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Expectations and Challenges for Data Science in the Medical and Health Fields

It can be said that the fields of medicine and health are extremely important areas for the application of data science.

Whether in the East or the West, everyone wants to maintain their health as long as possible and live as long as possible.

In 1950, the average Japanese life expectancy was 58 years for men and 61.5 years for women. In 2000, it was 77.7 years for men and 84.6 years for women, and in 2019 the average life expectancy was 81.4 years for men and 87.4 years for women (announced by the Ministry of Health, Labor and Welfare on July 31, 2020).

Humankind’s insatiable desire for health and longevity has improved and developed medical care and health care. By developing more effective drugs and effective treatments, we can overcome diseases that were once thought to be incurable, and by developing test methods that detect signs of illness at an early stage, we can prevent the onset of illness. can be said to have been a result of steady achievements.

It goes without saying that data science has greatly contributed to the improvement and development of such medical care and health care. Rather, it is no exaggeration to say that data science is the only way to improve and develop the medical and health fields.

The pandemic of the new coronavirus, which has become a hot topic in recent years, is expected to come to an end as several vaccines are put into practical use and spread. Data science is also playing an active role in such vaccine development.

To tell the truth, in terms of the use of data science in the medical and health fields, Japan is a little behind, and compared to countries such as Israel and Sweden, which are said to be “advanced healthcare countries,” there is still a sense of “not yet.” I can’t deny it.

For example, if data science can be used to appropriately analyze the vast amount of medical receipt data held by health insurance associations and medical records held by medical institutions, drug discovery, diagnosis, treatment, and preventive medicine can be further improved and developed. It is possible to let

As a result, there are high expectations for the expansion of business opportunities in this field.

On the other hand, however, there are still many problems to be overcome, such as the lack of a unified data infrastructure and the fact that the digitization of medical records has just begun.

At AI Lab, many data scientists have been involved in data science projects in the medical and health fields, and we would like to further strengthen our involvement in the future.

The AI ​​Lab blog introduces some of my knowledge as a data scientist based on such experiences, but in this article, I will first take a bird’s-eye view of the expectations and challenges of data science in the medical and health fields. increase. It would be greatly appreciated if you read it as a prologue before going into individual specific blog articles, or as an epilogue after reading individual blogs.

Direction of data science utilization: application of statistics in the case of few statistics

In a nutshell, data science in the medical and health fields has a wide range of applications, from things that are almost unrelated to the daily lives of ordinary people, such as the “Human Genome Project,” to almost everyone’s daily life. It is also used for image diagnosis using AI, which has become a hot topic in recent years.

In the first place, there are two major directions in which data science can be used in the medical and health fields.

One is the direction of “application of statistics in the case of few statistics”. When trying to evaluate medical effects using statistics, for example, when conducting clinical trials on humans in the process of drug discovery, it is not possible to continue clinical trials without restrictions until efficacy and safety are confirmed. Therefore, it is necessary to analyze the minimum necessary clinical trial samples and confirm their efficacy and safety. Therefore, it is necessary to utilize data science called “application of statistics in the case of small statistics”.

The use of data science in this direction is diverse, including analysis of receipt data and analysis of electronic medical records. In addition, the most familiar example of the use of data science today is the various statistical data related to the corona that is released daily.

Direction of data science utilization of “processing large amounts of data with computer power”

Another direction for the use of data science in the medical and health fields is to “process large amounts of data with computer power.”

 On the other hand, “processing large amounts of data with computer power” can be said to be the direction of technology-oriented utilization. In other words, the direction is to make use of data science in medicine and health with the idea that “Since the processing power of computers has increased so much, it can be used in this way.” For example, we are heading in the direction of using AI to analyze X-ray images, CT images, MRI images, etc., and use them to detect malignant tumors.

Data science will revolutionize the medical and health fields

Whether it is needs-oriented or technology-oriented, there is no doubt that the use of data science will improve and develop the medical and health fields.

Sometimes it may be a big change that can be called a big bang rather than improvement and development from the current situation.

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