Development of machine-learning models to diagnose influenza among international travellers based on symptoms and epidemiological information
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.
Authors
- Nobumasa Okumura (ORCID: https://orcid.org/0000-0002-0434-4842)
- Toshinori Sahara
- Kyoko Yokota
- Koh Shinohara (ORCID: https://orcid.org/0000-0001-8934-0375)
- Yukihiro Yoshimura (ORCID: https://orcid.org/0000-0002-5046-7819)
- Yusuke Yoshimi
- Ryota Hase (ORCID: https://orcid.org/0000-0002-3135-1757)
- Hidenori Nakagawa (ORCID: https://orcid.org/0000-0001-6711-588X)
- Natsuko Imakita (ORCID: https://orcid.org/0000-0002-4684-3498)
- Hiroshi Miyawaki
- Masaya Yamato
- Hidetoshi Nomoto
- Takahiro Ichikawa
- Naoya Itoh
- Norio Ohmagari
- Yoshihiro Yamamoto
- Naoya Sakamoto
- Atsushi Nagasaka
- Yusuke Asai
Institutions
- Nagoya City University (JP)
- National Center for Global Health and Medicine (JP)
- Ebara Hospital (JP)
- Osaka City General Hospital (JP)
- Tokyo Metropolitan Bokutoh Hospital (JP)
- Kyoto City Hospital (JP)
- Nara Medical University Hospital (JP)
- Yokohama Municipal Citizen's Hospital (JP)
- Japanese Red Cross Narita Hospital (JP)
- Toyama University Hospital (JP)
- Sapporo City General Hospital (JP)
- Japanese Red Cross Nagoya Daini Hospital (JP)
- Kagawa Prefectural Central Hospital (JP)
Publication Details
- Journal
- Travel Medicine and Infectious Disease
- Published
- 2026-03-07
- DOI
- https://doi.org/10.1016/j.tmaid.2026.102966
- Primary Topic
- Data-Driven Disease Surveillance
- Type
- article
- Field-Weighted Citation Impact
- 0.00