{"id":145,"date":"2023-06-08T13:36:00","date_gmt":"2023-06-08T04:36:00","guid":{"rendered":"https:\/\/en.mdv.co.jp\/news\/?p=145"},"modified":"2025-08-05T15:28:17","modified_gmt":"2025-08-05T06:28:17","slug":"idea-for-utilization-of-mdv-data-wins-first-prize-in-data-science-contest","status":"publish","type":"post","link":"https:\/\/en.mdv.co.jp\/news\/2023\/article-0608.html","title":{"rendered":"Idea for utilization of MDV data wins first prize in data science contest"},"content":{"rendered":"\n

Medical Data Vision Co., Ltd. (Headquarters: Chiyoda-ku, Tokyo; President and CEO: Hiroyuki Iwasaki; hereinafter referred to as MDV) is pleased to announce that a research team utilizing MDV\u2019s medical data has won the grand prize in a contest that showcased the skills of data scientists.<\/p>\n\n\n\n

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The contest, titled \u201cRising Future Data Science Contest,\u201d was organized by Snowflake Inc. (Headquarters: Shibuya-ku, Tokyo; President and Executive Officer: Hidetoshi Tojo), a data cloud provider. Participants were required to analyze and combine data provided by seven data providers, including MDV, and propose ideas and business plans for data utilization. The contest consisted of two categories: the Idea Division for individuals without prior experience in data science and students, and the Data Science Division for experienced data scientists and students. A total of 29 teams and 450 individuals submitted their entries.<\/p>\n<\/div>\n\n\n\n

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During the finalist announcement event held on May 31st, two teams from the Idea Division and three teams from the Data Science Division were selected to present their ideas. The team of Naomi Hayakawa and Takeshi Masuda, who utilized MDV\u2019s data, presented their proposal on overcoming the challenges of an aging population and a decline in the working-age population under the theme of \u201cOvercoming a Declining Workforce Society.\u201d They started their data analysis six months ago and presented the following two analytical results: 1) Extension of Healthy Lifespan and 2) Prediction of Medical Demand. As a result, they were awarded the grand prize in the Data Science Division.<\/p>\n\n\n\n

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1. Extension of Healthy Lifespan<\/h3>\n\n\n\n

By combining the National Consumer Purchasing Data (SCI) from Intage Corporation and MDV\u2019s medical institution visit dynamics data, they predicted \u201cwhat people eat and the diseases they are likely to develop.\u201d They selected the top 10 diseases with high risks based on factors such as the number of patients and created machine learning models for indicators of caution (high patient visit rate and high food purchase amount) and preventive measures (low patient visit rate and low food purchase amount). For example, cheese and canned meat were identified as caution indicators for diabetes, while soy milk, natto (fermented soybeans), and rice were identified as preventive indicators. The study revealed significant relationships between diet and diseases.

The application of predicting disease risks from shopping receipts, providing food advice for risk reduction, and combining with medical examination data holds the potential for more personalized risk analysis and food recommendations.<\/p>\n\n\n\n

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2. Prediction of Medical Demand<\/h3>\n\n\n\n

By combining weather data from Weathernews Inc., time-series data from truestar Inc., and MDV\u2019s medical institution visit dynamics data, they predicted \u201cdaily patient numbers.\u201d They classified the trends in outpatient numbers for each disease into four categories: \u201cincrease during summer,\u201d \u201cincrease during winter,\u201d \u201cno variation,\u201d and \u201caffected by COVID-19.\u201d They then identified the correlation between weather data and patient numbers. For example, the study showed that the number of influenza patients increases when the average temperature falls below 10\u2103 or when the absolute humidity drops below 10.
The team demonstrated the creation of machine learning models capable of predicting outpatient numbers and epidemic periods, such as infectious diseases.

By predicting the occurrence risks and patient numbers of specific diseases based on weather forecasts, the data can be used to allocate medical resources appropriately, provide alerts for local epidemics of seasonal diseases and infections, and contribute to effective solutions.<\/p>\n\n\n\n

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Furthermore, MDV\u2019s data was utilized by three out of the five finalist teams, including the team of Hayakawa and Masuda.<\/p>\n","protected":false},"excerpt":{"rendered":"

Medical Data Vision Co., Ltd. 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