Machine learning becomes the behind-the-scenes hero of precision medicine
Artificial intelligence (AI) is gradually emerging in all walks of life. Computers can think and learn like humans, also known as machine learning, and have great potential for development in the medical field. It even plays a key role in the era of consumerization, improving the entire medical system by improving the medical experience of patients and doctors.
According to reports, we are in a value-based care system, we must try to gain insights from the ever-increasing information. At this time, we must use machine learning technology. Unfortunately, most electronic medical records (EHR) still use technology 20 to 30 years ago.
Electronic medical records not only store data, but also provide the right information at the right time to reduce the time required to prepare documents. Thanks to machine learning, electronic medical records have the ability to learn, and can be adjusted according to each user, thus maximizing efficiency. Machine learning is also a behind-the-scenes hero of precision medicine, taking into account the unique genetic makeup, environmental factors, lifestyle patterns and family history of each patient, thereby improving the accuracy of diagnosis and providing personalized disease treatment and prevention programs. painting.
The medical industry is expected to make better use of new genetic data models and to change the way of care for various conditions, from cancer, hyperlipidemia, diabetes, kidney disease to neurodevelopmental disorders. The ultimate goal is to improve diagnosis and treatment. Accuracy and effectiveness, while sharing knowledge with research communities and pharmacogenomics scholars. Machine learning also helps analyze patient data, turn data into insights and actionable information, and now compares risk factors such as blood sugar, BMI, and age to analyze treatment outcomes. When doctors can design treatment plans for individual patients, You can refer to other similar patients, confirm in advance whether the treatment will be effective, and further develop algorithms to identify or predict risk groups in the future.
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