| アイテムタイプ |
学術雑誌論文 / Journal Article(1) |
| 公開日 |
2024-07-04 |
| タイトル |
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タイトル |
Diagnosing psychiatric disorders from history of present illness using a large-scale linguistic model |
| 言語 |
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言語 |
eng |
| キーワード |
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主題Scheme |
Other |
|
主題 |
BERT-based prediction |
| キーワード |
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主題Scheme |
Other |
|
主題 |
diagnostic prediction |
| キーワード |
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主題Scheme |
Other |
|
主題 |
history ofpresent illness |
| キーワード |
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主題Scheme |
Other |
|
主題 |
natural language processing |
| 資源タイプ |
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資源タイプ |
journal article |
| アクセス権 |
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アクセス権 |
open access |
| 著者 |
Otsuka, Norio
Kawanishi, Yuu
Doi, Fumimaro
Takeda, Tsutomu
Okumura, Kazuki
Yamauchi, Takahira
矢田, 竣太郎
若宮, 翔子
荒牧, 英治
Makinodan, Manabu
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| 抄録 |
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内容記述タイプ |
Abstract |
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内容記述 |
Aim: Recent advances in natural language processing models are expected to provide diagnostic assistance in psychiatry from the history of present illness (HPI). However, existing studies have been limited, with the target diseases including only major diseases, small sample sizes, or no comparison with diagnoses made by psychiatrists to ensure accuracy. Therefore, we formulated an accurate diagnostic model that covers all psychiatric disorders.
Methods: HPIs and diagnoses were extracted from discharge summaries of 2,642 cases at the Nara Medical University Hospital, Japan, from 21 May 2007, to 31 May 31 2021. The diagnoses were classified into 11 classes according to the code from ICD-10 Chapter V. Using UTH-BERT pre-trained on the electronic medical records of the University of Tokyo Hospital, Japan, we predicted the main diagnoses at discharge based on HPIs and compared the concordance rate with the results of psychiatrists. The psychiatrists were divided into two groups: semi-Designated with 3$20134$2009years of experience and Residents with only 2$2009months of experience.
Results: The model's match rate was 74.3%, compared to 71.5% for the semi-Designated psychiatrists and 69.4% for the Residents. If the cases were limited to those correctly answered by the semi-Designated group, the model and the Residents performed at 84.9% and 83.3%, respectively.
Conclusion: We demonstrated that the model matched the diagnosis predicted from the HPI with a high probability to the principal diagnosis at discharge. Hence, the model can provide diagnostic suggestions in actual clinical practice. |
| 書誌情報 |
en : Psychiatry and Clinical Neurosciences
巻 77,
号 11,
p. 597-604,
発行日 2023-08-01
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| 出版者 |
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出版者 |
Wiley |
| ISSN |
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収録物識別子タイプ |
EISSN |
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収録物識別子 |
1440-1819 |
| 出版者版DOI |
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関連タイプ |
isReplacedBy |
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識別子タイプ |
DOI |
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関連識別子 |
https://doi.org/10.1111/pcn.13580 |
| 出版者版URI |
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関連タイプ |
isReplacedBy |
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識別子タイプ |
URI |
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関連識別子 |
https://onlinelibrary.wiley.com/doi/full/10.1111/pcn.13580 |
| 権利 |
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権利情報Resource |
http://creativecommons.org/licenses/by-nc-nd/4.0/ |
|
権利情報 |
$00A9 2023 The Authors. This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in anymedium, provided the original work is properly cited, the use is non-commercial and no modi$FB01cations or adaptations are made. |
| 著者版フラグ |
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出版タイプ |
NA |
