Biomedical Informatics

Biomedical Informatics

Associate Professor

Takeshi Imai

1. Research

Targeting biomedical research support using information technologies, the division performs management of the research network and the central servers of the Graduate School of Medicine, and also performs researches on knowledge infrastructure, knowledge processing techniques, and their application to clinical practice.
For example, with an increase in the medical care information being compiled electronically, the significance of secondary use of medical information for advanced knowledge processing, such as information retrieval, data mining, automated coding and decision support systems, has emerged as a practical concern. For those purposes, several knowledge bases and fundamental techniques in the medical informatics domain are required, such as terminologies and ontologies, natural language processing, machine reasoning and so on.
The division actively collaborates with the Department of Medical Informatics and Economics, Graduate School of Medicine at the University of Tokyo, and the Department of Planning, Information and Management at the University of Tokyo Hospital, to develop such knowledge resources and processing techniques, and to develop methodologies for applying those techniques to clinical practice. Our activities also include international standardization of healthcare information models at ISO TC215 (health informatics) WG3.


  1. Development of the Japanese medical ontology and its theoretical basis.
  2. Development of methodologies for mapping medical terminologies and other text resources to medical ontologies.
  3. Natural language processing and its application to the medical domain.
  4. Development of machine reasoning techniques and their application to clinical decision support systems.
  5. Standardization of information models of health concept representation.

2. Publications

  1. Iwai S, Mitani T, Hayakawa J, Shinohara E, Imai T, Kawazoe Y, Ohe K. Development of Graph-Based Algorithm for Differentiating Pathophysiological Conditions. Applied Medical Informatics. 2020;42(2):107-117.
  2. Mitani T, Doi S, Yokota S, Imai T, Ohe K. Highly accurate and explainable detection of specimen mix-up using a machine learning model. Clin Chem Lab Med. 2020 Feb 25;58(3):375-383.
  3. Hayakawa M, Imai T, Kawazoe Y, Kozaki K, Ohe K. Auto-Generated Physiological Chain Data for an Ontological Framework for Pharmacology and Mechanism of Action to Determine Suspected Drugs in Cases of Dysuria. Drug Saf. 2019 Sep;42(9):1055-1069.
  4. Kagawa R, Shinohara E, Imai T, Kawazoe Y, Ohe K. Bias of Inaccurate Disease Mentions in Electronic Health Record-based Phenotyping. Int J Med Inform. 2019 Apr;124:90-96.
  5. Ishihara S, Fujiu K, Imai T. An analysis of one-shot screening methods of ECG with different types of 2-D CNN. Journal of Neuroscience and Biomedical Engineering, 2019, 1(1): 1-9.
  6. Ma X, Imai T, Shinohara E, Sakurai R, Kozaki K, Ohe K. A Semi-Automatic Framework to Identify Abnormal States in EHR Narratives. Stud Health Technol Inform. 2017;245:910-914.
  7. Kagawa R, Kawazoe Y, Shinohara E, Imai T, Ohe K. The Impact of “Possible Patients” on Phenotyping Algorithms: Electronic Phenotype Algorithms Can Only Be Reproduced by Sharing Detailed Annotation Criteria. Stud Health Technol Inform. 2017;245:432-436.
  8. Iwai S, Kawazoe Y, Imai T, Ohe K. Effects of Implementing a Tree Model of Diagnosis into a Bayesian Diagnostic Inference System. Stud Health Technol Inform. 2017;245:882-886.
  9. Kozaki K, Yamagata Y, Mizoguchi R, Imai T, Ohe K. Disease Compass- a navigation system for disease knowledge based on ontology and linked data techniques. J Biomed Semantics. 2017 Jun 19;8(1):22. doi: 10.1186/s13326-017-0132-2.
  10. Imai T, Shinohara E, Kajino M, Sakurai R, Ohe K, Kozaki K, Mizoguchi R. An Ontological Framework for Representing Topological Information in Human Anatomy. In Proc. of International Conference on Biomedical Ontology and BioCreative (ICBO-BioCreative 2016), Corvallis, USA, August 1-4, 2016. CEUR Workshop Proceedings, ISSN 1613-0073, available online at, 2016.
  11. Kagawa R, Kawazoe Y, Ida Y, Shinohara E, Tanaka K, Imai T, Ohe K. Development of Type 2 Diabetes Mellitus Phenotyping Framework Using Expert Knowledge and Machine Learning Approach. J Diabetes Sci Technol. 2017 Jul;11(4):791-799. doi: 10.1177/1932296816681584. Epub 2016 Dec 7.

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