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British research and development "artificial intelligence clinician" helps improve the survival rate of patients with sepsis
Medical Network October 26, October 22, Imperial College of Technology said on the 22nd that the university's researchers led the development of an artificial intelligence system that can help human doctors better make sepsis treatment decisions, thereby improving patients with sepsis Survival rate.
Sepsis is a life-threatening disease in which the body's body reacts extremely to inflammatory infections, causing damage to tissues and organs. The key to treating sepsis is the correct administration of injections and drugs to help patients maintain blood pressure stability, but current clinical practice still has some challenges in this regard.
In order to better treat sepsis, the team led by Imperial College Scholars developed an “artificial intelligence clinician†system and used it to analyze the medical records of 96,000 septic patients in the United States, using an important machine learning process. Types - Reinforced learning, which allows artificial intelligence systems to quickly learn how to make decisions and solve problems. Relevant results have been published in the British journal Nature Medicine.
Researchers say that artificial intelligence clinicians can extract the most useful information from massive data and predict the best treatment strategy for each patient. In 98% of cases, the treatment decisions given by “artificial intelligence clinicians†reached or exceeded the level of human doctors.
One of the authors of the report, Dr. Aldo Faisal of Imperial College, said: "Our new artificial intelligence system can analyze the patient's blood pressure, heart rate and other data to give the best treatment strategy. We found that when it is a human doctor The patient has the best survival rate when the treatment strategy used is consistent with the AI ​​recommendations."
The researchers point out that even if the mortality rate of sepsis is only slightly reduced, it can save a lot of life on a global scale every year.