[HTML][HTML] Deep learning and machine learning in psychiatry: a survey of current progress in depression detection, diagnosis and treatment
Informatics paradigms for brain and mental health research have seen significant advances
in recent years. These developments can largely be attributed to the emergence of new …
in recent years. These developments can largely be attributed to the emergence of new …
Beyond playing 20 questions with nature: Integrative experiment design in the social and behavioral sciences
The dominant paradigm of experiments in the social and behavioral sciences views an
experiment as a test of a theory, where the theory is assumed to generalize beyond the …
experiment as a test of a theory, where the theory is assumed to generalize beyond the …
[HTML][HTML] Digital phenotyping in depression diagnostics: Integrating psychiatric and engineering perspectives
J Kamath, RL Barriera, N Jain, E Keisari… - World Journal of …, 2022 - ncbi.nlm.nih.gov
Depression is a serious medical condition and is a leading cause of disability worldwide.
Current depression diagnostics and assessment has significant limitations due to …
Current depression diagnostics and assessment has significant limitations due to …
[HTML][HTML] Medical AI and human dignity: Contrasting perceptions of human and artificially intelligent (AI) decision making in diagnostic and medical resource allocation …
Abstract Forms of Artificial Intelligence (AI) are already being deployed into clinical settings
and research into its future healthcare uses is accelerating. Despite this trajectory, more …
and research into its future healthcare uses is accelerating. Despite this trajectory, more …
[HTML][HTML] Novel cuckoo search-based metaheuristic approach for deep learning prediction of depression
Depression is a common illness worldwide with doubtless severe implications. Due to the
absence of early identification and treatment for depression, millions of individuals …
absence of early identification and treatment for depression, millions of individuals …
[HTML][HTML] Prediction of generalized anxiety levels during the Covid-19 pandemic: A machine learning-based modeling approach
FM Albagmi, A Alansari, DS Al Shawan… - Informatics in Medicine …, 2022 - Elsevier
The rapid spread of the Covid-19 outbreak led many countries to enforce precautionary
measures such as complete lockdowns. These lifestyle-altering measures caused a …
measures such as complete lockdowns. These lifestyle-altering measures caused a …
Deep learning paired with wearable passive sensing data predicts deterioration in anxiety disorder symptoms across 17–18 years
NC Jacobson, D Lekkas, R Huang… - Journal of affective …, 2021 - Elsevier
Background Recent studies have demonstrated that passive smartphone and wearable
sensor data collected throughout daily life can predict anxiety symptoms cross-sectionally …
sensor data collected throughout daily life can predict anxiety symptoms cross-sectionally …
[HTML][HTML] Continuous action deep reinforcement learning for propofol dosing during general anesthesia
G Schamberg, M Badgeley, B Meschede-Krasa… - Artificial Intelligence in …, 2022 - Elsevier
Purpose Anesthesiologists simultaneously manage several aspects of patient care during
general anesthesia. Automating administration of hypnotic agents could enable more …
general anesthesia. Automating administration of hypnotic agents could enable more …
[HTML][HTML] Predicting acute suicidal ideation on Instagram using ensemble machine learning models
Introduction Online social networking data (SN) is a contextually and temporally rich data
stream that has shown promise in the prediction of suicidal thought and behavior. Despite …
stream that has shown promise in the prediction of suicidal thought and behavior. Despite …
[HTML][HTML] Machine learning-based behavioral diagnostic tools for depression: advances, challenges, and future directions
The psychiatric diagnostic procedure is currently based on self-reports that are subject to
personal biases. Therefore, the diagnostic process would benefit greatly from data-driven …
personal biases. Therefore, the diagnostic process would benefit greatly from data-driven …