New coronal pneumonia patient outcome prediction method based on interpretable machine learning algorithm
The invention provides a new coronal pneumonia patient outcome prediction method based on an interpretable machine learning algorithm, wherein the method comprises the steps: extracting COVID-19 patient data from a database, and dividing the patient data into an experimental group and a control group according to the illness state conversion condition of a patient; interpolating the missing value of each index through random forest regression; screening the indexes of the input model, and taking the screened indexes as key risk factors for identifying the deterioration of the patient; inputting the key risk factors of the patient into the XGBoost model and the logistic regression model; selecting an XGBoost model with better prediction expressive force to generate an index combination, and performing prediction by using the XGBoost model and recording the prediction result; defining the early warning range of the key index; when the key risk index of the patient enters the early warning range, giving out an alarm prompt to medical staff. According to the invention, the calculation result of the algorithm and the clinical experience of a doctor are synthesized, and two index combinations composed of 15 first groups of indexes and 5 second groups of indexes are proposed to be used for predicting the condition of the new coronal pneumonia patient..
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Patent| Erscheinungsjahr: |
2021 |
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| Erschienen: |
2021 |
| Enthalten in: |
Europäisches Patentamt - (2021) vom: 10. Dez. Zur Gesamtaufnahme - year:2021 |
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| Sprache: |
Englisch |
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| Beteiligte Personen: |
JIA LIJING [VerfasserIn] |
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| Links: |
Volltext [kostenfrei] |
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| Anmerkungen: |
Source: www.epo.org (no modifications made), First posted: 2021-12-10, Last update posted on www.tib.eu: 2022-11-05, Last updated: 2023-02-09 |
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| Patentnummer: |
CN113782197 |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
EPA015186652 |
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| 245 | 1 | 0 | |a New coronal pneumonia patient outcome prediction method based on interpretable machine learning algorithm |
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| 520 | |a The invention provides a new coronal pneumonia patient outcome prediction method based on an interpretable machine learning algorithm, wherein the method comprises the steps: extracting COVID-19 patient data from a database, and dividing the patient data into an experimental group and a control group according to the illness state conversion condition of a patient; interpolating the missing value of each index through random forest regression; screening the indexes of the input model, and taking the screened indexes as key risk factors for identifying the deterioration of the patient; inputting the key risk factors of the patient into the XGBoost model and the logistic regression model; selecting an XGBoost model with better prediction expressive force to generate an index combination, and performing prediction by using the XGBoost model and recording the prediction result; defining the early warning range of the key index; when the key risk index of the patient enters the early warning range, giving out an alarm prompt to medical staff. According to the invention, the calculation result of the algorithm and the clinical experience of a doctor are synthesized, and two index combinations composed of 15 first groups of indexes and 5 second groups of indexes are proposed to be used for predicting the condition of the new coronal pneumonia patient. | ||
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| 700 | 0 | |a WEI ZIJIAN |4 aut | |
| 700 | 0 | |a WANG JIAMING |4 aut | |
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| 700 | 0 | |a LI XIUCHENG |4 aut | |
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