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Search results (134)

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Assessing the Importance of Variation in Diagnostic Coding Among the Three Countries in the UK Biobank

Clifton L. et al, (2026), Learning Health Systems, 10

Beyond event-rate enrichment: proteomic risk scores for mechanism-aware prevention trial design

Fieggen J. et al, (2026)

Early plasma proteomic alterations precede amyloidosis diagnosis, reflecting cardiac and immune dysregulation

Hasheminasab SA. et al, (2026)

Development and Validation of a Multimodal AI-Based Model for Predicting Post-Prostatectomy Treatment Outcomes from Baseline Biparametric Prostate MRI.

Simon BD. et al, (2026)

Democratising clinical AI through dataset condensation for classical clinical models

Thakur A. et al, (2026)

Digital morphine: why AI scribes are symptomatic relief for a broken system

Segal B. et al, (2026), BMJ Digital Health & AI, 2, e000030 - e000030

When to and when not to use machine learning in risk prediction models

Clifton L. et al, (2026), Lancet Digital Health, 8

Cardiac health assessment across scenarios and devices using a multimodal foundation model pretrained on data from 1.7 million individuals

Gu X. et al, (2026), Nature Machine Intelligence, 8, 220 - 233

Clinical-R1: Empowering Large Language Models for Faithful and Comprehensive Reasoning with Clinical Objective Relative Policy Optimization

Gu B. et al, (2026), Proceedings of Machine Learning Research, 317

Associations between disease-specific polygenic risk scores and disease-specific causes of death in the UK Biobank cohort

Liu W. et al, (2025)

Multimorbidity, disease clusters and risk of all-cause and cause-specific mortality: a population-based prospective cohort study

Littlejohns TJ. et al, (2025), Scientific Reports, 15

Transforming label-efficient decoding of healthcare wearables with self-supervised learning and “embedded” medical domain expertise

Gu X. et al, (2025), Communications Engineering, 4

Sample Selection Bias in Machine Learning for Healthcare

Chauhan VK. et al, (2025), ACM Transactions on Computing for Healthcare, 6

A multimodal automated deep learning-based model for predicting biochemical recurrence of prostate cancer following prostatectomy from baseline MRI, Presurgical clinical covariates

Simon BD. et al, (2025), Clinical Imaging, 126

Foundation Models for Biosignals: A Survey

Gu X. et al, (2025)

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