Development of machine-learning models to diagnose influenza among international travellers based on symptoms and epidemiological information

Yusuke Asai; Yuki Hisaki; Hidetoshi Nomoto; Hidenori Nakagawa; Toshinori Sahara; Masaya Yamato; Naoya Sakamoto; Ryota Hase; Koh Shinohara; Yukihiro Yoshimura; Atsushi Nagasaka; Takahiro Ichikawa; Natsuko Imakita; Hiroshi Miyawaki; Kyoko Yokota; Yoshihiro Yamamoto; Naoya Itoh; Nobumasa Okumura; Yusuke Yoshimi; Norio Ohmagari

doi:10.1016/j.tmaid.2026.102966

Key Points

The aim is to develop machine-learning models for diagnosing influenza effectively using symptoms and epidemiological data.
Developed machine-learning models incorporating clinical and epidemiological data.
Analyzed diverse symptom profiles to enhance diagnostic accuracy.
Utilized data from international travelers to tailor the models.
Improved diagnostic accuracy when using epidemiological information alongside symptoms.
Models demonstrated the potential to effectively identify influenza cases.
Findings can inform travelers about influenza risks and enhance public health efforts.

Implication

Machine learning models improve influenza diagnosis in international travelers, suggesting higher accuracy through added epidemiological data.

Abstract

Adding epidemiological information to the usual clinical information can improve diagnostic accuracy. Machine-learning models capable of handling a wide variety of data would be particularly beneficial for diagnosis. The knowledge gained should also be used to raise awareness among travellers.

Discussion

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Cite This Study

Asai et al. (2026) studied this question.

synapsesocial.com/papers/69af94c970916d39fea4ba7a https://doi.org/10.1016/j.tmaid.2026.102966

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Development of machine-learning models to diagnose influenza among international travellers based on symptoms and epidemiological information

Key Points

Implication

Abstract

Discussion

Cite This Study

Development of machine-learning models to diagnose influenza among international travellers based on symptoms and epidemiological information

Key Points

Implication

Abstract

Discussion

Cite This Study

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