Multimodal llms for health grounded in individual-specific data
Foundation large language models (LLMs) have shown an impressive ability to solve tasks
across a wide range of fields including health. To effectively solve personalized health tasks …
across a wide range of fields including health. To effectively solve personalized health tasks …
Deep learning with noisy labels in medical prediction problems: a scoping review
Objectives Medical research faces substantial challenges from noisy labels attributed to
factors like inter-expert variability and machine-extracted labels. Despite this, the adoption of …
factors like inter-expert variability and machine-extracted labels. Despite this, the adoption of …
Advancing multimodal medical capabilities of Gemini
Many clinical tasks require an understanding of specialized data, such as medical images
and genomics, which is not typically found in general-purpose large multimodal models …
and genomics, which is not typically found in general-purpose large multimodal models …
Synthetic surrogates improve power for genome-wide association studies of partially missing phenotypes in population biobanks
Within population biobanks, incomplete measurement of certain traits limits the power for
genetic discovery. Machine learning is increasingly used to impute the missing values from …
genetic discovery. Machine learning is increasingly used to impute the missing values from …
A review of disease risk prediction methods and applications in the omics era
C Sun, X Cheng, J Xu, H Chen, J Tao, Y Dong… - …, 2024 - Wiley Online Library
Risk prediction and disease prevention are the innovative care challenges of the 21st
century. Apart from freeing the individual from the pain of disease, it will lead to low medical …
century. Apart from freeing the individual from the pain of disease, it will lead to low medical …
Unsupervised representation learning improves genomic discovery for lung function and respiratory disease prediction
Background: High-dimensional clinical data are becoming more accessible in biobank-scale
datasets. However, accurately phenotyping high-dimensional clinical data remains a major …
datasets. However, accurately phenotyping high-dimensional clinical data remains a major …
[HTML][HTML] Deep learning utilizing suboptimal spirometry data to improve lung function and mortality prediction in the UK Biobank
Background: Spirometry measures lung function by selecting the best of multiple efforts
meeting pre-specified quality control (QC), and reporting two key metrics: forced expiratory …
meeting pre-specified quality control (QC), and reporting two key metrics: forced expiratory …
[HTML][HTML] Unsupervised representation learning improves genomic discovery and risk prediction for respiratory and circulatory functions and diseases
High-dimensional clinical data are becoming more accessible in biobank-scale datasets.
However, effectively utilizing high-dimensional clinical data for genetic discovery remains …
However, effectively utilizing high-dimensional clinical data for genetic discovery remains …
[HTML][HTML] Genome-wide association study identifies novel susceptible loci and evaluation of polygenic risk score for chronic obstructive pulmonary disease in a …
Background Chronic Obstructive Pulmonary Disease (COPD) describes a group of
progressive lung diseases causing breathing difficulties. While COPD development typically …
progressive lung diseases causing breathing difficulties. While COPD development typically …
Unraveling COVID-19 relationship with anxiety disorders and symptoms using genome-wide data
Background There is still a limited understanding of the dynamics contributing to the
comorbidity of COVID-19 and anxiety outcomes. Methods To dissect the pleiotropic …
comorbidity of COVID-19 and anxiety outcomes. Methods To dissect the pleiotropic …