Use Machine Learning Methods to Explore the Associated Risk Factors of Osteoporosis or Bone Loss in Chinese People With Type 2 Diabetes：A Clinical Prediction Model

2020 ◽  
Author(s):  
Yaqian Mao ◽  
Ting Xue ◽  
Jixing Liang ◽  
Wei Lin ◽  
Junping Wen ◽  
...  
Keyword(s):  
Machine Learning ◽  
Risk Factors ◽  
Bone Loss ◽  
Prediction Model ◽  
Chinese People ◽  
Clinical Prediction ◽  
Clinical Prediction Model ◽  
Machine Learning Methods ◽  
Associated Risk Factors

scholarly journals Use Machine Learning Methods to Explore the Associated Risk Factors of Osteoporosis or Bone Loss in Chinese People With Type 2 Diabetes: A Clinical Prediction Model

2021 ◽  
Vol 5 (Supplement_1) ◽  
pp. A250-A251
Author(s):  
Yaqian Mao ◽  
Jixing Liang ◽  
Wei Lin ◽  
Junping Wen ◽  
Gang Chen
Keyword(s):  
Risk Factors ◽  
Bone Loss ◽  
Prediction Models ◽  
Clinical Prediction ◽  
Clinical Prediction Models ◽  
Prediction Ability ◽  
Related Risk ◽  
Associated Risk Factors ◽  
Index Value

Abstract Objective: This study aimed to use machine learning (ML) methods to explore the risk factors associated with OP and bone loss in the Chinese T2DM population, so as to construct useful clinical prediction models. Methods: This was a two-center, retrospective study. The data came from a chronic disease epidemiological investigation database conducted in Ningde City and Wuyishan City, Fujian Province, China from March 2011 to December 2014. Finally, 798 T2DM patients who met the enrollment criteria were included in the final analysis. In order to control gender as a confounding factor that affects the results, we constructed two clinical prediction models based on different genders. We used the least absolute shrinkage and selection operator (LASSO) algorithm to filter relevant feature variables. The selected characteristic variables were modeled by logistic regression (LR), and clinical nomograms were used for more intuitive expression. The stability, clinical applicability and recognition of the model were evaluated by C-index, receiver operating characteristic (ROC) curve, calibration chart and decision curve analysis (DCA). Internal verification was achieved through bootstrapping validation. Results: In exploring the related risk factors of OP or bone loss in female T2DM patients. There were a total of 9 related predictors, namely age, marital status, glutamyl transpeptidase, fracture, coronary heart disease, fruit-flavored drinks, moderate-intensity exercise, menopause and nap time were determined by LASSO analysis from a total of 69 variables. The model we constructed using these 9 related predictors showed medium prediction ability (C-index value: 0.738, 95%CI[0.692, 0.784]), the C-index in bootstrapping validation was 0.714, and the area under the ROC curve (AUC) was 0.738. The DCA showed that if the risk threshold was between 4% and 100%, the nomogram could be used clinically. In exploring the related risk factors of OP and bone loss in male T2DM patients. A total of 12 related predictors were identified from 65 variables through LASSO analysis, including age, marital status, fasting serum insulin, alanine aminotransferase, coronary heart disease, respiratory diseases, diabetic retinopathy, seafood, desserts, fruit-flavored beverages, coffee, high-intensity exercise. The model we constructed using these 12 related predictors showed medium prediction ability (C-index value: 0.751, 95%CI[0.694–0.808]), the C-index in bootstrapping validation was 0.704, and the AUC value was 0.751. The DCA showed that if the risk threshold was between 3% and 68%, the nomogram could be used clinically. Conclusion: We explored the associated risk factors of osteoporosis or bone Loss in Chinese people with type 2 diabetes, and developed a risk nomogram with moderate predictive power. The nomogram can help clinicians and patients make joint decisions before treatment.

scholarly journals Diving Deeper into Models

2018 ◽  
pp. 121-133
Author(s):  
Alberto Traverso ◽  
Frank J. W. M. Dankers ◽  
Biche Osong ◽  
Leonard Wee ◽  
Sander M. J. van Kuijk
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Prediction Model ◽  
Unsupervised Learning ◽  
Learning Objectives ◽  
Clinical Prediction ◽  
Clinical Prediction Model ◽  
Supervised And Unsupervised Learning

AbstractPre-requisites to better understand the chapter: knowledge of the major steps and procedures of developing a clinical prediction model.Logical position of the chapter with respect to the previous chapter: in the last chapters, you have learned how to develop and validate a clinical prediction model. You have been learning logistic regression as main algorithm to build the model. However, several different more complex algorithms can be used to build a clinical prediction model. In this chapter, the main machine learning based algorithms will be presented to you.Learning objectives: you will be presented with the definitions of: machine learning, supervised and unsupervised learning. The major algorithms for the last two categories will be introduced.

scholarly journals Predicting risk of pancreatic cancer in individuals with new-onset type-2 diabetes in primary care: protocol for the development and validation of a clinical prediction model (QPancreasD)

2021 ◽  
Author(s):  
Pui San Tan ◽  
Ashley Clift ◽  
Weiqi Liao ◽  
Martina Patone ◽  
Carol Coupland ◽  
...  
Keyword(s):  
Type 2 Diabetes ◽  
Primary Care ◽  
Pancreatic Cancer ◽  
Prediction Model ◽  
Individual Risk ◽  
Diabetes Diagnosis ◽  
Clinical Prediction ◽  
Clinical Prediction Model ◽  
New Onset

Background Pancreatic cancer continues to have an extremely poor prognosis in part due to late diagnosis. 25% of pancreatic cancer patients have a prior diagnosis of diabetes, and hence identifying individuals at risk of pancreatic cancer in those with recently diagnosed type 2 diabetes may be a useful opportunity to identify candidates for screening and early detection. In this study, we will comparatively evaluate regression and machine learning-based clinical prediction models for estimating individual risk of developing pancreatic cancer two years after type 2 diabetes diagnosis. Methods In the development dataset, we will include adults aged 30-84 years with incident type-2 diabetes registered with QResearch primary care database. Patients will be followed up from type-2 diabetes diagnosis to first diagnosis of pancreatic cancer as recorded in any one of primary care records, hospital episode statistics, cancer registry data, or death records. Cox-proportional hazards models will be used to develop a risk prediction model for estimating individual risk of developing pancreatic cancer during up to 2 years of follow-up. We will perform variable selection using a combination of clinical and statistical significance approach i.e. HR <0.9 or >1.1 and p<0.01. Linear predictors and baseline survivor function at 2 years will be used to compute absolute risk predictions. Internal-external cross-validation (IECV) framework across geographical regions within England will be used to assess performance and pooled using random effects meta-analysis using: (i) model fit in terms of variation explained by the model Royston & Sauerbrei's R2D, (ii) calibration slope and calibration-in-the-large, and (iii) discrimination measured in terms of Harrell's C and Royston & Sauerbrei's D-statistic. Further, we will evaluate machine learning (ML) approaches for the clinical prediction model using neural networks (NN) and XGBoost. The model predictors and performance of these will be compared with the results of those derived from the regression-based strategy. Discussion The proposed study will develop and validate a novel risk prediction model to aid early diagnosis of pancreatic cancer in patients with new-onset diabetes in primary care. With an enhanced decision-risk tool for use at point-of care by general practitioners to assess pancreatic cancer risk, it may improve decision-making so that at-risk patients are rapidly prioritised to aid early diagnosis of pancreatic cancer in patients with newly diagnosed diabetes.

scholarly journals Prediction of Postoperative Delirium in Geriatric Hip Fracture Patients: A Clinical Prediction Model Using Machine Learning Algorithms

2021 ◽  
Vol 12 ◽  
pp. 215145932110622
Author(s):  
Jacobien H. F. Oosterhoff ◽  
Aditya V. Karhade ◽  
Tarandeep Oberai ◽  
Esteban Franco-Garcia ◽  
Job N. Doornberg ◽  
...  
Keyword(s):  
Machine Learning ◽  
Hip Fracture ◽  
Prediction Model ◽  
Postoperative Delirium ◽  
Machine Learning Algorithms ◽  
Brier Score ◽  
Clinical Prediction ◽  
Clinical Prediction Model ◽  
Preoperative Prediction ◽  
Geriatric Hip Fracture

Introduction Postoperative delirium in geriatric hip fracture patients adversely affects clinical and functional outcomes and increases costs. A preoperative prediction tool to identify high-risk patients may facilitate optimal use of preventive interventions. The purpose of this study was to develop a clinical prediction model using machine learning algorithms for preoperative prediction of postoperative delirium in geriatric hip fracture patients. Materials & Methods Geriatric patients undergoing operative hip fracture fixation were queried in the American College of Surgeons National Surgical Quality Improvement Program database (ACS NSQIP) from 2016 through 2019. A total of 28 207 patients were included, of which 8030 (28.5%) developed a postoperative delirium. First, the dataset was randomly split 80:20 into a training and testing subset. Then, a random forest (RF) algorithm was used to identify the variables predictive for a postoperative delirium. The machine learning-model was developed on the training set and the performance was assessed in the testing set. Performance was assessed by discrimination (c-statistic), calibration (slope and intercept), overall performance (Brier-score), and decision curve analysis. Results The included variables identified using RF algorithms were (1) age, (2) ASA class, (3) functional status, (4) preoperative dementia, (5) preoperative delirium, and (6) preoperative need for mobility-aid. The clinical prediction model reached good discrimination (c-statistic = .79), almost perfect calibration (intercept = −.01, slope = 1.02), and excellent overall model performance (Brier score = .15). The clinical prediction model was deployed as an open-access web-application: https://sorg-apps.shinyapps.io/hipfxdelirium/ . Discussion & Conclusions We developed a clinical prediction model that shows promise in estimating the risk of postoperative delirium in geriatric hip fracture patients. The clinical prediction model can play a beneficial role in decision-making for preventative measures for patients at risk of developing a delirium. If found to be externally valid, clinicians might use the available web-based application to help incorporate the model into clinical practice to aid decision-making and optimize preoperative prevention efforts.

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