Intelligent cancer diagnosis and treatment decision-making system capable of slicing according to medical record time

Abstract

The invention discloses an intelligent cancer diagnosis and treatment decision-making system capable of slicing according to medical record time in the technical field of cancer diagnosis and treatment decision-making systems, therapeutic schedule record libraries and time axes are in one-to-one mapping, the output of the time axes is connected with a time slice division module, and the output of a decision-making engine is connected with an AI model. The output of the AI model is connected with an AI decision treatment scheme, the output of the AI decision treatment scheme is connected with a decision calibration verification module, the output of the single chip microcomputer is connected with a real treatment scheme, and the output of the real treatment scheme is connected with the decision calibration verification module, so that on one hand, the correctness of the scheme issued by the decision engine can be verified; and on the other hand, a decision engine can be trained in reverse after a proper sample is selected through manual intervention, and a short treatment path case model obtained after the long treatment path is sliced according to time can be used for predicting whether patients which are treated in an early stage evolve towards an expected direction or not according to a specific scheme.

Description

technical field [0001] The invention relates to the technical field of cancer diagnosis and treatment decision-making system, in particular to an intelligent cancer diagnosis and treatment decision-making system that can be sliced ​​according to the time of medical records. Background technique [0002] Before the hospital performs surgery on the patient, the nursing staff related to the surgery need to conduct a preoperative visit to the patient, the purpose of which is to understand the patient's situation and communicate with the patient to eliminate the anxiety and Fear, in order to improve the cooperation between nurses and patients in the operation, it is beneficial to increase the patient's tolerance to surgery, shorten the operation time, reduce the pain of patients and ensure the smooth progress of the operation, and in the actual diagnosis and treatment process, the doctor A complete set of treatment plan will be formulated for the patient. The patient and his fami...

AI Technical Summary

Problems solved by technology

[0002] Before the hospital performs surgery on the patient, the nursing staff related to the surgery need to conduct a preoperative visit to the patient, the purpose of which is to understand the patient's situation and communicate with the patient to eliminate the anxiety and Fear, in order to improve the cooperation between nurses and patients in the operation, it is beneficial to increase the patient's tolerance to surgery, shorten the operation time, reduce the pain of patients and ensure the smooth progress of the operation, and in the actual diagnosis and treatment process, the doctor A complete set of treatment plan will be formulated for the patient. The patient and his family members lack medical knowledge and cannot understand the meaning of decision-making. Doctors often do not have the energy or do not explain well enough. There is no effective communication channel between doctors and patients. Treatment plans are answered, and patients and their families often lack direction and purpose when they turn to traditional search engines for inquiries

[0003] Compared with other diseases, cancer is not only more difficult to treat, but also has a relatively longer treatment cycle. A patient who relapses from diagnosis to treatment, evaluation, and relapse may undergo several repeated treatments and pathological information collection. All these medical record data All can be organized into a time-ordered structured case model for real-world research. Existing patients will have their own medical record information. The knowledge and experience of the doctor will come up with a set of treatment plan in real time, but the doctor himself has limitations, so the treatment plan given may not be the most effective and accurate treatment plan, which greatly reduces the feasibility of the decision-making plan. To this end, we propose an intelligent cancer diagnosis and treatment decision system that can be sliced ​​according to the time of medical records

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