Machine learning methods to predict outcomes of pharmacological treatment in psychosis
In recent years, machine learning (ML) has been a promising approach in the research of
treatment outcome prediction in psychosis. In this study, we reviewed ML studies using …
treatment outcome prediction in psychosis. In this study, we reviewed ML studies using …
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 …
Improving mental health services: A 50-year journey from randomized experiments to artificial intelligence and precision mental health
L Bickman - Administration and Policy in Mental Health and Mental …, 2020 - Springer
This conceptual paper describes the current state of mental health services, identifies critical
problems, and suggests how to solve them. I focus on the potential contributions of artificial …
problems, and suggests how to solve them. I focus on the potential contributions of artificial …
Ensembling classical machine learning and deep learning approaches for morbidity identification from clinical notes
The past decade has seen an explosion of the amount of digital information generated
within the healthcare domain. Digital data exist in the form of images, video, speech …
within the healthcare domain. Digital data exist in the form of images, video, speech …
Role of artificial intelligence in pharmacy practice: a narrative review
A Wong, E Wentz, N Palisano, M Dirani… - Journal of the …, 2023 - Wiley Online Library
Artificial intelligence (AI) has emerged as a potentially useful tool in transforming healthcare.
Roles for AI include drug safety, operations, as well as precision medicine to improve patient …
Roles for AI include drug safety, operations, as well as precision medicine to improve patient …
Machine learning-based ABA treatment recommendation and personalization for autism spectrum disorder: an exploratory study
Autism spectrum is a brain development condition that impairs an individual's capacity to
communicate socially and manifests through strict routines and obsessive–compulsive …
communicate socially and manifests through strict routines and obsessive–compulsive …
[HTML][HTML] Identifying clinical clusters with distinct trajectories in first-episode psychosis through an unsupervised machine learning technique
S Amoretti, N Verdolini, G Mezquida… - European …, 2021 - Elsevier
The extreme variability in symptom presentation reveals that individuals diagnosed with a
first-episode psychosis (FEP) may encompass different sub-populations with potentially …
first-episode psychosis (FEP) may encompass different sub-populations with potentially …
Using administrative data to predict suicide after psychiatric hospitalization in the veterans health administration system
There is a very high suicide rate in the year after psychiatric hospital discharge. Intensive
postdischarge case management programs can address this problem but are not cost …
postdischarge case management programs can address this problem but are not cost …
Optimally choosing medication type for patients with opioid use disorder
Patients with opioid use disorder (OUD) tend to get assigned to one of 3 medications based
on the treatment program to which the patient presents (eg, opioid treatment programs tend …
on the treatment program to which the patient presents (eg, opioid treatment programs tend …
[HTML][HTML] Assessing treatment switch among patients with multiple sclerosis: A machine learning approach
Background Patients with multiple sclerosis (MS) frequently switch their Disease-Modifying
Agents (DMA) for effectiveness and safety concerns. This study aimed to develop and …
Agents (DMA) for effectiveness and safety concerns. This study aimed to develop and …