Real-world data: a brief review of the methods, applications, challenges and opportunities
F Liu, D Panagiotakos - BMC Medical Research Methodology, 2022 - Springer
Background The increased adoption of the internet, social media, wearable devices, e-
health services, and other technology-driven services in medicine and healthcare has led to …
health services, and other technology-driven services in medicine and healthcare has led to …
Multimodal machine learning in precision health: A scoping review
Abstract Machine learning is frequently being leveraged to tackle problems in the health
sector including utilization for clinical decision-support. Its use has historically been focused …
sector including utilization for clinical decision-support. Its use has historically been focused …
[HTML][HTML] A review of challenges and opportunities in machine learning for health
Modern electronic health records (EHRs) provide data to answer clinically meaningful
questions. The growing data in EHRs makes healthcare ripe for the use of machine learning …
questions. The growing data in EHRs makes healthcare ripe for the use of machine learning …
[PDF][PDF] Multi-label classification of patient notes: case study on ICD code assignment
The automatic coding of clinical documentation according to diagnosis codes is a useful task
in the Electronic Health Record, but a challenging one due to the large number of codes and …
in the Electronic Health Record, but a challenging one due to the large number of codes and …
[HTML][HTML] Unsupervised machine learning for the discovery of latent disease clusters and patient subgroups using electronic health records
Abstract Machine learning has become ubiquitous and a key technology on mining
electronic health records (EHRs) for facilitating clinical research and practice. Unsupervised …
electronic health records (EHRs) for facilitating clinical research and practice. Unsupervised …
Probabilistic machine learning for healthcare
Machine learning can be used to make sense of healthcare data. Probabilistic machine
learning models help provide a complete picture of observed data in healthcare. In this …
learning models help provide a complete picture of observed data in healthcare. In this …
Evaluation of clustering and topic modeling methods over health-related tweets and emails
Background Internet provides different tools for communicating with patients, such as social
media (eg, Twitter) and email platforms. These platforms provided new data sources to shed …
media (eg, Twitter) and email platforms. These platforms provided new data sources to shed …
Using clinical notes and natural language processing for automated HIV risk assessment
Objective: Universal HIV screening programs are costly, labor intensive, and often fail to
identify high-risk individuals. Automated risk assessment methods that leverage longitudinal …
identify high-risk individuals. Automated risk assessment methods that leverage longitudinal …
Inferring multimodal latent topics from electronic health records
Electronic health records (EHR) are rich heterogeneous collections of patient health
information, whose broad adoption provides clinicians and researchers unprecedented …
information, whose broad adoption provides clinicians and researchers unprecedented …
Learning tasks for multitask learning: Heterogenous patient populations in the icu
Machine learning approaches have been effective in predicting adverse outcomes in
different clinical settings. These models are often developed and evaluated on datasets with …
different clinical settings. These models are often developed and evaluated on datasets with …