100% "conquering" certain cancers is no longer an unreachable dream, and artificial intelligence will revolutionize the way the pharmaceutical industry works. Suppose you click "OK", the system is only 1 time out of 5 times, and the remaining 4 times pop up the window "Please try again", what would you feel? But just as you can't stop a person on the side of the road and let him run for 100 seconds. The doctor has to keep up with the speed of medical development today. It takes at least 160 hours a week to read the newly published literature, and only 168 hours a week. . Some things can't be done by manpower alone. As a result, MSKCC (Catherine Cancer Center) collaborated with IBM to leverage the power of supercomputer IBM Watson to absorb knowledge and extend the limits of humanity: IBM Watson is able to scan terabytes of medical data per second, and “unforgettableâ€, Combine powerful machine learning algorithms to derive insights from a large number of structured and unstructured data to help doctors make more personalized treatments. In Forbes' report, doctors at Wellpoint, a medical institution with IBM Watson, said that IBM Watson had a 90% chance of correcting lung cancer in the trial, compared with only 50% of human doctors. Doctors at ClevelandClinic, a hospital that uses IBM Watson to participate in clinical teaching, are often amazed at the recommended treatment options given by IBM Watson: "Why didn't I think of that point?!" Let the machine find the law from the data Although "the rapid development of science and technology" has become a commonplace, cancer treatment is still a field full of myths. At present, cancers that people often mention and fear include: "lung cancer", "breast cancer" and the like. With the help of bioinformatics technologies such as genetic testing, we know that the so-called "lung cancer", "kidney cancer" and "breast cancer" are actually a general term for thousands of different cell mutation patterns or misalignment. According to a 2015 paper on kidney cancer, just as there are no two identical leaves in the world, there are no two identical tumors on the earth; and studies have found that there are no two in the same tumor as a person. Cells are genetically identical. Knowing this is critical to cancer treatment. At present, cancer diagnosis is almost entirely "look": human doctors look at cells or tissue samples under a microscope, and sometimes some test results on cellular DNA, RNA or protein can help doctors make judgments - but in the end, rely on " Look". According to media reports, in 2014, the mortality rate of lung cancer, breast cancer and skin cancer in the United States was nearly 40%, so for cancer, early detection and early treatment is very important. The emergence of targeted drugs and therapies has greatly improved the medical situation of cancer. The so-called "targeted therapy" is the development of "targeted drugs" for cells containing mutations in certain cancer-related genes, either killing them or making them unreplicable. However, according to statistics, the current cancer drug treatment is effective - that is, the tumor volume is significantly reduced - the proportion is only 22%. In other words, how to "target", when the number of targets is too much, the treatment is beyond the reach. How do you find patterns and find patterns from a lot of messy information? It is easy to think of using the computational power of the machine to analyze and predict medical data. In 1991, a simple Bayesian QMR model for medical diagnosis was proposed. In 1994, the American Society for Medical Information (JAMIA) published a research article entitled "Medical Diagnostic Decision Support Systems: Past, Present, and Future." In 2005, the British Medical Journal (BMJ) published a paper entitled "Improving Clinical Practice with Clinical Decision Support Systems." In recent years, with the improvement of computing power and the development of related medical big data , more and more doctors have begun to use machine learning to help diagnose cancer. Machine learning algorithms use a variety of statistics, probabilities, and optimization methods to let computers "learn" from input and identify patterns that are difficult for humans to identify from massive unstructured data. In addition to cancer diagnosis, machine learning can also be used to judge cancer prognosis and recurrence. There are three main types of machine learning algorithms used in cancer diagnosis: supervised learning, unsupervised learning, and reinforcement learning. In the case of supervised learning, cancer diagnosis can be simplified as “classificationâ€: the model divides the input data into several categories based on the training results. Assuming that data related to breast cancer is collected and the tumor is judged to be benign or malignant depending on the size of the tumor, the question of questioning the machine learning model becomes the probability that the tumor is benign (or malignant), as shown below.
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