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Emergency and Critical Care Medicine Lab
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A lab that tackles questions from emergency and intensive care settings using data and genetics.

AboutAbout the lab
Joyfully creating the future of emergency and intensive care.
The Emergency and Intensive Care Medicine Lab studies a wide range of problems related to emergency and intensive care, aiming to apply the results in clinical practice.
At Chiba University Hospital, the emergency and intensive care departments handle patients continuously, from prehospital emergency care and initial treatment in the emergency department to treatment in the intensive care unit.
The main research themes are genetic polymorphisms and functional analysis in sepsis, increased vascular permeability, and the development of artificial supportive therapies.
The lab also develops ICT systems and biometric devices to address challenges in emergency and disaster medicine, as well as AI-based prediction algorithms and diagnostic imaging support tools.
Key terms
- Sepsis
- A condition in which an infection triggers a strong systemic reaction that impairs organ function
- Mendelian randomization
- A statistical method that uses the fact that genes are randomly passed on from parents to infer cause-and-effect relationships
- Rapid Response System
- A system that detects signs of sudden deterioration in hospitalized patients and sends a specialized team to respond
NewsLab news
A paper examining changes in energy metabolism in critically ill patients was published in Clinical Nutrition
An Assistant Professor received the top prize at the Roche Infectious Disease Award 2025
A paper by a graduate student was published in JMIR Formative Research
The lab regularly holds case-review study sessions for medical and nursing students
A paper was published in the Journal of the Japanese Association for Acute Medicine
ResearchResearch themes
The lab studies a wide range of problems related to emergency and intensive care, with the aim of applying the results in clinical practice. From genetic analysis of sepsis to the operation of emergency medical services and AI-based prediction, it answers questions that arise in clinical settings with data.
*Illustrative imageExploring the genetic predisposition to sepsis
Studies have shown that mortality in sepsis has a strong genetic component. By analyzing specimens from patients with septic shock collected at three intensive care units in Chiba Prefecture, the lab uses Mendelian randomization to clarify the causal relationship between genetic predisposition and prognosis.
*Illustrative imageEmergency dispatch and response time
As the time from an emergency call to arrival at the scene gets longer, patient outcomes worsen. By analyzing emergency dispatch data from Chiba City, the lab showed that an increase in the number of dispatches correlates with longer ambulance travel distances and longer response times.
*Illustrative imageAI-assisted literature screening
Systematic reviews for developing clinical practice guidelines require a great deal of effort and time. The lab showed that large language models such as ChatGPT can identify the necessary literature with high accuracy and can greatly shorten the time spent on searching.
*Illustrative imagePredicting outcomes for patients who suddenly deteriorate
In a Rapid Response System, which responds to sudden deterioration of hospitalized patients, predicting patient outcomes is important. By applying machine learning to multicenter nationwide registry data, the lab developed an algorithm that predicts 30-day mortality and identified important factors affecting prognosis.
MembersFaculty
The members of the Emergency and Intensive Care Medicine Lab are faculty, hospital physicians, specialty residents, and physicians seconded to affiliated hospitals.
- ProfessorTakaaki Nakada
- LecturerNoriyuki Hattori
- LecturerTakehiko Oami
- LecturerKeisuke Tomita
- Assistant ProfessorTaku Omura
- Assistant ProfessorSatoshi Sunahara
- Assistant ProfessorTaro Imaeda
- Assistant ProfessorNatsumi Suga
- Assistant ProfessorSatoshi Karasawa
- Assistant ProfessorTakashi Shimazui
- Assistant ProfessorNozomi Takahashi
- Assistant ProfessorAkiko Higashi
- Project Associate ProfessorTaku Oshima
- Project Assistant ProfessorTakeo Kurita
- Project Assistant ProfessorShigenobu Ishida
- Project Assistant ProfessorNatsuko Ishikawa
- Project Assistant ProfessorYosuke Matsumura
- Project Assistant ProfessorDaiki Saito
VoicesStudent voices
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Report an errorAbout the images
The top and research theme images are illustrative and were not taken at the lab. They are cropped from Wikimedia Commons images. Cropped CC BY-SA images are provided under the same CC BY-SA license as the original (version shown on each line).
- Top image: “20250808 Intensive care unit bed” Ismael Olea / CC BY 4.0 (cropped)
- Exploring the genetic predisposition to sepsis: “MegaBACE DNA sequencing instrument” Nels A. Olson / CC BY-SA 4.0 (cropped)
- Emergency dispatch and response time: “British Red Cross (BRC) Rapid Response Vehicle (RRV) Ambulance 1” Fbo194 / CC BY-SA 4.0 (cropped)
- AI-assisted literature screening: “PDC server room” Esquilo / CC BY-SA 3.0 (cropped)
- Predicting outcomes for patients who suddenly deteriorate: “Vital signs monitor display” Petty Officer 1st Class James Stenberg / Public domain (cropped)
- Related lab (Artificial Intelligence (AI) Medicine Lab): “Human interactome” Keiono / CC BY-SA 3.0 (cropped)
- Related lab (Diagnostic Reasoning Lab): “Middle-aged female doctor talking to a patient during a video consultation at home in a cozy setting” Shixart1985 / CC BY 2.0 (cropped)
- Related lab (Infectious Disease Pathogenesis Lab): “SARS-CoV-2 scanning electron microscope image” National Institute of Allergy and Infectious Diseases (NIAID) / CC BY 2.0 (cropped)
- Related lab (Tumor Pathology Lab): “Scanning electron microscope - UFCH JH (2020) 01” Tadeáš Bednarz / CC BY-SA 4.0 (cropped)
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