[HTML][HTML] Individual risk prediction using data beyond the medical clinic
RM Califf, FE Harrell - CMAJ, 2018 - Can Med Assoc
E948 CMAJ| AUGUST 13, 2018| VOLUME 190| ISSUE 32 are universally available in North
America, and transactions are recorded in real time. As technology improves, curated …
America, and transactions are recorded in real time. As technology improves, curated …
Medical risk prediction models: with ties to machine learning
SE Lazic - 2022 - academic.oup.com
Written by two leaders in the field, this book provides a high-level overview of building and
validating medical risk prediction models. Unlike many machine learning (ML) or predictive …
validating medical risk prediction models. Unlike many machine learning (ML) or predictive …
Thoroughly modern risk prediction?
MJ Pencina, RB D'Agostino Sr - Science translational medicine, 2012 - science.org
Thoroughly Modern Risk Prediction? | Science Translational Medicine news careers commentary
Journals Science Science brought to you byGoogle Indexer Log in science science advances …
Journals Science Science brought to you byGoogle Indexer Log in science science advances …
Causal inference in medicine and in health policy: A summary
A data science task can be deemed as making sense of the data or testing a hypothesis
about it. The conclusions inferred from the data can greatly guide us in making informative …
about it. The conclusions inferred from the data can greatly guide us in making informative …
Avoiding “toxic knowledge”: the importance of framing personalized risk information in clinical decision-making
KM Kostick, JS Blumenthal-Barby - Personalized medicine, 2021 - Future Medicine
Every patient is constituted by a multitude of oftentimes hidden factors that predict their
mortality and morbidity across a range of scenarios. Identifying and making sense out of …
mortality and morbidity across a range of scenarios. Identifying and making sense out of …
[BOOK][B] Medical risk prediction models: with ties to machine learning
Medical Risk Prediction Models: With Ties to Machine Learning is a hands-on book for
clinicians, epidemiologists, and professional statisticians who need to make or evaluate a …
clinicians, epidemiologists, and professional statisticians who need to make or evaluate a …
Prediction under hypothetical interventions: evaluation of performance using longitudinal observational data
RH Keogh, N van Geloven - arXiv preprint arXiv:2304.10005, 2023 - arxiv.org
Prediction models provide risks of an adverse event occurring for an individual based on
their characteristics. Some prediction models have been used to make treatment decisions …
their characteristics. Some prediction models have been used to make treatment decisions …
Risk prediction: methods, challenges, and opportunities
The primary efforts of disease and epidemiological research can be divided into two areas:
identifying the causal mechanisms and utilizing important variables for risk prediction. The …
identifying the causal mechanisms and utilizing important variables for risk prediction. The …
Developing and implementing predictive models in a learning healthcare system: traditional and artificial intelligence approaches in the veterans health administration
D Atkins, CA Makridis, G Alterovitz… - Annual Review of …, 2022 - annualreviews.org
Predicting clinical risk is an important part of healthcare and can inform decisions about
treatments, preventive interventions, and provision of extra services. The field of predictive …
treatments, preventive interventions, and provision of extra services. The field of predictive …
Causal inference and counterfactual prediction in machine learning for actionable healthcare
Big data, high-performance computing, and (deep) machine learning are increasingly
becoming key to precision medicine—from identifying disease risks and taking preventive …
becoming key to precision medicine—from identifying disease risks and taking preventive …