Results 11 to 20 of about 15,843,136 (243)

Human-AI co-design for clinical prediction models [PDF]

npj Digital Medicine
Developing safe, effective, and practically useful clinical prediction models (CPMs) traditionally requires extensive collaboration between clinical experts, data scientists, and informaticists to refine the many small but critical details of the model ...
Jean Feng, Avni Kothari, Patrick Vossler, Andrew Bishara, Lucas Zier, Newton Addo, Aaron Kornblith, Yan Shuo Tan, Chandan Singh   +8 more
doaj   +2 more sources

Reducing overconfident errors in clinical prediction models [PDF]

BMC Medical Informatics and Decision Making
Machine Learning (ML) models are increasingly being used in clinical workflows. Evaluation of these models tends to focus on global performance metrics, which can obscure error patterns and lead to bias in clinical decision making.
Henry Bayly, Yorghos Tripodis, Steven Lenio, Prasad Patil, for the Alzheimer’s Disease Neuroimaging Initiative   +4 more
doaj   +2 more sources

Research on machine learning-based clinical prediction models: a bibliometric analysis [PDF]

Frontiers in Oncology
BackgroundMachine learning (ML) has emerged as a transformative approach for developing high-performance clinical prediction models (CPMs). By leveraging multidimensional patient data, ML enables more accurate disease risk stratification, prognostic ...
Qinshan Li, Qinshan Li, Mingli Zeng, Mingli Zeng, Danmei Liang, Xuemei Zheng, Xiaoxia Zhang, Xiaoxia Zhang, Ruben Martin-Payo, Ruben Martin-Payo   +9 more
doaj   +2 more sources

Generative models and synthetic data in clinical prediction models: Promoting consistency, reproducibility, and transparency [PDF]

Journal of Clinical and Translational Science
Introduction: Reproducibility, consistency, and transparency are essential to responsible and ethical scientific inquiry, though practices supporting these qualities are often neglected. However, in many cases data are confidential or otherwise unable to
Anthony A. Mangino, Taha Ahmed, Vincent L. Sorrell   +2 more
doaj   +2 more sources

CPLLM: Clinical prediction with large language models. [PDF]

PLOS Digital Health
We present Clinical Prediction with Large Language Models (CPLLM), a method that involves fine-tuning a pre-trained Large Language Model (LLM) for predicting clinical disease and readmission. We utilized quantization and fine-tuned the LLM using prompts.
Ofir Ben Shoham, Nadav Rappoport
doaj   +3 more sources

Navigating fairness aspects of clinical prediction models [PDF]

BMC Medicine
Background Algorithms are increasingly used in healthcare, yet most algorithms lack thorough evaluation and impact assessment across diverse populations.
Kaustubh Chakradeo, Inchuen Huynh, Sedrah B. Balaganeshan, Ole L. Dollerup, Hjørdis Gade-Jørgensen, Susanne K. Laupstad, Mikkel Malham, Tri-Long Nguyen, Adam Hulman, Tibor V. Varga   +9 more
doaj   +2 more sources

Will large language models transform clinical prediction?

Diagnostic and Prognostic Research
Objective Large language models (LLMs) are attracting increasing interest in healthcare. This commentary evaluates the potential of LLMs to improve clinical prediction models (CPMs) for diagnostic and prognostic tasks, with a focus on their ability to ...
Yusuf Yildiz, Goran Nenadic, Meghna Jani, David A. Jenkins   +3 more
doaj   +4 more sources

Practical guide to building machine learning-based clinical prediction models using imbalanced datasets

Trauma Surgery & Acute Care Open
Clinical prediction models often aim to predict rare, high-risk events, but building such models requires robust understanding of imbalance datasets and their unique study design considerations.
Joseph D Forrester, Jeff Choi, Advait Patil, Jacklyn Luu, Evgenia Borisenko, Valerie Przekop   +5 more
doaj   +2 more sources

Clinical prediction models: diagnosis versus prognosis. [PDF]

, 2021
Clinical prediction models play an increasingly important role in contemporary clinical care, by informing healthcare professionals, patients and their relatives about outcome risks, with the aim to facilitate (shared) medical decision making and improve
van Smeden, Maarten, Collins, G.S.; id_orcid, van Smeden, M., Riley, R.D.; id_orcid, Moons, K.G., Reitsma, J.B., Riley, Richard D., Collins, Gary S., Moons, Karel GM, Reitsma, Johannes B., Epi Methoden, Epi Methoden Team 3, Epi Methoden Team 4, Epi Methoden Team 1, Infection & Immunity, Circulatory Health, JC onderzoeksprogramma Methodology, Cancer, Johannes B Reitsma, Gary S Collins, Karel GM Moons, Richard D Riley, Maarten van Smeden   +22 more
core   +2 more sources

Calculating the sample size required for developing a clinical prediction model. [PDF]

, 2020
Clinical prediction models aim to predict outcomes in individuals, to inform diagnosis or prognosis in healthcare. Hundreds of prediction models are published in the medical literature each year, yet many are developed using a dataset that is too small ...

core   +1 more source