Paper · 42298144

Digital phenotyping with large language models to detect depressive state changes in patients.

Yuan Y and 5 others. NPJ Digit Med, 2026. Cited by 0 papers, by iCite.

11 sourcespapers-2026-11-12-0.1.0

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ConductScience Library. Digital phenotyping with large language models to detect depressive state changes in patients. Papers, record 42298144. Release papers-2026-11-12-0.1.0, 12 Nov 2026. https://conductscience.org/library/papers/42298144

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Paper

PubMed's record of the paper, with iCite's citation figures. No abstract is shown.

Title: Digital phenotyping with large language models to detect depressive state changes in patients.; PubMed
Journal: NPJ Digit Med; PubMed
Year: 2026; PubMed
Publication types: Journal Article; PubMed
PubMed: https://pubmed.ncbi.nlm.nih.gov/42298144/; PubMed
DOI: https://doi.org/10.1038/s41746-026-02883-0; PubMed
PMC: https://pmc.ncbi.nlm.nih.gov/articles/PMC13631235/; PubMed
The journal: https://conductscience.org/library/journal-index/101731738; PubMed
Citations, by iCite: 0; iCite
Revised in PubMed: 2026-10-04; PubMed

Authors6

The authors in the order PubMed's record lists them, with the ORCID iDs it states.

Order	Author	Forename
1	Yuan Y	Yunhao
2	Gao Y	Ya
3	Moen H	Hans
4	Isometsä E	Erkki
5	Marttinen P	Pekka
6	Aledavood T	Talayeh

Source PubMed

Topics0

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None in this release.

Grants0

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None in this release.

Trials0

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None in this release.

Comments and corrections0

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None in this release.

References36

The papers the reference list in PubMed's record names, those in our copy of PubMed, in the list's order.

Order	PMID	Title	Journal	Year	In Papers	Source
1	26,977,102	Automatic detection of social rhythms in bipolar disorder.	J Am Med Inform Assoc	2016	https://conductscience.org/library/papers/26977102	PubMed
2	41,992,044	Variability in self-reported depression symptomology and associated behavioral markers in digital phenotyping.	Sci Rep	2026	https://conductscience.org/library/papers/41992044	PubMed
3	31,304,300	Characterizing the clinical relevance of digital phenotyping data quality with applications to a cohort with schizophrenia.	NPJ Digit Med	2018	https://conductscience.org/library/papers/31304300	PubMed
4	33,261,932	Meaningful Change in Depression Symptoms Assessed with the Patient Health Questionnaire (PHQ-9) and Montgomery-Åsberg Depression Rating Scale (MADRS) Among Patients with Treatment Resistant Depression in Two, Randomized, Double-blind, Active-controlled Trials of Esketamine Nasal Spray Combined With a New Oral Antidepressant.	J Affect Disord	2021	https://conductscience.org/library/papers/33261932	PubMed
5	28,600,276	Data Collection for Mental Health Studies Through Digital Platforms: Requirements and Design of a Prototype.	JMIR Res Protoc	2017	https://conductscience.org/library/papers/28600276	PubMed
6	34,397,386	Automated Screening for Social Anxiety, Generalized Anxiety, and Depression From Objective Smartphone-Collected Data: Cross-sectional Study.	J Med Internet Res	2021	https://conductscience.org/library/papers/34397386	PubMed
7	25,370,280	Depression and borderline personality disorder.	Med J Aust	2013	https://conductscience.org/library/papers/25370280	PubMed
8	38,036,723	Zero-shot interpretable phenotyping of postpartum hemorrhage using large language models.	NPJ Digit Med	2023	https://conductscience.org/library/papers/38036723	PubMed
9	37,037,254	Psychotic Relapse Prediction in Schizophrenia Patients Using A Personalized Mobile Sensing-Based Supervised Deep Learning Model.	IEEE J Biomed Health Inform	2023	https://conductscience.org/library/papers/37037254	PubMed
10	38,347,090	Digital health technologies and machine learning augment patient reported outcomes to remotely characterise rheumatoid arthritis.	NPJ Digit Med	2024	https://conductscience.org/library/papers/38347090	PubMed

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Cited by0

The papers in our copy of PubMed whose reference list names this one, newest first. iCite, which reads more sources, counts 0 citations.

None in this release.

Genes0

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None in this release.

Drugs0

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None in this release.

Data0

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Data dates

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PubMed: 2026-11-06; PubMed
iCite: 2026-11-05; iCite
PMC's ID file: 2026-11-05; PMC's ID file
NIH RePORTER's grant and paper links: 2026-11-01; NIH RePORTER's grant and paper links
Trials: 2026-11-12
Gene search: 2026-11-11
Pipelines: 2026-09-18

Sources 11

ChEMBL, through Open TargetsCC BY-SA 3.0
ClinicalTrials.govNo license stated
HGNCCC0
iCiteCC BY 4.0
NCBI GenePublic domain (US government)
NIH RePORTER's grant and paper linksPublic domain
Open TargetsCC0 1.0
PMC's ID filePublic domain (US government)
PubMedNLM's terms
PubTator 3Public domain
UniProtCC BY 4.0

Each source's terms

ChEMBL, through Open TargetsCC BY-SA 3.0, for the parts from ChEMBL. Read on 8 Nov 2026 at https://platform-docs.opentargets.org/licence
ClinicalTrials.govnone stated
HGNCCC0: https://www.genenames.org/about/license/
iCiteCC BY 4.0
NCBI GeneNone named for the file. NCBI says information created by or for the US government on its site is in the public domain, and asks that NLM be acknowledged: https://www.ncbi.nlm.nih.gov/home/about/policies/
NIH RePORTER's grant and paper linksPublic domain (NIH RePORTER)
Open TargetsCC0 1.0. Open Targets asks that users cite its latest paper: Buniello et al. 2025, Open Targets Platform: facilitating therapeutic hypotheses building in drug discovery, Nucleic Acids Research 53(D1):D1467, PMID 39657122. Read on 8 Nov 2026 at https://platform-docs.opentargets.org/licence
PMC's ID fileNone named for the file. NCBI says information created by or for the US government on its site is in the public domain, and asks that NLM be acknowledged: https://www.ncbi.nlm.nih.gov/home/about/policies/
PubMedNLM's terms
PubTator 3Public domain
UniProtCC BY 4.0. UniProt's licence file: "All databases and documents in the UniProt FTP directory may be copied and redistributed freely, without advance permission, provided that this copyright statement is reproduced with each copy." UniProt asks that users cite its latest paper: The UniProt Consortium, UniProt: the Universal Protein Knowledgebase in 2025, Nucleic Acids Research 53:D609-D617, https://doi.org/10.1093/nar/gkae1010. Read on 9 Nov 2026 at https://www.uniprot.org/help/license

ClinicalTrials.gov's data carry an international copyright outside the United States: https://clinicaltrials.gov/about-site/terms-conditions.

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