General anesthesia invasive systolic pressure prediction method and system based on machine learning fusion
The invention relates to a general anesthesia invasive systolic pressure prediction method and system based on machine learning fusion. The method comprises the following steps: S1, collecting the body feature data and vital sign data of a patient, carrying out the preprocessing of all data, and dividing the preprocessed data into a training set, a test set and a verification set; respectively performing slice sampling on the training set, the test set and the verification set to collect n training subsets, test subsets and verification subsets; s2, constructing a prediction model based on machine learning fusion, and predicting the general anesthesia invasive systolic pressure by using the prediction model; the prediction model comprises n primary learners and a secondary learner, and the output values of all the primary learners are used as the input of the secondary learner. The system comprises a database module, a data collection module, a prediction module and an interaction module. A prediction program is stored in the prediction module. According to the method, the defect of low prediction precision of a single algorithm is overcome, the input of the secondary learner is optimized, and the prediction precision is further improved..
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Patent| Erscheinungsjahr: |
2022 |
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| Erschienen: |
2022 |
| Enthalten in: |
Europäisches Patentamt - (2022) vom: 14. Jan. Zur Gesamtaufnahme - year:2022 |
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| Sprache: |
Englisch |
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| Beteiligte Personen: |
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| Links: |
Volltext [kostenfrei] |
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| Anmerkungen: |
Source: www.epo.org (no modifications made), First posted: 2022-01-14, Last update posted on www.tib.eu: 2022-08-30, Last updated: 2023-02-09 |
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| Patentnummer: |
CN113936801 |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
EPA013301837 |
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| 520 | |a The invention relates to a general anesthesia invasive systolic pressure prediction method and system based on machine learning fusion. The method comprises the following steps: S1, collecting the body feature data and vital sign data of a patient, carrying out the preprocessing of all data, and dividing the preprocessed data into a training set, a test set and a verification set; respectively performing slice sampling on the training set, the test set and the verification set to collect n training subsets, test subsets and verification subsets; s2, constructing a prediction model based on machine learning fusion, and predicting the general anesthesia invasive systolic pressure by using the prediction model; the prediction model comprises n primary learners and a secondary learner, and the output values of all the primary learners are used as the input of the secondary learner. The system comprises a database module, a data collection module, a prediction module and an interaction module. A prediction program is stored in the prediction module. According to the method, the defect of low prediction precision of a single algorithm is overcome, the input of the secondary learner is optimized, and the prediction precision is further improved. | ||
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