Harnessing multimodal data integration to advance precision oncology
Advances in quantitative biomarker development have accelerated new forms of data-driven
insights for patients with cancer. However, most approaches are limited to a single mode of …
insights for patients with cancer. However, most approaches are limited to a single mode of …
[HTML][HTML] Automated machine learning: Review of the state-of-the-art and opportunities for healthcare
J Waring, C Lindvall, R Umeton - Artificial intelligence in medicine, 2020 - Elsevier
Objective This work aims to provide a review of the existing literature in the field of
automated machine learning (AutoML) to help healthcare professionals better utilize …
automated machine learning (AutoML) to help healthcare professionals better utilize …
The National COVID Cohort Collaborative (N3C): rationale, design, infrastructure, and deployment
Abstract Objective Coronavirus disease 2019 (COVID-19) poses societal challenges that
require expeditious data and knowledge sharing. Though organizational clinical data are …
require expeditious data and knowledge sharing. Though organizational clinical data are …
[HTML][HTML] Key challenges for delivering clinical impact with artificial intelligence
Background Artificial intelligence (AI) research in healthcare is accelerating rapidly, with
potential applications being demonstrated across various domains of medicine. However …
potential applications being demonstrated across various domains of medicine. However …
Artificial intelligence with multi-functional machine learning platform development for better healthcare and precision medicine
Precision medicine is one of the recent and powerful developments in medical care, which
has the potential to improve the traditional symptom-driven practice of medicine, allowing …
has the potential to improve the traditional symptom-driven practice of medicine, allowing …
Machine learning in medicine
Machine Learning in Medicine In this view of the future of medicine, patient–provider
interactions are informed and supported by massive amounts of data from interactions with …
interactions are informed and supported by massive amounts of data from interactions with …
Graph embedding on biomedical networks: methods, applications and evaluations
Motivation Graph embedding learning that aims to automatically learn low-dimensional
node representations, has drawn increasing attention in recent years. To date, most recent …
node representations, has drawn increasing attention in recent years. To date, most recent …
[HTML][HTML] Opportunities and challenges in developing risk prediction models with electronic health records data: a systematic review
Objective: Electronic health records (EHRs) are an increasingly common data source for
clinical risk prediction, presenting both unique analytic opportunities and challenges. We …
clinical risk prediction, presenting both unique analytic opportunities and challenges. We …
Artificial intelligence and suicide prevention: a systematic review of machine learning investigations
Suicide is a leading cause of death that defies prediction and challenges prevention efforts
worldwide. Artificial intelligence (AI) and machine learning (ML) have emerged as a means …
worldwide. Artificial intelligence (AI) and machine learning (ML) have emerged as a means …
[HTML][HTML] Electronic health records to facilitate clinical research
MR Cowie, JI Blomster, LH Curtis, S Duclaux… - Clinical Research in …, 2017 - Springer
Electronic health records (EHRs) provide opportunities to enhance patient care, embed
performance measures in clinical practice, and facilitate clinical research. Concerns have …
performance measures in clinical practice, and facilitate clinical research. Concerns have …