| アイテムタイプ |
学術雑誌論文 / Journal Article(1) |
| 公開日 |
2026-03-23 |
| タイトル |
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|
タイトル |
Development of a Machine Learning-Based Prediction Model to Differentiate Infectious and Non-Infectious Diseases in Patients with Undiagnosed Fever: A Single Hospital-Based Retrospective Study |
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言語 |
en |
| 言語 |
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|
言語 |
eng |
| キーワード |
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|
言語 |
en |
|
キーワード |
infection |
| キーワード |
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言語 |
en |
|
キーワード |
lactate dehydrogenase level |
| キーワード |
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|
言語 |
en |
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キーワード |
machine learning |
| キーワード |
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言語 |
en |
|
キーワード |
neutrophil percentage |
| キーワード |
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言語 |
en |
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キーワード |
platelet count |
| キーワード |
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言語 |
en |
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キーワード |
prediction model |
| キーワード |
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|
言語 |
en |
|
キーワード |
serum ferritin level |
| キーワード |
|
|
言語 |
en |
|
キーワード |
undiagnosed fever |
| キーワード |
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言語 |
en |
|
キーワード |
white blood cell count |
| 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 |
中村, 仁彦
WEKO
34185
e-Rad_Researcher
40893053
| ja |
中村, 仁彦
宮崎大学
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| ja-Kana |
ナカムラ, マサヒコ
|
| en |
Nakamura, Masahiko
University of Miyazaki
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Search repository
Yamashita, Shun
Osako, Ryosuke
Motomura, So
Katsuki, Naoko E
Yamashita, Shu-Ichi
Tago, Masaki
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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 |
Background/Objectives: Fever can develop from several causes, including infectious diseases, noninfectious inflammatory diseases (NIID), malignancies, and other medical conditions. Although serum ferritin (SF) level can help differentiate infectious from non-infectious diseases, its discriminative ability (specificity) is far from satisfactory. The aim of this study was to develop a diagnostic prediction model to distinguish infectious diseases from other febrile illnesses using only common blood tests available on admission, in addition to SF level, in patients with undiagnosed fever. Methods: This single-center retrospective observational study included patients with fever of unidentified origin aged ≥18 years admitted to a Japanese acute care hospital between 1 January 2013, and 31 December 2022. They were divided into infectious and non-infectious disease groups based on their final diagnosis. Machine learning and multivariable logistic regression analysis were used to develop a model to differentiate infectious diseases from non-infectious diseases. Model performance was evaluated using area under the curve (AUC), shrinkage coefficient, and stratified likelihood ratio. Results: Among the 143 patients included, 73 had infectious diseases. A prediction model consisting of five factors-serum white blood cell count, neutrophil percentage, platelet count, lactate dehydrogenase level, and log-transformed SF level-was developed. The AUC of the model was 0.794 (95% confidence interval: 0.721-0.867) with a sensitivity of 77.1%, specificity of 68.5%, shrinkage coefficient of 0.876, and stratified likelihood ratio of 0.13-5.04. Conclusions: We developed a prediction model consisting of only five high-performing indicators, which would help differentiate infectious diseases from other fever causes early after admission. |
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言語 |
en |
| 書誌情報 |
en : Journal of clinical medicine
巻 15,
号 5,
p. 1905,
発行日 2026-03-02
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| 出版者 |
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出版者 |
MDPI AG |
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言語 |
en |
| ISSN |
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収録物識別子タイプ |
PISSN |
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収録物識別子 |
20770383 |
| DOI |
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関連タイプ |
isVersionOf |
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識別子タイプ |
DOI |
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関連識別子 |
https://doi.org/10.3390/jcm15051905 |
| 権利 |
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|
権利情報 |
© 2026 by the authors. |
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言語 |
en |
| 著者版フラグ |
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出版タイプ |
VoR |
Fulltext (768 KB)
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