Frontiers in oncology · 2026Review
- Quantifying surprise in clinical care: Detecting highly informative events in electronic health records with foundation models.Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing · 2026Article
- Diagnostic Codes in AI Prediction Models and Label Leakage of Same-Admission Clinical Outcomes.JAMA network open · 2025Article
- Combined Applications of Artificial Intelligence and Simulation for Healthcare Process Optimization: A Systematic Review.Healthcare (Basel, Switzerland) · 2025Review
- Artificial Intelligence for Cardiovascular Care in Action: From Learning to Implementation in Health Systems.JACC. Advances · 2025Review
- Large language models forecast patient health trajectories enabling digital twins.NPJ digital medicine · 2025Article
- Differential dementia detection from multimodal brain images in a real-world dataset.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2025Article
- Can laboratory test-based frailty indices contribute to frailty screening in emergency departments?Age and ageing · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
Abstract
Integrating modern machine learning and clinical decision-making has great promise for mitigating healthcare's increasing cost and complexity. We introduce the Enhanced Transformer for Health Outcome Simulation (ETHOS), a novel application of the transformer deep-learning architecture for analyzing high-dimensional, heterogeneous, and episodic health data. ETHOS is trained using Patient Health Timelines (PHTs)-detailed, tokenized records of health events-to predict future health trajectories, leveraging a zero-shot learning approach. ETHOS represents a significant advancement in foundation model development for healthcare analytics, eliminating the need for labeled data and model fine-tuning. Its ability to simulate various treatment pathways and consider patient-specific factors positions ETHOS as a tool for care optimization and addressing biases in healthcare delivery. Future developments will expand ETHOS' capabilities to incorporate a wider range of data types and data sources. Our work demonstrates a pathway toward accelerated AI development and deployment in healthcare.
Identifiers
What Socratic holds
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 Socratic graph.