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[Li Ka SHING MONEY MANAGEMENT]Can machine learning to use conventional clinical data to improve cardiovascular risk prediction?

2021-06-25 15:48:06 

  Research Background

  Cardiovascular disease (CVD) is still the main cause of death in a global scale.2012,1.75 million people died in cardiovascular disease,Among them, 7.4 million people died in coronary heart disease.6.7 million people died in stroke[1].American Heart Association / USA Cardiology (ACC / AHA) According to established risk factors,Such as hypertension, cholesterol, age, smoking and diabetes,Assess the risk of cardiovascular disease.These risk factors have been included in most CVD risk prediction tools (ACC / AHA[2],QRISK2[3],FRAMINGHAM[4],REYNOLDS[5]).But we still have no accurate predict the risk of cardiovascular diseases facing patients and make corresponding preventive treatment.

  We analyze the models of the above forecast CVD,That is, it is assumed that each risk factor is linear with the CVD results[7].These models may excessively simplify complex relationships,These include a nonlinear relationship between a large risk factor.Therefore, we need to explore better methods to consider a variety of risk factors.And determine the subtle relationship between risk factors and results.

  Machine learning (ML) utilizes “Big Data” to perform mode identification and calculation learning to provide a standard predictive model to solve the above limitations.This depends on the computer by minimizing the error between the prediction and observation, learning the variables all complex and nonlinear interactions[8].In addition,ML may also recognize the potential variables inference from other variables.

  so far,There is no large-scale research application machine learning to make a prognosis evaluation through conventional clinical data.The purpose of this study is to determine high-precision machine learning algorithms.And assess whether the machine learning can improve the accuracy of cardiovascular risk prediction of large-scale ordinary primary health care people.

  research method

  The forward-looking queue study,Selected 378,256 years old without cardiovascular disease history, genetic lipid metabolic disorder, 8 core baseline variables (gender, age, smoking status, shrink, blood pressure, total cholesterol)HDL and diabetes)[2],Compare four machine learning algorithms (random forests, logistic regression, gradient enhancers, neural networks) with established algorithms (US Cardiology College Guide),Prediction 10 years (2005.01.01-2015.01.The first cardiovascular event between 01).The prediction accuracy is evaluated by the area under “receiver working curve” (AUC); sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) predicts 7.5% cardiovascular risk (threshold of statins).

  8 core variables are used to export baseline risk prediction models,Use 2013 ACC / AHA to evaluate the equation published in the CVD Risk Guide[2].In addition, 9 consecutive variables,There is a missing data,At the same time, it is assumed that some clinical variables are missing (eg,Body mass index and laboratory results may indicate the correlation of these patients.Considering that the records of normal BMI values in primary health care[23],We will create a virtual variable to indicate if these continuous variables are lost.In addition to population classification variables, Tomson deprived index and race,A separate “unknown” category is given in the analysis.There are a total of 30 variables (virtual variables that do not include missing values) have been analyzed in the machine learning model before baseline (Table1).

  

  Machine learning algorithm

  In order to compare the machine learning risk algorithm,We randomly extract 75% of CPRD queues as a “training” queue,Export for CVD risk algorithms,The rest of 25% is “verification” queue,Used to apply and test algorithms.Used four common machine learning algorithms: logistic regression[25], random forest[26], gradient enhancers[27]and neural networks[28].Also RSTUDIO algorithm (http: // CRAN.R-Project.ORG / PACKAGEKARET) For neural networks,H2O (http: // www.H2O.AI) for the rest of the algorithm.The super parameter of each model is determined by two folded cross-validation through the grid search and training queue.

  Statistical Analysis

  This study provides the descriptive characteristics of the crowd.The number (%) and the average value (SD) of the classified variable and continuous variables, respectively.The performance of the machine learning prediction algorithm developed from the training queue,Evaluate the verification queue by calculating the Harrell’s C-Statistic[29].Calculate the total area of the receiver working characteristic curve (AUC),C-Statistic uses Jack-KNife program to calculate standard errors and 95% confidence interval[30].In addition,According to the ACC / AHA Guide[2], the 10-year CVD risk for starting fat-proof treatment is> 7.5% threshold,Cases and non-cases that are observed and expected in the verification queue are compared using binary classification analysis.This process provides sensitivity, specific, positive predictive value (PPV), and negative predictive values (NPV).Statistical analysis of algorithm performance with STATA13MP4.

  Research result

  Data Extraction

  In this study,The total of 383,592 patients in 200,000 patients met the qualification standards.Exclude 5336 cases of erroneous patients (i.e., non-numerical entry of blood pressure / cholesterol) and extreme observation?(> 5 times SD of the average value),The analysis queue consists of 378,256 patients.then,The queue randomly divided into 75% samples of 29,5267 patients.The remaining samples of the training machine learning algorithm and 82,989 patients are used to verify (Fig 1).

  

  In the whole queue,378,256 patients, 10 patients,There are 24,970 cases (6.6%) cardiovascular disease cases.Women in CVD cases are significantly less than men (42% f,52% M),In the case of non-CVD cases, women are only slightly more than men (52% f,48% m).The average baseline age of cardiovascular disease patient is 65.3 years,The average baseline age of patients with non-cardiovascular disease is 57.3 years old (P<0.001)。CVD 和非 CVD 患者的进一步特征见 Table2。

  

  The input variables of the machine learning model are listed in Table2.The importance of variables is determined by the coefficient effect size of the ACC / AHA baseline model and the machine learning Logistic regression.Random forest and gradient enhancement machine model,Based on the decision tree,Sorting the variable of the variable of the variable as the decision node,The neural network uses the overall weight of the variable in the model.Top 10 risk factors for CVD prediction algorithms see Table3.

  

  Standard risk factors in the ACC / AHA algorithm in gender are age, total cholesterol, high-density lipoprotein cholesterol, smoking, blood pressure and diabetes.Several risk factors (age, gender, smoking) in the ACC / AHA model are top risk factors for all four machine learning algorithms.diabetes,A significant factor in many CVD algorithms,However, in the machine learning model, it is not listed as top risk factors (although HBA1C is used as a proxy in a random forest model).Machine learning discovered some other new risk factors found by the previous risk prediction tool,Including medical conditions,Such as COPD and severe mental illness,The prescription oral corticosteroids,And biomarkers,Such as triglyceride levels.Random forests and gradient enhancers are most similar in risk factors and rankings.There are some differences in ranking order and BMI alternative systolic pressure.Logic regression and neural network prioritize medical conditions,Such as atrial fibrillation,Chronic kidney disease and rheumatoid arthritis,More than the risk factors of biometric characteristics.Neural networks also will be aged as a weighted less risk factor.It includes “body mass index deletion” as a protection risk factor for CVD.

  According to the discriminant tool (AUC statistic), there is a predictive accuracy of all models to see Table4.

  

  The ACC / AHA risk model is a comparison baseline (AUC 0.728,95% CI 0.723-0.735).Compared with the baseline model,All tested machine learning algorithms show significant improvements in statistical skills (increasing from random forest algorithms.7% to the neural network increase 3.6%) ACC / AHA baseline models are correctly predicted from 7404 cases of 4,643 cases.Sensitivity is 62.7%,PPV is 17.1%.The random forest algorithm predicts 191 cases of CVD cases on the basis of the baseline model.Sensitivity is increased to 65.3%,PPV is increased to 17.8%,Logistic regression predicts 324 cases of CVD cases (sensitivity 67.1%; PPV18.3%).Gradient enhancement machine and neural network performance,354 cases were predicted correctly, respectively, respectively (sensitivity 67.5%; PPV18.4%) and 355 CVDs (sensitivity 67.5%; PPV18.4%).The ACC / AHA baseline model is correctly predicted from 75,585 non-cases, 5,3106 non-cases.The results specifically 70.3%,NPV is 95.1%.Compared with the baseline ACC / AHA model,The number of non-pathological examples of the random forest algorithm increased by 191 cases,The neural network increased by 355 cases.

  Analysis conclusion

  Machine learning has significantly improved the accuracy of cardiovascular risk prediction,Compared with the established AHA / ACC risk prediction algorithm,We have found that all test machine learning algorithms can better identify will develop into CVDs and do not develop individuals with CVD.Unlike established risk prediction methods,The machine learning method used is not limited to a small risk factor,And incorporate more pre-existing medical conditions.Neural network performance is best,The prediction accuracy is increased by 3.6%.

  Advantages and limitations

  This study conducted the first time for the machine’s electronic medical records in the patient’s electronic medical record.It is confirmed that machine learning can better predict large-scale general population CVD risks.A series of machine learning algorithms used in this study showed that the models based on decision tree are similar to each other.Gradient enhancers have superior performance than random forests.Neural network and logistic regression more emphasis on classified variables and CVD related medical conditions,Patients having similar features in each group are clustered.This may help further explore different predictive risk factors.And the new risk prediction method and the development trend of algorithms.In addition, the disclosure of the deficiency or no response is ignored in conventional CVD risk prediction tools[2-5].This study shows thatLack value,Especially for conventional biometric variables,BMI,It is the independent predictor of CVD.

  It has to be recognized that machine learning algorithms,Especially the nature of the “black box” of the neural network may be difficult to explain.This refers to the internal complexity of how the risk factor variable interactions and its independent influence on the results.however,The improvement of data visualization improves the understanding of these models,Describes the importance of network connection between risk factors[35](see Fig.2 Example of visualization neural network model).

  

  In addition, we also realize thatAs the number of potential risk factors increases,The complexity of the model may result in excessive fitness,Produces incredible results.We solve this problem by actively and appropriately selecting the pre-training, hyperfeit selection and regularization[36].Although we have used a separate data set cross to verify the performance of the machine learning algorithm,This is a common method,Used to develop established cardiovascular risk algorithm in clinical practice[2-5,34,37]But must recognize thatJack-Knife programs may produce more accurate results,Such as genomic or proteome data sets[38,39]In addition,These established risk prediction algorithms have been developed from the binary classification framework.This usually causes the imbalance of the data set.Collective learning has proven to build a balanced data set to increase the solution of predictive performance[40].These methods are not common in clinical data concentration development risk prediction models,But their utility should be discussed in future research.

  to sum up

  With the improvement of computing power in the healthcare system,Using machine learning to improve disease risk prediction in clinical practice will be widely used[7].Compared with the established risk prediction method,This study shows thatThe machine learning algorithm can better predict cardiovascular disease cases.Increase the absolute quantity of predictive cases,A non-CVD case is successfully excluded.

  references

  Slide the view

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  Literature source

  Weng, S. F., REPS, J., Kai, J., Garibaldi, J. M., & Quereshi, N. (2017). Can Machine-Learning Improve Cardiovascular Risk PREDICTION USING ROUTINE CLINICAL DATA? Plos One, 12 (4), E0174944. DOI: 10.PONE.0174944

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