Evidence map›Paper›PMID 36532181›Full record

ArticleFrontiers in psychiatry2022

Understanding psychiatric illness through natural language processing (UNDERPIN): Rationale, design, and methodology.

Taishiro Kishimoto, Hironobu Nakamura, Yoshinobu Kano, Yoko Eguchi, Momoko Kitazawa, Kuo-Ching Liang, Koki Kudo, Ayako Sento, Akihiro Takamiya, Toshiro Horigome and 11 more

Abstract read
In one paragraph

Article in Frontiers in psychiatry, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

5 citing papers in PubMed.

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  5. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

21 authors.

Taishiro KishimotoDepartment of Neuropsychiatry, Keio University School of Medicine, Tokyo, Japan.
Hironobu NakamuraDepartment of Psychiatry and Behavioral Sciences, Graduate School of Medical and Dental Sciences, Tokyo Medical and Dental University, Tokyo, Japan.
Yoshinobu KanoFaculty of Informatics, Shizuoka University, Shizuoka, Japan.
Yoko EguchiDepartment of Neuropsychiatry, Keio University School of Medicine, Tokyo, Japan.
Momoko KitazawaDepartment of Neuropsychiatry, Keio University School of Medicine, Tokyo, Japan.
Kuo-Ching LiangDepartment of Neuropsychiatry, Keio University School of Medicine, Tokyo, Japan.
Koki KudoDepartment of Neuropsychiatry, Keio University School of Medicine, Tokyo, Japan.
Ayako SentoDepartment of Neuropsychiatry, Keio University School of Medicine, Tokyo, Japan.
Akihiro TakamiyaDepartment of Neuropsychiatry, Keio University School of Medicine, Tokyo, Japan.
Toshiro HorigomeDepartment of Neuropsychiatry, Keio University School of Medicine, Tokyo, Japan.
Toshihiko YamasakiComputer Vision and Media Lab (Yamasaki Lab), Department of Information and Communication Engineering, Graduate School of Information Science and Technology, The University of Tokyo, Tokyo, Japan.
Yuki SunamiKeio University School of Medicine, Tokyo, Japan.
Toshiaki KikuchiDepartment of Neuropsychiatry, Keio University School of Medicine, Tokyo, Japan.
Kazuki NakajimaDepartment of Neuropsychiatry, Keio University School of Medicine, Tokyo, Japan.
Masayuki TomitaDepartment of Psychiatry, Oizumi Hospital, Tokyo, Japan.
Shogyoku BunDepartment of Neuropsychiatry, Keio University School of Medicine, Tokyo, Japan.
Yuki MomotaDepartment of Neuropsychiatry, Keio University School of Medicine, Tokyo, Japan.
Kyosuke SawadaDepartment of Neuropsychiatry, Keio University School of Medicine, Tokyo, Japan.
Junichi MurakamiDepartment of Psychiatry, Biwako Hospital, Otsu, Japan.
Hidehiko TakahashiDepartment of Psychiatry and Behavioral Sciences, Graduate School of Medical and Dental Sciences, Tokyo Medical and Dental University, Tokyo, Japan.
Masaru MimuraDepartment of Neuropsychiatry, Keio University School of Medicine, Tokyo, Japan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: Psychiatric disorders are diagnosed through observations of psychiatrists according to diagnostic criteria such as the DSM-5. Such observations, however, are mainly based on each psychiatrist's level of experience and often lack objectivity, potentially leading to disagreements among psychiatrists. In contrast, specific linguistic features can be observed in some psychiatric disorders, such as a loosening of associations in schizophrenia. Some studies explored biomarkers, but biomarkers have yet to be used in clinical practice. Aim: The purposes of this study are to create a large dataset of Japanese speech data labeled with detailed information on psychiatric disorders and neurocognitive disorders to quantify the linguistic features of those disorders using natural language processing and, finally, to develop objective and easy-to-use biomarkers for diagnosing and assessing the severity of them. Methods: This study will have a multi-center prospective design. The DSM-5 or ICD-11 criteria for major depressive disorder, bipolar disorder, schizophrenia, and anxiety disorder and for major and minor neurocognitive disorders will be regarded as the inclusion criteria for the psychiatric disorder samples. For the healthy subjects, the absence of a history of psychiatric disorders will be confirmed using the Mini-International Neuropsychiatric Interview (M.I.N.I.). The absence of current cognitive decline will be confirmed using the Mini-Mental State Examination (MMSE). A psychiatrist or psychologist will conduct 30-to-60-min interviews with each participant; these interviews will include free conversation, picture-description task, and story-telling task, all of which will be recorded using a microphone headset. In addition, the severity of disorders will be assessed using clinical rating scales. Data will be collected from each participant at least twice during the study period and up to a maximum of five times at an interval of at least one month. Discussion: This study is unique in its large sample size and the novelty of its method, and has potential for applications in many fields. We have some challenges regarding inter-rater reliability and the linguistic peculiarities of Japanese. As of September 2022, we have collected a total of >1000 records from >400 participants. To the best of our knowledge, this data sample is one of the largest in this field. Clinical Trial Registration: Identifier: UMIN000032141.

Indexed as

biomarkerlanguagemachine learningnatural language processing (computer science)neurocognitive disorderspsychiatric disorders

Identifiers

PMID36532181
PMCPMC9752868

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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.