Large-Scale Dialog Corpus Towards Automatic Mental Disease Diagnosis

Sakishita, Masahito; Kishimoto, Toshifumi; Takinami, Akiho; Eguchi, Yoko; Kano, Yoshinobu

doi:10.1007/978-3-030-24409-5_10

Cited by 4 publications

(4 citation statements)

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“…SVM, which does not require such large numbers of training samples, was used to avoid this issue in our previous study [ 13 ], and it showed an accuracy of approximately 80% for classifying each mental disease compared to healthy people, even though we built the world’s largest database of recorded conversations between subjects (more than 300 h). These accuracy scores were obtained by cross-fold validation to obtain stable evaluations.…”

Section: Discussionmentioning

confidence: 99%

“…The patient’s verbal explanations of these scenes were recorded, manually transcribed and annotated and then automatically analyzed using AI software [ 13 ]. This AI software performs classification tasks by machine learning; it was originally trained to classify psychiatric diseases such as depression, anxiety and dementia using more than 300 h of recorded conversations between subjects and doctors.…”

Section: Methodsmentioning

confidence: 99%

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Speech Analysis Using Artificial Intelligence as a Peri-Operative Evaluation: A Case Report of a Patient with Temporal Lobe Epilepsy Secondary to Tuberous Sclerosis Complex Who Underwent Epilepsy Surgery

et al. 2021

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Background: Improved conversational fluency is sometimes identified postoperatively in patients with epilepsy, but improvements can be difficult to assess using tests such as the intelligence quotient (IQ) test. Evaluation of pre- and postoperative differences might be considered subjective at present because of the lack of objective criteria. Artificial intelligence (AI) could possibly be used to make the evaluations more objective. The aim of this case report is thus to analyze the speech of a young female patient with epilepsy before and after surgery. Method: The speech of a nine-year-old girl with epilepsy secondary to tuberous sclerosis complex is recorded during interviews one month before and two months after surgery. The recorded speech is then manually transcribed and annotated, and subsequently automatically analyzed using AI software. IQ testing is also conducted on both occasions. The patient remains seizure-free for at least 13 months postoperatively. Results: There are decreases in total interview time and subjective case markers per second, whereas there are increases in morphemes and objective case markers per second. Postoperatively, IQ scores improve, except for the Perceptual Reasoning Index. Conclusions: AI analysis is able to identify differences in speech before and after epilepsy surgery upon an epilepsy patient with tuberous sclerosis complex.

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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Speech Analysis Using Artificial Intelligence as a Peri-Operative Evaluation: A Case Report of a Patient with Temporal Lobe Epilepsy Secondary to Tuberous Sclerosis Complex Who Underwent Epilepsy Surgery

et al. 2021

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“…The design of the study and the dataset that was acquired were briefly presented at the International Workshop on Health Intelligence, 2019 ( 58 ).…”

Section: Preprint and Previous Presentationmentioning

confidence: 99%

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

et al. 2022

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IntroductionPsychiatric 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.AimThe 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.MethodsThis 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.DiscussionThis 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 RegistrationIdentifier: UMIN000032141.

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“…The design of the study and the dataset that was acquired were briefly presented at the International Workshop on Health Intelligence, 2019 [23].…”

Section: Preprint and Previous Presentationmentioning

confidence: 99%

Understanding Psychiatric Illness Through Natural Language Processing (UNDERPIN): Rationale, Design, and Methodology

Kishimoto

Nakamura²,

Kano

et al. 2021

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Introduction: Psychiatric disorders are diagnosed according to diagnostic criteria such as the DSM-5 and ICD-11. Basically, psychiatrists extract symptoms and make a diagnosis by conversing with patients. However, such processes often lack objectivity. In contrast, specific linguistic features can be observed in some psychiatric disorders, such as a loosening of associations in schizophrenia. The purposes of the present study are to quantify the language features of psychiatric disorders and neurocognitive disorders using natural language processing and to identify features that differentiate disorders from one another and from healthy subjects. Methods: This study will have a multi-center prospective design. Major depressive disorder, bipolar disorder, schizophrenia, anxiety disorder including obsessive compulsive disorder and, major and minor neurocognitive disorders, as well as healthy subjects will be recruited. A psychiatrist or psychologist will conduct 30-to-60-min interviews with each participant and these interviews 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. Discussion: The overall goal of this proposed study, the Understanding Psychiatric Illness Through Natural Language Processing (UNDERPIN), is to develop objective and easy-to-use biomarkers for diagnosing and assessing the severity of each psychiatric disorder using natural language processing. As of August 2021, we have collected a total of >900 datasets from >350 participants. To the best of our knowledge, this data sample is one of the largest in this field.

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Large-Scale Dialog Corpus Towards Automatic Mental Disease Diagnosis

Cited by 4 publications

References 8 publications

Speech Analysis Using Artificial Intelligence as a Peri-Operative Evaluation: A Case Report of a Patient with Temporal Lobe Epilepsy Secondary to Tuberous Sclerosis Complex Who Underwent Epilepsy Surgery

Speech Analysis Using Artificial Intelligence as a Peri-Operative Evaluation: A Case Report of a Patient with Temporal Lobe Epilepsy Secondary to Tuberous Sclerosis Complex Who Underwent Epilepsy Surgery

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

Understanding Psychiatric Illness Through Natural Language Processing (UNDERPIN): Rationale, Design, and Methodology

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