Integrating machine learning and multiscale modeling—perspectives, challenges, and opportunities in the biological, biomedical, and behavioral sciences
Fueled by breakthrough technology developments, the biological, biomedical, and
behavioral sciences are now collecting more data than ever before. There is a critical need …
behavioral sciences are now collecting more data than ever before. There is a critical need …
[HTML][HTML] Machine learning in healthcare
H Habehh, S Gohel - Current genomics, 2021 - ncbi.nlm.nih.gov
Abstract Recent advancements in Artificial Intelligence (AI) and Machine Learning (ML)
technology have brought on substantial strides in predicting and identifying health …
technology have brought on substantial strides in predicting and identifying health …
The promise of machine learning in predicting treatment outcomes in psychiatry
For many years, psychiatrists have tried to understand factors involved in response to
medications or psychotherapies, in order to personalize their treatment choices. There is …
medications or psychotherapies, in order to personalize their treatment choices. There is …
Causal machine learning for healthcare and precision medicine
Causal machine learning (CML) has experienced increasing popularity in healthcare.
Beyond the inherent capabilities of adding domain knowledge into learning systems, CML …
Beyond the inherent capabilities of adding domain knowledge into learning systems, CML …
Multiscale modeling meets machine learning: What can we learn?
GCY Peng, M Alber, A Buganza Tepole… - … Methods in Engineering, 2021 - Springer
Abstract Machine learning is increasingly recognized as a promising technology in the
biological, biomedical, and behavioral sciences. There can be no argument that this …
biological, biomedical, and behavioral sciences. There can be no argument that this …
From real‐world patient data to individualized treatment effects using machine learning: current and future methods to address underlying challenges
Clinical decision making needs to be supported by evidence that treatments are beneficial to
individual patients. Although randomized control trials (RCTs) are the gold standard for …
individual patients. Although randomized control trials (RCTs) are the gold standard for …
Artificial intelligence for precision medicine in neurodevelopmental disorders
The ambition of precision medicine is to design and optimize the pathway for diagnosis,
therapeutic intervention, and prognosis by using large multidimensional biological datasets …
therapeutic intervention, and prognosis by using large multidimensional biological datasets …
The state of our understanding of the pathophysiology and optimal treatment of depression: glass half full or half empty?
CB Nemeroff - American Journal of Psychiatry, 2020 - Am Psychiatric Assoc
Major depressive disorder is a remarkably common and often severe psychiatric disorder
associated with high levels of morbidity and mortality. Patients with major depression are …
associated with high levels of morbidity and mortality. Patients with major depression are …
[HTML][HTML] Supervised machine learning in drug discovery and development: Algorithms, applications, challenges, and prospects
Drug discovery and development is a time-consuming process that involves identifying,
designing, and testing new drugs to address critical medical needs. In recent years, machine …
designing, and testing new drugs to address critical medical needs. In recent years, machine …
Machine learning in the prediction of depression treatment outcomes: a systematic review and meta-analysis
Background Multiple treatments are effective for major depressive disorder (MDD), but the
outcomes of each treatment vary broadly among individuals. Accurate prediction of …
outcomes of each treatment vary broadly among individuals. Accurate prediction of …