首页|Second Hospital of Jilin University Reports Findings in Personalized Medicine (D evelopment of a COVID-19 early risk assessment system based on multiple machine learning algorithms and routine blood tests: a real-world study)
Second Hospital of Jilin University Reports Findings in Personalized Medicine (D evelopment of a COVID-19 early risk assessment system based on multiple machine learning algorithms and routine blood tests: a real-world study)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News-New research on Drugs and Therapies - Personalize d Medicine is the subject of a report. According to news originating from Jilin, People's Republic of China, by NewsRx correspondents, research stated, "During the Coronavirus Disease 2019 (COVID-19) epidemic, the massive spread of the dise ase has placed an enormous burden on the world's healthcare and economy. The ear ly risk assessment system based on a variety of machine learning (ML) algorithms may be able to provide more accurate advice on the classification of COVID-19 p atients, offering predictive, preventive, and personalized medicine (PPPM) solut ions in the future." Our news journalists obtained a quote from the research from the Second Hospital of Jilin University, "In this retrospective study, we divided a portion of the data into training and validation cohorts in a 7:3 ratio and established a model based on a combination of two ML algorithms first. Then, we used another portio n of the data as an independent testing cohort to determine the most accurate an d stable model and compared it with other scoring systems. Finally, patients wer e categorized according to risk scores and then the correlation between their cl inical data and risk scores was studied. The elderly accounted for the majority of hospitalized patients with COVID-19. The C-index of the model constructed by combining the stepcox[both] and survivalSV M algorithms was 0.840 in the training cohort and 0.815 in the validation cohort , which was calculated to have the highest C-index in the testing cohort compare d to the other 119 ML model combinations. Compared with current scoring systems, including the CURB-65 and several reported prognosis models previously, our mod el had the highest AUC value of 0.778, representing an even higher predictive pe rformance. In addition, the model's AUC values for specific time intervals, incl uding days 7,14 and 28, demonstrate excellent predictive performance. Most impor tantly, we stratified patients according to the model's risk score and demonstra ted a difference in survival status between the high-risk, median-risk, and low- risk groups, which means a new and stable risk assessment system was built. Fina lly, we found that COVID-19 patients with a history of cerebral infarction had a significantly higher risk of death. This novel risk assessment system is highly accurate in predicting the prognosis of patients with COVID-19, especially elde rly patients with COVID-19, and can be well applied within the PPPM framework."
JilinPeople's Republic of ChinaAsiaAlgorithmsCOVID-19CoronavirusCyborgsDrugs and TherapiesEmerging Techn ologiesEpidemicEpidemiologyMachine LearningPersonalized MedicinePerson alized TherapyRNA VirusesRisk and PreventionSARS-CoV-2Severe Acute Respi ratory Syndrome Coronavirus 2ViralVirology
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