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Real-World Information, Health and Medicine Lab
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The lab analyzes big medical data to predict and prevent disease.
AboutAbout the lab
Exploring disease risk from big medical data
The Real-World Information, Health and Medicine Lab uses large-scale medical and health data, such as genome information, insurance claims, and electronic medical records, to search for risk factors that lead to disease onset and progression to severe illness.
By combining data science, biostatistics, and epidemiology, the lab is also developing algorithms that predict onset and progression to severe illness.
Based on these results, the lab aims to advance medicine and health sciences by proposing optimal prevention and intervention measures.
Through its research, the lab also works to train data scientists in medicine and health who can handle big data.
Key terms
- Omics data
- Biological information obtained comprehensively from blood or organs. Examples include DNA methylation, miRNA, and proteins
- Real-world data
- Data accumulated in real-world healthcare settings, such as insurance claims and electronic medical records
- Single nucleotide polymorphism (SNP)
- A type of genomic data used to investigate the genetic factors behind individual differences in constitution
NewsLab news
An Assistant Professor received the Encouragement Award at the 36th Annual Meeting of the Japanese Society on Urolithiasis Research
A paper from a joint study on blood proteins in pancreatic cancer was published in International Journal of Cancer
A graduate student received the Excellent Poster Award and other awards at the 5th Health Science Research Meeting
A paper written by lab staff was published in Cancer Science
A doctoral student received the Top Prize of the Medical Encouragement Award from the Graduate School of Medicine
ResearchResearch themes
In recent years, large and diverse medical big data have accumulated, including omics data such as genomes and real-world data such as insurance claims and electronic medical records. Using data science, biostatistics, epidemiology, and other approaches, the lab analyzes these data from multiple angles. It searches for risk factors, develops algorithms that predict onset and progression to severe illness, and proposes optimal prevention and intervention measures.
*Illustrative imageGenomic data analysis of diseases and physical constitution
In collaboration with research institutions in Japan and abroad, the lab explores the genetic factors behind individual differences in constitution, using genomic data such as single nucleotide polymorphisms (SNPs) and copy number variants. It is also developing models that predict the risk of disease onset from genetic information.
*Illustrative imageDiagnosis and prediction using blood omics data
The lab obtains omics data, such as DNA methylation, miRNA, and proteins, from the blood of community residents. It is identifying factors that could lead to early diagnosis and onset prediction of conditions such as lifestyle-related diseases and amyotrophic lateral sclerosis (ALS), and is developing prediction models.
*Illustrative imagePredicting early signs of disease onset from health checkup data
Using health checkup data and genomic data accumulated from residents of the Iwaki district of Hirosaki, Aomori Prefecture, the lab searches for risk factors for the onset of lifestyle-related diseases and mental illness. It also develops prediction models that detect early signs of onset, and is working on designing lifestyles based on big data.
*Illustrative imagePrognosis prediction for newborns
If the risk of death in preterm infants can be accurately predicted before birth, high-risk preterm infants can be identified and connected to early neonatal care. Using data from Japanese newborns and their mothers, the lab is developing an algorithm that uses machine learning and AI to identify high-risk preterm infants.
MembersFaculty
The members of the lab consist of faculty, doctoral, master's, and undergraduate students, and technical assistants.As of 2026
- Associate ProfessorMasahiro Nakatochi
- Assistant ProfessorYuki Ohashi
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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: “COMPUTER ROOM DISCHARGE” Keith4048 / Public domain (cropped)
- Genomic data analysis of diseases and physical constitution: “Illumina NextSeq 500” Konrad Förstner / CC BY 2.0 (cropped)
- Diagnosis and prediction using blood omics data: “Bruker HCTultra mass spectrometer June2007” Mwillett4000 / Public domain (cropped)
- Predicting early signs of disease onset from health checkup data: “Community health fair improves healthcare accessibility on St Kitts, Nevis (8978314)” U.S. Air Force photo by Maj. Stephani Schafer / Public domain (cropped)
- Prognosis prediction for newborns: “UTI Neonatal - Hospital Mário Dourado Sobrinho - Irecê - BA” Manu Dias / AGECOM / CC BY 2.0 (cropped)
- Related lab (Life and Human Information, Health and Medicine Lab): “Janet Thornton giving a talk at the European Conference on Computational Biology 2012 in Basel” University of Basel / CC BY 3.0 (cropped)
- Related lab (Advanced Measurement Information, Health and Medicine Lab): “MRI scanner at the Royal Stoke University Hospital - geograph.org.uk - 4276444” Jonathan Hutchins / CC BY-SA 2.0 (cropped)
- Related lab (Cellular Genetics and Information Science Lab): “Test tubes in rack” J.N. Eskra / CC BY-SA 4.0 (cropped)
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