Machine Learning Identifies Digital Phenotyping Measures Most Relevant to Negative Symptoms in Psychotic Disorders: Implications for Clinical Trials.

Sayli M NarkhedeLauren LutherIan M RaughAnna R KnippenbergFarnaz Zamani EsfahlaniHiroki Sayama Alex S CohenBrian KirkpatrickGregory P Strauss

Published in: Schizophrenia bulletin (2022)

These findings suggest that negative symptoms can be modeled from digital phenotyping data recorded in situ. Implications for selecting the most appropriate digital phenotyping variables for use as outcome measures in clinical trials targeting negative symptoms are discussed.

Keyphrases

clinical trial
high throughput
machine learning
sleep quality
big data
bipolar disorder
randomized controlled trial
phase ii
gene expression
deep learning
study protocol
depressive symptoms
phase iii