Fetching the paper…
Reading the bibliography…
Digital health tools have the potential to significantly improve the delivery of healthcare services.
Verification of forecasts expressed in terms of probability
Brier, G. W · 1950
Earlier work this paper cites.
Nonparametric estimation from incomplete observations
Kaplan, E. L. & Meier, P · 1958
Earlier work this paper cites.
Validation of clinical classification schemes for predicting strokeresults from the national registry of atrial fibrillation
Gage, B. F. et al · 2001
Earlier work this paper cites.
Decision curve analysis: A novel method for evaluating prediction models
Vickers, A. J. & Elkin, E. B · 2006
Earlier work this paper cites.
Barriers to apply cardiovascular prediction rules in primary care: A postal survey
Eichler, K., Zoller, M., Tschudi, P. & Steurer, J · 2007
Earlier work this paper cites.
General cardiovascular risk profile for use in primary care
D’Agostino, R. B. et al · 2008
Earlier work this paper cites.
Decision analysis for the evaluation of diagnostic tests, prediction models, and molecular markers
Vickers, A. J · 2008
Earlier work this paper cites.
Prognosis and prognostic research: what, why, and how?
Moons, K. G., Royston, P., Vergouwe, Y., Grobbee, D. E. & Altman, D. G · 2009
Earlier work this paper cites.
Apolipoprotein(a) isoforms and the risk of vascular disease: Systematic review of 40 studies involving 58,000 participants
Erqou, S. et al · 2009
Earlier work this paper cites.
Barriers to Routine Risk-Score Use for Healthy Primary Care Patients: Survey and Qualitative Study
Müller-Riemenschneider, F. et al · 2010
Earlier work this paper cites.
2010 ACCF/AHA guideline for assessment of cardiovascular risk in asymptomatic adults: A report of the American College of Cardiology Foundation/American Heart Association Task Force on practice guidelines
Greenland, P. et al · 2010
Earlier work this paper cites.
On the C-statistics for evaluating overall adequacy of risk prediction procedures with censored survival data
Uno, H., Cai, T., Pencina, M. J., D’Agostino, R. B. & Wei, L. J · 2011
Earlier work this paper cites.
mice: Multivariate imputation by chained equations in R
van Buuren, S. & Groothuis-Oudshoorn, K · 2011
Earlier work this paper cites.
Development and validation of a continuous measure of patient condition using the electronic medical record
Rothman, M. J., Rothman, S. I. & Beals, J · 2013
Earlier work this paper cites.
UK Biobank: An open access resource for identifying the causes of a wide range of complex diseases of middle and old age
Sudlow, C. et al · 2015
Earlier work this paper cites.
Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD): Explanation and elaboration
Moons, K. G. et al · 2015
Earlier work this paper cites.
Relationship between clerical burden and characteristics of the electronic environment with physician burnout and professional satisfaction
Shanafelt, T. D. et al · 2016
Earlier work this paper cites.
Development and validation of QRISK3 risk prediction algorithms to estimate future risk of cardiovascular disease: Prospective cohort study
Hippisley-Cox, J., Coupland, C. & Brindle, P · 2017
Earlier work this paper cites.
A unified approach to interpreting model predictions
Lundberg, S. M. & Lee, S.-I · 2017
Earlier work this paper cites.
Wearables and the medical revolution
Dunn, J., Runge, R. & Snyder, M · 2018
Earlier work this paper cites.
Electronic Health Record Usability Issues and Potential Contribution to Patient Harm
Howe, J. L., Adams, K. T., Hettinger, A. Z. & Ratwani, R. M · 2018
Earlier work this paper cites.
Multiple imputation
Rubin, D. B · 2018
Earlier work this paper cites.
Digital health: A path to validation
Mathews, S. C. et al · 2019
Cited alongside, same era.
A Decade of Health Information Technology Usability Challenges and the Path Forward
Ratwani, R. M., Reider, J. & Singh, H · 2019
Cited alongside, same era.
Physician stress and burnout: The impact of health information technology
Gardner, R. L. et al · 2019
Cited alongside, same era.
The potential for artificial intelligence in healthcare
Davenport, T. & Kalakota, R · 2019
Cited alongside, same era.
Key challenges for delivering clinical impact with artificial intelligence
Kelly, C. J., Karthikesalingam, A., Suleyman, M., Corrado, G. & King, D · 2019
Cited alongside, same era.
Proposed regulatory framework for modifications to artificial intelligence/machine learning (AI/ML)-based software as a medical device (SaMD) (2019)
BioGPT: Generative pre-trained transformer for biomedical text generation and mining
Luo, R. et al · 2022
Later among the works it cites.
A large language model for electronic health records
Yang, X. et al · 2022
Later among the works it cites.
Rethinking explainability as a dialogue: A practitioner’s perspective
Lakkaraju, H., Slack, D., Chen, Y., Tan, C. & Singh, S · 2022
Later among the works it cites.
The global burden of cardiovascular diseases and risk
Muthiah, V., A., M. G., Varieur, T. J., Valentin, F. & A., R. G · 2022
Later among the works it cites.
Independent external validation of the QRISK3 cardiovascular disease risk prediction model using UK Biobank
Parsons, R. E. et al · 2022
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Food and Drug Administration and others · 2019
Cited alongside, same era.
Cardiovascular disease risk prediction using automated machine learning: A prospective study of 423,604 UK Biobank participants
Alaa, A. M., Bolton, T., Di Angelantonio, E., Rudd, J. H. F. & van der Schaar, M · 2019
Cited alongside, same era.
An overview of clinical decision support systems: benefits, risks, and strategies for success
Sutton, R. T. et al · 2020
Cited alongside, same era.
Artificial intelligence and human trust in healthcare: focus on clinicians
Asan, O., Bayrak, A. E. & Choudhury, A · 2020
Cited alongside, same era.
On faithfulness and factuality in abstractive summarization
Maynez, J., Narayan, S., Bohnet, B. & McDonald, R · 2020
Cited alongside, same era.
Artificial intelligence in healthcare: Transforming the practice of medicine
Bajwa, J., Munir, U., Nori, A. & Williams, B · 2021
Cited alongside, same era.
Transparency of machine-learning in healthcare: The GDPR & European health law
Mourby, M., Ó Cathaoir, K. & Collin, C. B · 2021
Cited alongside, same era.
Taylor, R. et al · 2022
Later among the works it cites.
AutoPrognosis 2.0: Democratizing diagnostic and prognostic modeling in healthcare with automated machine learning
Imrie, F., Cebere, B., McKinney, E. F. & van der Schaar, M · 2023
Closest in time.
Foundation models for generalist medical artificial intelligence
Moor, M. et al · 2023
Closest in time.
Large language models encode clinical knowledge
Singhal, K. et al · 2023
Closest in time.
Revolutionizing radiology with GPT-based models: Current applications, future possibilities and limitations of ChatGPT
Lecler, A., Duron, L. & Soyer, P · 2023
Closest in time.
Survey of hallucination in natural language generation
Ji, Z. et al · 2023
Closest in time.
Toolformer: Language models can teach themselves to use tools
Schick, T. et al · 2023
Closest in time.
Health system-scale language models are all-purpose prediction engines
Jiang, L. Y. et al · 2023
Closest in time.
OpenAI · 2023
Closest in time.
Multiple stakeholders drive diverse interpretability requirements for machine learning in healthcare
Imrie, F., Davis, R. & van der Schaar, M · 2023
Closest in time.
Cardiovascular disease: risk assessment and reduction, including lipid modification (2014)
National Institute for Health and Care Excellence · 2023
Closest in time.
The user experience of ChatGPT: Findings from a questionnaire study of early users
Skjuve, M., Følstad, A. & Brandtzaeg, P. B · 2023
Closest in time.
Assessing eligibility for lung cancer screening using parsimonious ensemble machine learning models: A development and validation study
Callender, T. et al · 2023
Closest in time.
ReAct: Synergizing reasoning and acting in language models
Yao, S. et al · 2023
Closest in time.
Langchain
Chase, H · 2023
Closest in time.
A survey on in-context learning
Dong, Q. et al · 2023
Closest in time.