What can I do in 10 minutes? At best, it only takes time to read a few news. For Watson, he can diagnose a patient. Watson's cognitive computing capabilities are already familiar to us, and it is also a constant force in the field of medicine. A few days ago, the University of Tokyo Medical Institute used Watson to judge a woman with rare leukemia, which took only 10 minutes. The patient, a 60-year-old woman, initially showed her acute leukemia based on the diagnosis. However, after experiencing various therapies, the effect is not obvious. According to Arinobu Tojo, a researcher at the Toka Medical School, they use the Watson system to diagnose this patient. The system reached a diagnosis in 10 minutes by matching 20 million cancer research papers: The patient had a rare leukemia. The research was jointly completed by the IBM Institute in the United States, the New York Genomics Center and the East University Medical Institute. Hsnewsbeat Lin Xueting, a software engineer at the Watson Health Cloud of the Tokyo System and Software Development Institute, told Lei Fengwang (searching for "Lei Feng Net" public number) that the current medical difficulties are more uniform. First of all, you need to have comparable data. In this project, you work with the New York Gene Center. Second, when the data is used, the third party can only use the statistical information of personal data according to the HIPPA protocol; Moreover, it may be how to export the data as a sample of this study. This is also very troublesome, because the genetic data is very large. Kang Chaozi, CEO of the artificial intelligence diagnostic intelligence map, Zhang Chao, a senior research and development engineer at the former Baidu Natural Language Processing Department and head of the text knowledge excavation, also cited the main difficulties of this study. "Data extraction is a very threshold technology", mainly reflected in four aspects: 1. Compared to structured or semi-structured extraction, unstructured extraction is faced with more challenges. For example, extraction of template learning is more complicated, semantic transfer of extraction process, and many ambiguities and boundary problems need to be handled; 2. The data sources faced by unstructured extraction are more complicated, such as web pages, articles, books, question and answer data, etc. The data cleaning work brought by different data sources is not the same; 3. In the medical application scenario, the accuracy and recall rate of the required extraction work needs to be high, which is also a major challenge in extracting tasks; 4. The process of unstructured text extraction is accompanied by a large number of calculations, and there are also high requirements for computational performance. The "10 minutes" mentioned in the news, in Lin Xueting's view, "should not include the time to export the data." Zhang Chao also affirmed this point. "This 10 minutes should be used for matching searches." In other words, this process must first construct the structured knowledge and it must be completed offline. In this case, the relevant data of the gene center was conducted in a data pool and content management was performed. What Watson did within 10 minutes was to compare the data that had been screened and find similar items. “So 20 million papers in the article should be used after offline extraction; there is also a possibility to use this 20 million papers to adjust the original model.†In February 2011, Watson defeated human opponents on the intellectual show “The Edge of Danger†and used natural language to implement in-depth question and answer, demonstrating its strong learning ability. And Watson's cooperation in medical institutions also helps medical researchers in the continuous application of cognitive computing. Watson Health was founded in April 2015 and sounded Watson's quest to enter the medical industry. Last July, Watson worked with CVS, the second-largest chain pharmacy in the United States, to analyze user behavior and indicators and predict their health status. In the first phase of the cooperation, CVS mainly opened the user's behavior information, clinical data, drug purchase data, and insurance information to Watson. In August, IBM also acquired Merge, a medical imaging company. Together with Watson's cognitive learning capabilities, IBM was able to link medical imaging, diagnostics, and medication plans. The in-depth interpretation of medical imaging became the core strength of Watson. Except, IBM has also established cooperative relationships with companies such as Apple and Medtronic. Watson's application in medical research is not limited to the field of diagnosis. He has already achieved results in reading cases, reading papers, and looking for medications to treat diseases. Previously, IBM and the American Cancer Society (AACR) established cooperation. It is speculated that this measure may be to obtain patient statistics, but Watson's success in treatment should be the first case. Zhang Chao’s confidence in IBM Watson is relatively sufficient. He believes that as long as there is enough data, it can be applied on a large scale in the oncology field. "In the dimension of memory, machines are more powerful than humans; as long as enough knowledge is given to machines, machines can replace people to search for various possibilities, and finally assist doctors." According to Lin Xueting's statement to Lei Fengwang, Watson Health Cloud will put this case as a successful application on the medical cloud, which means it may become a large-scale application. “But I think that the actual implementation of the cloud can be used, which means that hospitals can be applied directly to the diagnostic field for two to three years. Before that, they were all research results.†Lin Xueting also pointed out that currently all projects, the cited data are from the United States, Japan has no relevant third-party regulations for the use of medical data, "it is said (Japan) will re-propose legislation next year." Although we have already seen IBM's success in the medical field, but to truly enter our daily diagnosis, not only does it require constant optimization of technology, it also needs to catch up in legal terms. General Dry Herb Vaporizers,Professional Desktop Evaporator,Low Budget Concentrate Vaporiser,Compact Concentrate Vaporiser END GAME LABS , https://www.eglvape.com