ArticleNature biomedical engineering2022
A cost-aware framework for the development of AI models for healthcare applications.
Article in Nature biomedical engineering, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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
16 citing papers in PubMed.
- Using Theory-Based Frameworks to Identify Barriers, Facilitators, Expectations, and Willingness to Pay for Online Medical Consultation Services in China: Qualitative Study.JMIR human factors · 2026Article
- MAP: evaluation and multi-agent enhancement of large language models for inpatient pathways.npj health systems · 2026Article
- Clinician-deployable deep hypergraph model integrating clinical and CT radiomics predicts immunotherapy outcomes in NSCLC.PLOS digital health · 2026Article
- Development and Real-World Evaluation of an Interpretable Machine Learning Framework for Predicting COPD-Related Healthcare Costs.International journal of chronic obstructive pulmonary disease · 2026Article
- The Role of Artificial Intelligence in Modern Medicine: Clinical Applications, Economic Implications, and Ethical Considerations.Current pharmaceutical design · 2026Review
- Integration of Artificial Intelligence and Wearable Devices in Pediatric Clinical Care: A Review.Bioengineering (Basel, Switzerland) · 2025Review
- The application of machine learning models in a resource-constrained environment.Irish journal of medical science · 2025Article
- An Interdisciplinary Overview on Ambient Assisted Living Systems for Health Monitoring at Home: Trade-Offs and Challenges.Sensors (Basel, Switzerland) · 2025Review
- Artificial intelligence in personalized rehabilitation: current applications and a SWOT analysis.Frontiers in digital health · 2025Review
- AI with agency: a vision for adaptive, efficient, and ethical healthcare.Frontiers in digital health · 2025Article
- Designing nanotheranostics with machine learning.Nature nanotechnology · 2024Review
- Explainable machine learning model for pre-frailty risk assessment in community-dwelling older adults.Health care science · 2024Article
- From Basic to Extra Features: Hypergraph Transformer Pretrain-then-Finetuning for Balanced Clinical Predictions on EHR.Proceedings of machine learning research · 2024Article
- Construction and optimization of multi-platform precision pathways for precision medicine.Scientific reports · 2024Article
- Leveraging physiology and artificial intelligence to deliver advancements in health care.Physiological reviews · 2023Review
- Optimizing the Implementation of Clinical Predictive Models to Minimize National Costs: Sepsis Case Study.Journal of medical Internet research · 2023Article
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
Accurate artificial intelligence (AI) for disease diagnosis could lower healthcare workloads. However, when time or financial resources for gathering input data are limited, as in emergency and critical-care medicine, developing accurate AI models, which typically require inputs for many clinical variables, may be impractical. Here we report a model-agnostic cost-aware AI (CoAI) framework for the development of predictive models that optimize the trade-off between prediction performance and feature cost. By using three datasets, each including thousands of patients, we show that relative to clinical risk scores, CoAI substantially reduces the cost and improves the accuracy of predicting acute traumatic coagulopathy in a pre-hospital setting, mortality in intensive-care patients and mortality in outpatient settings. We also show that CoAI outperforms state-of-the-art cost-aware prediction strategies in terms of predictive performance, model cost, training time and robustness to feature-cost perturbations. CoAI uses axiomatic feature-attribution methods for the estimation of feature importance and decouples feature selection from model training, thus allowing for a faster and more flexible adaptation of AI models to new feature costs and prediction budgets.
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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.