A machine learning model slightly outperforms a conventional regression model at predicting which children hospitalized for asthma will be readmitted within 180 days.
n this study, 773 untreated breast cancer patients from all over China were collected and followed up for at least 5 years. We obtained clinical data from 773 cases, RNA sequencing data from 752 cases ...
A new machine learning tool called T1GRS accurately predicts the genetic risk of developing Type 1 diabetes in both children ...
Retrospective validation of a novel multimodal AI prognostic tool integrating digital pathology and clinical data against real world data and Oncotype DX in a Swiss breast cancer cohort. This is an ...
By analyzing CpG-based DNA methylation, the model achieved 87% accuracy when applied to 31 cases representing 17 different cancer types.
Discover how machine learning asthma prediction can identify high-risk children early and support personalised care ...
Open-Source Hybrid Large Language Model Integrated System for Extraction of Breast Cancer Treatment Pathway From Free-Text Clinical Notes The diagnosis of chronic lymphocytic leukemia (CLL) is often ...
A recent study, “Picking Winners in Factorland: A Machine Learning Approach to Predicting Factor Returns,” set out to answer a critical question: Can machine learning techniques improve the prediction ...
Predicting observable traits from genetic variation remains difficult due to the complex interplay of multiple genes and environmental influences. Widely used statistical approaches are limited in ...
More aggressive feature scaling and increasingly complex transistor structures are driving a steady increase in process complexity, increasing the risk that a specified pattern may not be ...
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