ArticleSchizophrenia bulletin2022
Machine Learning Identifies Digital Phenotyping Measures Most Relevant to Negative Symptoms in Psychotic Disorders: Implications for Clinical Trials.
Article in Schizophrenia bulletin, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 2 of them syntheses that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
19 citing papers in PubMed, 2 syntheses or guidelines pooled it, 35 citations in OpenAlex.
- Digital phenotyping for mental health conditions: a systematic review of implementation and application.Frontiers in digital health · 2026Pooled it
- Geolocation-derived mobility indices and the association with clinical symptoms and functioning in severe mental illness: A multivariate meta-analysis.Journal of psychopathology and clinical science · 2025Pooled it
- Adherence and Acceptability of Smartphone- and Wearable-Based Digital Phenotyping in Patients With First-Episode Psychosis: Observance et acceptabilité du phénotypage numérique fondé sur les téléphones intelligents et les objets personnels connectés chez les patients présentant un premier épisode de psychose.Canadian journal of psychiatry. Revue canadienne de psychiatrie · 2026Article
- Vocal Markers of Schizophrenia: Assessing the Generalizability of Machine Learning Models and Their Clinical Applicability.Schizophrenia bulletin · 2026Article
- Application of artificial intelligence in schizophrenia rehabilitation management: a systematic scoping review.Translational psychiatry · 2026Article
- Subjective Perceptions of Digital Phenotyping Measures of Negative Symptoms Among Individuals At Clinical High Risk for Psychosis and Their Relatives.Schizophrenia bulletin open · 2026Article
- Negative symptoms in schizophrenia: Methodological challenges and emerging solutions for outcome measurement.Neuroscience applied · 2026Review
- AI-enabled passive digital phenotyping of experiential negative symptoms in schizophrenia-spectrum disorders: a narrative review on the path from sensing to clinical endpoints.Frontiers in psychiatry · 2026Review
- Environmental deprivation is associated with reward processing impairments and negative symptoms in schizophrenia.Schizophrenia research · 2025Article
- Social network reductions are associated with negative symptoms in schizophrenia.Social psychiatry and psychiatric epidemiology · 2025Article
- Prediction of antipsychotic drug efficacy for schizophrenia treatment based on neural features of the resting-state functional connectome.Translational psychiatry · 2025Article
- Comparison of Deep Learning and Traditional Machine Learning Models for Predicting Mild Cognitive Impairment Using Plasma Proteomic Biomarkers.International journal of molecular sciences · 2025Article
- A systematic review of passive data for remote monitoring in psychosis and schizophrenia.NPJ digital medicine · 2025Article
- Advances and Challenges in Depression Marker Research.Neuropsychiatric disease and treatment · 2025Review
- Digital Phenotypes for Early Detection of Internet Gaming Disorder in Adolescent Students: Explorative Data-Driven Study.JMIR mental health · 2024Article
- Negative symptoms in schizophrenia differ across environmental contexts in daily life.Journal of psychiatric research · 2023Article
- The future of psychopharmacology: a critical appraisal of ongoing phase 2/3 trials, and of some current trends aiming to de-risk trial programmes of novel agents.World psychiatry : official journal of the World Psychiatric Association (WPA) · 2023Article
- Ambulatory audio and video recording for digital phenotyping in schizophrenia: Adherence & data usability.Psychiatry research · 2022Article
- Advances in Using MRI to Estimate the Risk of Future Outcomes in Mental Health - Are We Getting There?Frontiers in psychiatry · 2022Article
Corrections and comments
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Authors and funding
9 authors at 5 institutions in 1 country.
Funding
Abstract
backgroundDigital phenotyping has been proposed as a novel assessment tool for clinical trials targeting negative symptoms in psychotic disorders (PDs). However, it is unclear which digital phenotyping measurements are most appropriate for this purpose.
aimsMachine learning was used to address this gap in the literature and determine whether: (1) diagnostic status could be classified from digital phenotyping measures relevant to negative symptoms and (2) the 5 negative symptom domains (anhedonia, avolition, asociality, alogia, and blunted affect) were differentially classified by active and passive digital phenotyping variables.
methodsParticipants included 52 outpatients with a PD and 55 healthy controls (CN) who completed 6 days of active (ecological momentary assessment surveys) and passive (geolocation, accelerometry) digital phenotyping data along with clinical ratings of negative symptoms.
resultsMachine learning algorithms classifying the presence of a PD diagnosis yielded 80% accuracy for cross-validation in H2O AutoML and 79% test accuracy in the Recursive Feature Elimination with Cross Validation feature selection model. Models classifying the presence vs absence of clinically significant elevations on each of the 5 negative symptom domains ranged in test accuracy from 73% to 91%. A few active and passive features were highly predictive of all 5 negative symptom domains; however, there were also unique predictors for each domain.
conclusionsThese 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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.