Artificial intelligence brings b convenience health management and genetic testing

Health management. Disease prevention can reduce the number of patients and save medical costs at the source. Health management is one of the most important ways of disease prevention. AI+health management can monitor human physiological indicators in real time through wearable devices or mobile devices, collect personal data such as genetic data, physiological data (e.g. blood pressure, pulse), environmental data (e.g. air breathed every day) and social data, etc. AI health management learns the physical characteristics of each person through data and designs a personalized health management plan for each person. Personalized health management program; able to quickly detect and respond when abnormalities occur in the body; able to provide health management tips and programs with long and uninterrupted monitoring. In addition to disease prevention, AI + health management can also monitor patients after illness and for rehabilitation scenarios, after they are discharged from the hospital, greatly reducing the possibility of recurrence of some diseases or the occurrence of subsequent complications and sequelae.

Health management with intelligent hardware can theoretically achieve comprehensive health management of the human body, but due to the current low level of development of sensors and hardware, as well as the lack of accumulation of relevant disease data, the health links involved are mainly risk identification, health assessment, mental monitoring, health intervention, etc.

(1) Risk identification, which identifies the risk of disease occurrence and provides measures to reduce the risk by acquiring information and analyzing it with artificial intelligence technology. Health assessment, which collects information on patients’ personal habits such as diet, exercise cycles, medication habits, etc., uses AI technology to analyze the data and assess the overall status of the patient, and helps plan the patient’s daily life. Mental monitoring, using AI technology to perform emotion recognition based on data such as language, expressions and voice. Health interventions, using AI to analyze user sign data and develop health management plans. According to the application of AI in different fields of health management, we divide the application of AI in health management into four subdivisions: population health management, maternal and child health management, chronic disease health management and mental health management.

Population health management. Population health is the main theme of the vision of “Healthy People 2020” [1], which is about health research and health service construction. The ultimate goal of population health management is to enable people to live longer and healthier lives, to prevent disease, and to avoid all forms of disability, injury, and premature death. Artificial intelligence has natural advantages for processing and analyzing massive data, which can better achieve better personalized medical and health services for the whole population, better medical and health development for social groups, and affordable medical expenses, so that people can live longer and healthier lives, better prevent diseases, and at the same time, avoid various forms of disabilities, injuries, and premature deaths, etc., and also provide solutions for medical institutions, governments, wearable device It also provides population health solutions for medical institutions, government, wearable device companies, etc.

Maternal and child health management. Artificial intelligence in the field of maternal and infant health can be divided into two aspects, on the one hand, data monitoring for women before and after conception, usually combined with intelligent hardware or wearable devices, to monitor individual physiological symptoms, emotional state, sleep and other data; on the other hand, intelligent Q&A for parenting knowledge, from healthy conception of a new life, to the birth and growth of a baby, as well as personal physical changes, psychological and emotional changes, parenting skills and various complex family problems. On the other hand, it is a wisdom quiz for parenting knowledge, from healthy nurturing of a new life to the birth and growth of a baby, as well as personal physical changes, psychological and emotional changes, parenting skills and various complex family problems.

Chronic Disease Health Management. Chronic disease health management in China, led by cardiovascular disease and diabetes, accounts for 85% of all deaths each year, and chronic diseases account for more than 70% of the disease burden in China, causing a great economic burden. For patients with chronic diseases, although medication can alleviate disease symptoms and slow down disease development to a certain extent, it is more important that they should change their unhealthy lifestyle habits and make reasonable planning and control of diet, exercise and work and rest. With the development of science and technology, chronic disease management for patients has gradually evolved from purely offline doctor-patient communication to a new chronic disease management model that combines online and offline. Artificial intelligence health management can help better integrate online and offline, monitor patients’ living habits, diet, exercise, work and rest and make recommendations such as early warning of risk factors and control programs.

Mental health management. According to the World Health Organization estimates, mental disorders account for 13% of the total number of diseases worldwide, and almost 1 in 10 people worldwide currently suffer from mental disorders, with 1 in 17 of them enduring serious mental disorders, which has led to suicide becoming the second leading cause of human death, with more than 800,000 people dying by suicide every year. More and more startups are focusing on this piece of mental illness management. Artificial intelligence health monitoring can determine whether the patient’s daily behavior habits suddenly change, and then pass to the responsible clinical medical team and relatives abnormal warning, notify the medical team and relatives to prepare for emergencies to avoid accidents.

Genetic testing. A blue ocean of genetic testing, genetic testing is a scan of DNA through body fluids or cells to interpret the body at a molecular level. Through genetic testing, people can find many hidden risks of disease under the healthy body, so that they can be avoided in advance. According to the American Cancer Society (ACS), cancer mortality in the United States has decreased by 22% over the past 20 years, which translates to 1.5 million cancer deaths averted, many of which have escaped the call of death through genetic testing.

The commercialization prospect of this technology was favored by many companies, and a lot of capital was already in the rush to land at that time. In recent years, along with the rise of genetic testing companies such as UW Genetics and WeGene, China’s genetic testing industry has also begun to appear in the public eye. In the United States, where genetic testing has become more common, 4 to 5 million people do genetic testing each year, while in China, the genetic testing industry, on behalf of the UW Gene July 14, 2017, the first day of listing to the “opening board”, a total of 19 one-word stop, the share price from the opening price of 16.37 yuan all the way up to “open board” when the 107.18 yuan, just 19 trading days rose more than 554.73%, the crazy degree can be seen.

Artificial intelligence + genetic testing. Genetic testing usually contains two aspects: gene sequencing and gene interpretation. In the process of genetic testing tends to be popular, the genetic interpretation is still a bottleneck that needs to be broken. The new generation of sequencing technology generates huge amounts of data and is growing at an exponential rate. If the information recorded in genes is considered as one-dimensional (e.g., disease phenotypes and sequenced gene sequences), the relationship between these data is two-dimensional and multidimensional, and the amount of information that may exist between the data is many orders of magnitude higher than the one-dimensional data itself. With the accumulation of data, accurate annotation and interpretation of the data and its clinical application become the key to the next step of development of the genetic industry. Analytical capabilities and large databases are the key to genetic interpretation and consultation, and the interpretation and integration of information become the core competencies of gene-related companies. Artificial intelligence, with its powerful data processing and learning capabilities, can better assist in gene sequence interpretation.

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