The development and implementation of stroke risk prediction model in National Health Insurance Service's personal health record

Abstract Background Great efforts were made to collect information and identify risk factors in predicting post-anesthetic mortality. In this study, we use national health insurance data base, including medications, underlying comorbidities and surgical factors to assess the relationship between these factors and mortality after surgery. Methods This is a retrospective, population based study. The study population who underwent general anesthesia (GA) were retrieved from the National Health Insurance Research Database in Taiwan between January 1, 2005 and December 31, 2013. We classified the study patients into 4 major comparison groups by surgical procedures, including major organ transplantation (heart, liver, lung, kidney, or pancreas), CV surgery, major neurosurgery, and others according to the diagnostic codes of the international classification of diseases, ninth revision, clinical modification (ICD-9-CM) codes. We proposed a logistic regression model with valuable variables which can significantly predicts the post-anesthesia mortality. We also designed different models for 4 subgroups according the results. Results A total of 833,685 subjects were included in this study, and the most common comorbidity was hypertension. Age was an important determinant associated with post-operation mortality among different surgical types. Perioperative prescription could reduce risks of operation. The prediction model based on the preliminary training group also performed well in the validation group (AUROC=0.8753 for in-hospital mortality; AUROC= 0.8767 for 30-days mortality). A reliable predicting model can help anesthesiologists to decide the anesthesia method or monitors, as well as helping physicians to take care of their patients after operation. Conclusions While GA is commonly used for the majority of the patients undergoing operations, the prediction model that we proposed from this nationwide study could identify the predictors for post-operation mortality. The potentially protective effects of anti-lipid, hypoglycemic, and anti-hypertensive agents were encouraging in geriatric preoperative group. It is expected that applying this prediction model and prescription into clinical practice could improve surgical risk stratification and further improve patient outcomes. Trial registration The protocol of this study was approved by the National Taiwan University Hospital Research Ethics Committee (Trial Registration 201411078RINC). Informed consent was waived by the National Taiwan University Hospital Research Ethics Committee due to the retrospective and anonymous nature of the claims data.

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J-015 DEVELOPMENT OF A STROKE RISK PREDICTION MODEL FOR JAPANESE

Journal of Hypertension ◽

10.1097/01.hjh.0000408059.63001.6f ◽

2011 ◽

Vol 29 ◽

pp. e27-e28

Author(s):

Toshiharu Ninomiya ◽

Yutaka Kiyohara ◽

Takashi Ando ◽

Akiko Harada ◽

Yasuo Ohashi ◽

...

Keyword(s):

Prediction Model ◽

Risk Prediction ◽

Stroke Risk ◽

Risk Prediction Model

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Development and validation of a dynamic inpatient risk prediction model for clinically significant hypokalemia using electronic health record data

American Journal of Health-System Pharmacy ◽

10.1093/ajhp/zxy051 ◽

2019 ◽

Vol 76 (5) ◽

pp. 301-311 ◽

Cited By ~ 2

Author(s):

Yan Li ◽

Benjamin Staley ◽

Carl Henriksen ◽

Dandan Xu ◽

Gloria Lipori ◽

...

Keyword(s):

Prediction Model ◽

Electronic Health Record ◽

Risk Prediction ◽

Risk Prediction Model ◽

Health Record ◽

Electronic Health Record Data ◽

Record Data ◽

Electronic Health ◽

Clinically Significant ◽

Development And Validation

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Prediction of the risk of developing hepatocellular carcinoma in health screening examinees: a Korean cohort study

BMC Cancer ◽

10.1186/s12885-021-08498-w ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Chansik An ◽

Jong Won Choi ◽

Hyung Soon Lee ◽

Hyunsun Lim ◽

Seok Jong Ryu ◽

...

Keyword(s):

Machine Learning ◽

Hepatocellular Carcinoma ◽

Health Insurance ◽

Liver Disease ◽

Prediction Model ◽

National Health Insurance ◽

National Health ◽

Claim Data ◽

Health Screening ◽

Training Cohort

Abstract Background Almost all Koreans are covered by mandatory national health insurance and are required to undergo health screening at least once every 2 years. We aimed to develop a machine learning model to predict the risk of developing hepatocellular carcinoma (HCC) based on the screening results and insurance claim data. Methods The National Health Insurance Service-National Health Screening database was used for this study (NHIS-2020-2-146). Our study cohort consisted of 417,346 health screening examinees between 2004 and 2007 without cancer history, which was split into training and test cohorts by the examination date, before or after 2005. Robust predictors were selected using Cox proportional hazard regression with 1000 different bootstrapped datasets. Random forest and extreme gradient boosting algorithms were used to develop a prediction model for the 9-year risk of HCC development after screening. After optimizing a prediction model via cross validation in the training cohort, the model was validated in the test cohort. Results Of the total examinees, 0.5% (1799/331,694) and 0.4% (390/85,652) in the training cohort and the test cohort were diagnosed with HCC, respectively. Of the selected predictors, older age, male sex, obesity, abnormal liver function tests, the family history of chronic liver disease, and underlying chronic liver disease, chronic hepatitis virus or human immunodeficiency virus infection, and diabetes mellitus were associated with increased risk, whereas higher income, elevated total cholesterol, and underlying dyslipidemia or schizophrenic/delusional disorders were associated with decreased risk of HCC development (p < 0.001). In the test, our model showed good discrimination and calibration. The C-index, AUC, and Brier skill score were 0.857, 0.873, and 0.078, respectively. Conclusions Machine learning-based model could be used to predict the risk of HCC development based on the health screening examination results and claim data.

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Machine Learning-Based Cardiovascular Disease Prediction Model: A Cohort Study on the Korean National Health Insurance Service Health Screening Database

Diagnostics ◽

10.3390/diagnostics11060943 ◽

2021 ◽

Vol 11 (6) ◽

pp. 943

Author(s):

Joung Ouk (Ryan) Kim ◽

Yong-Suk Jeong ◽

Jin Ho Kim ◽

Jong-Weon Lee ◽

Dougho Park ◽

...

Keyword(s):

Machine Learning ◽

Health Insurance ◽

Prediction Model ◽

National Health Insurance ◽

National Health ◽

Prediction Models ◽

Characteristic Curve ◽

Health Screening ◽

Gradient Boosting ◽

Extreme Gradient Boosting

Background: This study proposes a cardiovascular diseases (CVD) prediction model using machine learning (ML) algorithms based on the National Health Insurance Service-Health Screening datasets. Methods: We extracted 4699 patients aged over 45 as the CVD group, diagnosed according to the international classification of diseases system (I20–I25). In addition, 4699 random subjects without CVD diagnosis were enrolled as a non-CVD group. Both groups were matched by age and gender. Various ML algorithms were applied to perform CVD prediction; then, the performances of all the prediction models were compared. Results: The extreme gradient boosting, gradient boosting, and random forest algorithms exhibited the best average prediction accuracy (area under receiver operating characteristic curve (AUROC): 0.812, 0.812, and 0.811, respectively) among all algorithms validated in this study. Based on AUROC, the ML algorithms improved the CVD prediction performance, compared to previously proposed prediction models. Preexisting CVD history was the most important factor contributing to the accuracy of the prediction model, followed by total cholesterol, low-density lipoprotein cholesterol, waist-height ratio, and body mass index. Conclusions: Our results indicate that the proposed health screening dataset-based CVD prediction model using ML algorithms is readily applicable, produces validated results and outperforms the previous CVD prediction models.

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