{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,18]],"date-time":"2026-08-18T04:28:11Z","timestamp":1787027291092,"version":"build-2736575974"},"reference-count":58,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2022,12,1]],"date-time":"2022-12-01T00:00:00Z","timestamp":1669852800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Psychiatry"],"abstract":"<jats:sec>\n                    <jats:title>Introduction<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Aim<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Discussion<\/jats:title>\n                    <jats:p>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 &amp;gt;1000 records from &amp;gt;400 participants. To the best of our knowledge, this data sample is one of the largest in this field.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Clinical Trial Registration<\/jats:title>\n                    <jats:p>Identifier: UMIN000032141.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fpsyt.2022.954703","type":"journal-article","created":{"date-parts":[[2022,12,1]],"date-time":"2022-12-01T10:53:35Z","timestamp":1669892015000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":10,"title":["Understanding psychiatric illness through natural language processing (UNDERPIN): Rationale, design, and methodology"],"prefix":"10.3389","volume":"13","author":[{"given":"Taishiro","family":"Kishimoto","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hironobu","family":"Nakamura","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yoshinobu","family":"Kano","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yoko","family":"Eguchi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Momoko","family":"Kitazawa","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kuo-ching","family":"Liang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Koki","family":"Kudo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ayako","family":"Sento","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Akihiro","family":"Takamiya","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Toshiro","family":"Horigome","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Toshihiko","family":"Yamasaki","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuki","family":"Sunami","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Toshiaki","family":"Kikuchi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kazuki","family":"Nakajima","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Masayuki","family":"Tomita","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shogyoku","family":"Bun","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuki","family":"Momota","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kyosuke","family":"Sawada","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Junichi","family":"Murakami","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hidehiko","family":"Takahashi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Masaru","family":"Mimura","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2022,12,1]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"326","DOI":"10.1192\/bjp.bp.106.025106","article-title":"Impact of psychiatric disorders on health-related quality of life: general population survey","volume":"190","author":"Saarni","year":"2007","journal-title":"Br J Psychiatry."},{"key":"B2","doi-asserted-by":"publisher","first-page":"386","DOI":"10.1192\/bjp.bp.109.076489","article-title":"Quality of life of people with schizophrenia, bipolar disorder and other psychotic disorders","volume":"197","author":"Saarni","year":"2010","journal-title":"Br J Psychiatry."},{"key":"B3","doi-asserted-by":"publisher","first-page":"201","DOI":"10.1016\/j.schres.2007.09.001","article-title":"Subjective and objective quality of life in schizophrenia","volume":"98","author":"Narvaez","year":"2008","journal-title":"Schizophr Res."},{"key":"B4","year":"2019","journal-title":"Global health estimates: Leading causes of DALYs"},{"key":"B5","doi-asserted-by":"publisher","first-page":"18","DOI":"10.1186\/1741-7015-9-18","article-title":"complexity as a biomarker for autism spectrum disorder risk","volume":"9","author":"Bosl","year":"2011","journal-title":"BMC Med."},{"key":"B6","doi-asserted-by":"publisher","first-page":"e628","DOI":"10.1038\/tp.2015.124","article-title":"Neurophysiological features of Internet gaming disorder and alcohol use disorder: a resting-state EEG study","volume":"5","author":"Son","year":"2015","journal-title":"Transl Psychiatry."},{"key":"B7","doi-asserted-by":"publisher","first-page":"314","DOI":"10.1016\/j.schres.2016.05.007","article-title":"Machine-learning-based diagnosis of schizophrenia using combined sensor-level and source-level EEG features","volume":"176","author":"Shim","year":"2016","journal-title":"Schizophr Res."},{"key":"B8","doi-asserted-by":"publisher","first-page":"736","DOI":"10.1016\/j.neuroimage.2016.10.045","article-title":"Deriving reproducible biomarkers from multi-site resting-state data: An Autism-based example","volume":"147","author":"Abraham","year":"2017","journal-title":"Neuroimage."},{"key":"B9","doi-asserted-by":"publisher","first-page":"e3000966","DOI":"10.1371\/journal.pbio.3000966","article-title":"Generalizable brain network markers of major depressive disorder across multiple imaging sites","volume":"18","author":"Yamashita","year":"2020","journal-title":"PLoS Biol."},{"key":"B10","doi-asserted-by":"publisher","first-page":"1265","DOI":"10.1002\/hbm.20463","article-title":"Temporal lobe and \u201cdefault\u201d hemodynamic brain modes discriminate between schizophrenia and bipolar disorder","volume":"29","author":"Calhoun","year":"2008","journal-title":"Hum Brain Mapp."},{"key":"B11","doi-asserted-by":"publisher","first-page":"1371","DOI":"10.1016\/S0140-6736(12)62129-1","article-title":"Identification of risk loci with shared effects on five major psychiatric disorders: a genome-wide analysis","volume":"381","year":"2013","journal-title":"Lancet"},{"key":"B12","doi-asserted-by":"publisher","first-page":"502","DOI":"10.1038\/s41586-022-04434-5","article-title":"Mapping genomic loci implicates genes and synaptic biology in schizophrenia","volume":"604","author":"Trubetskoy","year":"2022","journal-title":"Nature."},{"key":"B13","doi-asserted-by":"publisher","first-page":"373","DOI":"10.1176\/appi.ajp.2015.14091200","article-title":"Identification of distinct psychosis biotypes using brain-based biomarkers","volume":"173","author":"Clementz","year":"2016","journal-title":"Am J Psychiatry."},{"key":"B14","doi-asserted-by":"publisher","first-page":"26","DOI":"10.1016\/j.biopsych.2016.08.030","article-title":"Brain structure biomarkers in the psychosis biotypes: findings from the bipolar-schizophrenia network for intermediate phenotypes","volume":"82","author":"Ivleva","year":"2017","journal-title":"Biol Psychiatry."},{"key":"B15","doi-asserted-by":"publisher","first-page":"344","DOI":"10.1111\/eip.12948","article-title":"Linguistic determinants of formal thought disorder in first episode psychosis","volume":"15","author":"Mackinley","year":"2021","journal-title":"Early Interv Psychiatry."},{"key":"B16","doi-asserted-by":"publisher","first-page":"158","DOI":"10.1016\/j.schres.2020.04.032","article-title":"Language as a biomarker for psychosis: a natural language processing approach","volume":"226","author":"Corcoran","year":"2020","journal-title":"Schizophr Res."},{"key":"B17","doi-asserted-by":"publisher","first-page":"58","DOI":"10.1038\/s41537-022-00263-7","article-title":"Who does what to whom? Graph representations of action-predication in speech relate to psychopathological dimensions of psychosis","volume":"8","author":"Nikzad","year":"2022","journal-title":"Schizophrenia."},{"key":"B18","doi-asserted-by":"publisher","first-page":"493","DOI":"10.1016\/j.schres.2020.09.002","article-title":"Lower speech connectedness linked to incidence of psychosis in people at clinical high risk","volume":"228","author":"Spencer","year":"2021","journal-title":"Schizophr Res."},{"key":"B19","doi-asserted-by":"publisher","first-page":"440","DOI":"10.1017\/S0033291718001046","article-title":"Measuring disorganized speech in schizophrenia: automated analysis explains variance in cognitive deficits beyond clinician-rated scales","volume":"49","author":"Minor","year":"2019","journal-title":"Psychol Med."},{"key":"B20","doi-asserted-by":"publisher","first-page":"405","DOI":"10.1093\/schbul\/sbaa126","article-title":"Using natural language processing on electronic health records to enhance detection and prediction of psychosis risk","volume":"47","author":"Irving","year":"2021","journal-title":"Schizophr Bull."},{"key":"B21","doi-asserted-by":"publisher","first-page":"7","DOI":"10.18653\/v1\/2020.acl-main.2","article-title":"Predicting depression in screening interviews from latent categorization of interview prompts","author":"Alex","year":"2020","journal-title":"Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics"},{"key":"B22","doi-asserted-by":"publisher","first-page":"363","DOI":"10.1176\/appi.ajp.2014.14030423","article-title":"Validation of electronic health record phenotyping of bipolar disorder cases and controls","volume":"172","author":"Castro","year":"2015","journal-title":"Am J Psychiatry."},{"key":"B23","doi-asserted-by":"publisher","first-page":"14146","DOI":"10.1038\/s41598-019-49165-2","article-title":"Use of Natural Language Processing to identify Obsessive Compulsive Symptoms in patients with schizophrenia, schizoaffective disorder or bipolar disorder","volume":"9","author":"Chandran","year":"2019","journal-title":"Sci Rep."},{"key":"B24","doi-asserted-by":"publisher","first-page":"12","DOI":"10.7275\/R56W988P","article-title":"Detecting language impairments in autism: a computational analysis of semi-structured conversations with vector semantics","volume":"1","author":"Adam","year":"2018","journal-title":"Proc Soc Comp Linguist."},{"key":"B25","doi-asserted-by":"crossref","first-page":"5841","DOI":"10.1109\/ICASSP.2019.8682690","article-title":"Automatic diagnosis of alzheimer's disease using neural network language models","volume-title":"ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","author":"Fritsch","year":"2019"},{"key":"B26","doi-asserted-by":"crossref","first-page":"1946","DOI":"10.18653\/v1\/2020.acl-main.176","article-title":"A tale of two perplexities: sensitivity of neural language models to lexical retrieval deficits in dementia of the Alzheimer's type","volume-title":"Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics","author":"Cohen","year":"2020"},{"key":"B27","doi-asserted-by":"publisher","first-page":"91","DOI":"10.1111\/sjop.12790","article-title":"Morphological characteristics of spoken language in schizophrenia patients - an exploratory study","volume":"63","author":"Ziv","year":"2022","journal-title":"Scand J Psychol."},{"key":"B28","doi-asserted-by":"publisher","first-page":"25","DOI":"10.1038\/s41537-022-00238-8","article-title":"Thought disorder is correlated with atypical spoken binomial orderings","volume":"8","author":"Murphy","year":"2022","journal-title":"Schizophrenia."},{"key":"B29","doi-asserted-by":"publisher","first-page":"114130","DOI":"10.1016\/j.psychres.2021.114130","article-title":"Quantified language connectedness in schizophrenia-spectrum disorders","volume":"304","author":"Voppel","year":"2021","journal-title":"Psychiatry Res."},{"key":"B30","doi-asserted-by":"publisher","first-page":"114135","DOI":"10.1016\/j.psychres.2021.114135","article-title":"Detecting formal thought disorder by deep contextualized word representations","volume":"304","author":"Sarzynska-Wawer","year":"2021","journal-title":"Psychiatry Res."},{"key":"B31","doi-asserted-by":"publisher","first-page":"25","DOI":"10.1038\/s41537-021-00154-3","article-title":"Natural language processing methods are sensitive to sub-clinical linguistic differences in schizophrenia spectrum disorders","volume":"7","author":"Tang","year":"2021","journal-title":"NPJ Schizophr."},{"key":"B32","doi-asserted-by":"publisher","first-page":"9","DOI":"10.1038\/s41537-019-0077-9","article-title":"A machine learning approach to predicting psychosis using semantic density and latent content analysis","volume":"5","author":"Rezaii","year":"2019","journal-title":"NPJ Schizophr."},{"key":"B33","doi-asserted-by":"crossref","first-page":"126","DOI":"10.18653\/v1\/W19-3015","article-title":"Coherence models in schizophrenia","volume-title":"Proceedings of the Sixth Workshop on Computational Linguistics and Clinical Psychology","author":"Just","year":"2019"},{"key":"B34","doi-asserted-by":"publisher","first-page":"e34928","DOI":"10.1371\/journal.pone.0034928","article-title":"Speech graphs provide a quantitative measure of thought disorder in psychosis","volume":"7","author":"Mota","year":"2012","journal-title":"PLoS One."},{"key":"B35","doi-asserted-by":"publisher","first-page":"630","DOI":"10.1038\/s41398-021-01722-y","article-title":"Natural Language Processing markers in first episode psychosis and people at clinical high-risk","volume":"11","author":"Morgan","year":"2021","journal-title":"Transl Psychiatry."},{"key":"B36","doi-asserted-by":"publisher","first-page":"67","DOI":"10.1002\/wps.20491","article-title":"Prediction of psychosis across protocols and risk cohorts using automated language analysis","volume":"17","author":"Corcoran","year":"2018","journal-title":"World Psychiatry."},{"key":"B37","doi-asserted-by":"publisher","first-page":"15030","DOI":"10.1038\/npjschz.2015.30","article-title":"Automated analysis of free speech predicts psychosis onset in high-risk youths","volume":"1","author":"Bedi","year":"2015","journal-title":"NPJ Schizophr."},{"key":"B38","doi-asserted-by":"publisher","DOI":"10.1101\/2021.08.16.21262125","article-title":"A longitudinal normative dataset protocol for speech language biomarker research","author":"Schwoebel","year":"2021","journal-title":"medRxiv. Preprint"},{"key":"B39","unstructured":""},{"key":"B40","doi-asserted-by":"publisher","first-page":"96","DOI":"10.1002\/lio2.354","article-title":"Automated assessment of psychiatric disorders using speech: a systematic review","volume":"5","author":"Low","year":"2020","journal-title":"Laryngoscope Investig Otolaryngol."},{"key":"B41","unstructured":"BoersmaP\n            WeeninkD\n          PraatDoing Phonetics by Computer2018"},{"key":"B42","doi-asserted-by":"crossref","first-page":"1459","DOI":"10.1145\/1873951.1874246","article-title":"openSMILE - the munich versatile and fast open-source audio feature extractor","volume-title":"Proceedings of the 18th ACM international conference on Multimedia","author":"Eyben","year":"2010"},{"key":"B43","first-page":"230","article-title":"Applying conditional random fields to japanese morphological analysis","volume-title":"Proceedings of the 2004 Conference on Empirical Methods in Natural language Processing","author":"Kudo","year":"2004"},{"key":"B44","unstructured":""},{"key":"B45","first-page":"507","article-title":"A syntactic analysis method of long Japanese sentences based on the detection of conjunctive structures","volume":"20","author":"Kurohashi","year":"1994","journal-title":"Comput Linguist."},{"key":"B46","unstructured":""},{"key":"B47","first-page":"4171","article-title":"BERT: Pre-training of deep bidirectional transformers for language understanding","volume-title":"Proceedings of 2019th Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies","author":"Devlin","year":"2019"},{"key":"B48","doi-asserted-by":"publisher","first-page":"143","DOI":"10.1002\/sim.4780030207","article-title":"Regression modelling strategies for improved prognostic prediction","volume":"3","author":"Harrell FE","year":"1984","journal-title":"Stat Med"},{"key":"B49","doi-asserted-by":"publisher","first-page":"1248","DOI":"10.1038\/nm.4190","article-title":"The search for imaging biomarkers in psychiatric disorders","volume":"22","author":"Abi-Dargham","year":"2016","journal-title":"Nat Med."},{"key":"B50","doi-asserted-by":"publisher","first-page":"211","DOI":"10.1002\/wps.20144","article-title":"Biomarkers and clinical staging in psychiatry","volume":"13","author":"McGorry","year":"2014","journal-title":"World Psychiatry."},{"key":"B51","doi-asserted-by":"publisher","first-page":"257","DOI":"10.1016\/0093-934X(74)90040-6","article-title":"linguist looks at \u201cschizophrenic\u201d language","volume":"1","author":"Chaika","year":"1974","journal-title":"Brain Lang."},{"key":"B52","doi-asserted-by":"publisher","first-page":"167","DOI":"10.1016\/0021-9924(82)90032-6","article-title":"unified explanation for the diverse structural deviations reported for adult schizophrenics with disrupted speech","volume":"15","author":"Chaika","year":"1982","journal-title":"J Commun Disord."},{"key":"B53","doi-asserted-by":"publisher","first-page":"10","DOI":"10.1038\/s41537-020-0099-3","article-title":"Language in schizophrenia: relation with diagnosis, symptomatology and white matter tracts","volume":"6","author":"De Boer","year":"2020","journal-title":"NPJ Schizophr."},{"key":"B54","doi-asserted-by":"publisher","first-page":"954898","DOI":"10.3389\/fnhum.2022.954898","article-title":"Widespread cortical thinning, excessive glutamate and impaired linguistic functioning in schizophrenia: A cluster analytic approach","volume":"16","author":"Liang","year":"2022","journal-title":"Front Hum Neurosci."},{"key":"B55","doi-asserted-by":"publisher","first-page":"1785","DOI":"10.1176\/appi.ajp.162.10.1785","article-title":"Relationship between duration of untreated psychosis and outcome in first-episode schizophrenia: a critical review and meta-analysis","volume":"162","author":"Perkins","year":"2005","journal-title":"Am J Psychiatry."},{"key":"B56","doi-asserted-by":"publisher","DOI":"10.1016\/j.schres.2022.07.002","article-title":"Speech disturbances in schizophrenia: Assessing cross-linguistic generalizability of NLP automated measures of coherence","author":"Parola","year":"2022","journal-title":"Schizophr Res"},{"key":"B57","doi-asserted-by":"publisher","DOI":"10.1101\/2022.04.03.22273354","article-title":"Voice patterns as markers of schizophrenia: building a cumulative generalizable approach via a cross-linguistic and meta-analysis based investigation","author":"Parola","year":"2022","journal-title":"medRxiv. Preprint"},{"key":"B58","article-title":"Large-scale dialog corpus towards automatic mental disease diagnosis","author":"Sakishita","year":"2019","journal-title":"International Workshop on Health Intelligence (W3PHAI 2019), AAAI-19"}],"container-title":["Frontiers in Psychiatry"],"original-title":[],"link":[{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fpsyt.2022.954703\/full","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,12,1]],"date-time":"2022-12-01T10:53:43Z","timestamp":1669892023000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fpsyt.2022.954703\/full"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,12,1]]},"references-count":58,"alternative-id":["10.3389\/fpsyt.2022.954703"],"URL":"https:\/\/doi.org\/10.3389\/fpsyt.2022.954703","relation":{"has-preprint":[{"id-type":"doi","id":"10.1101\/2021.12.05.21267037","asserted-by":"object"}]},"ISSN":["1664-0640"],"issn-type":[{"value":"1664-0640","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,12,1]]},"article-number":"954703"}}