• Kidney disease
• healthcare
• nurses

• chronic kidney disease
• estimated glomerular filtration rate
• health examination
• proteinuria
• recurrent neural network

• Humans
• Renal Insufficiency, Chronic/epidemiology
• Machine Learning
• Male
• Female
• Middle Aged
• Glomerular Filtration Rate
• Adult
• Proteinuria/epidemiology
• Proteinuria/diagnosis
• Aged
• Risk Assessment/methods
• Risk Factors
• Logistic Models
• Incidence

• Longitudinal study
• Machine learning
• Osteoporosis

• Humans
• Female
• Machine Learning
• Risk Factors
• Bone Density
• Middle Aged
• Postmenopause
• Follow-Up Studies
• Aged
• Osteoporosis, Postmenopausal
• China/epidemiology

• assessment
• chronic kidney disease
• random forest model
• risk factors

• chronic kidney disease
• feature ensemble
• health screening
• longitudinal data
• machine learning
• metabolic syndrome

• NSTC 111-2221-E-030-009 National Science and Technology Council, Taiwan
• A0111181 Fu Jen Catholic University

• chronic kidney disease
• end-stage renal disease
• influencing factors
• patient delay
• strategic research

• Humans
• Cross-Sectional Studies
• Renal Insufficiency, Chronic/therapy
• Self Efficacy

• Chronic kidney disease
• interpretable models
• machine learning
• random survival forests
• survival analysis

• National Key Research and Development Program of China
• Natural Science Foundation of Shanghai Municipality
• Three-year Action Plan for Promoting Clinical Skills and Innovation Ability of Municipal Hospitals
• China National Science Foundation

• data mining
• hospitalization
• machine learning
• multivariate adaptive regression splines
• myasthenia gravis
• prognosis

• machine learning
• premenopausal women
• vitamin D

The prevalence of type 2 diabetes (T2D) has been increasing dramatically in recent decades, and 47.5% of T2D patients will die of cardiovascular disease. Thallium-201 myocardial perfusion scan (MPS) is a precise and non-invasive method to detect coronary artery disease (CAD). Most previous studies used traditional logistic regression (LGR) to evaluate the risks for abnormal CAD. Rapidly developing machine learning (Mach-L) techniques could potentially outperform LGR in capturing non-linear relationships.

To aims were: (1) Compare the accuracy of Mach-L methods and LGR; and (2) Found the most important factors for abnormal TMPS.

556 T2D were enrolled in the study (287 men and 269 women). Demographic and biochemistry data were used as independent variables and the sum of stressed score derived from MPS scan was the dependent variable. Subjects with a MPS score ≥ 9 were defined as abnormal. In addition to traditional LGR, classification and regression tree (CART), random forest, Naïve Bayes, and eXtreme gradient boosting were also applied. Sensitivity, specificity, accuracy and area under the receiver operation curve were used to evaluate the respective accuracy of LGR and Mach-L methods.

Except for CART, the other Mach-L methods outperformed LGR, with gender, body mass index, age, low-density lipoprotein cholesterol, glycated hemoglobin and smoking emerging as the most important factors to predict abnormal MPS.

Four Mach-L methods are found to outperform LGR in predicting abnormal TMPS in Chinese T2D, with the most important risk factors being gender, body mass index, age, low-density lipoprotein cholesterol, glycated hemoglobin and smoking.

• Myocardial perfusion scintigraphy
• Machine learning
• Type 2 diabetes
• Thallium-201

• decision tree
• food metabolite
• metabolic syndrome
• sleep time
• sperm count

• Female
• Humans
• Bayes Theorem
• East Asian People/statistics & numerical data
• Linear Models
• Machine Learning
• Osteoporosis/epidemiology
• Risk Factors
• Middle Aged
• Risk Assessment/methods
• Taiwan/epidemiology

• Biochemistry data
• Lifestyle
• Machine learning
• Renal function

• chronic kidney disease
• diabetes
• estimated glomerular filtration
• feature selection
• machine learning

• Artificial intelligence
• Chronic kidney disease
• Machine learning
• Systematic review

• anticoagulant agents
• arrhythmia
• cardioembolic stroke
• decision tree
• machine learning

• Humans
• Dabigatran/therapeutic use
• Dabigatran/adverse effects
• Atrial Fibrillation/drug therapy
• Bayes Theorem
• Hemorrhage/chemically induced
• Hemorrhage/epidemiology
• Machine Learning
• Decision Trees

• Change management
• Chronic renal failure
• PRIMARY CARE

• Humans
• Renal Insufficiency, Chronic
• General Practice
• Risk Factors
• Primary Health Care
• Anti-Inflammatory Agents, Non-Steroidal/adverse effects

• Metabolic syndrome (MetS)
• chronic kidney disease (CKD)
• end-stage kidney disease (ESKD)
• hybrid risk factor
• machine learning (ML)

• Artificial intelligence
• Chronic kidney disease
• Machine learning models
• Prediction
• Renal failure

• Humans
• Artificial Intelligence
• Algorithms
• Machine Learning
• Renal Insufficiency, Chronic/diagnosis
• Databases, Factual
• Disease Progression

• China
• chronic kidney disease
• elderly
• longevity areas
• machine learning
• prediction

Type 2 diabetes mellitus (T2DM) patients have a high risk of coronary artery disease (CAD). Thallium-201 myocardial perfusion scan (Th-201 scan) is a non-invasive and extensively used tool in recognizing CAD in clinical settings. In this study, we attempted to compare the predictive accuracy of evaluating abnormal Th-201 scans using traditional multiple linear regression (MLR) with four machine learning (ML) methods. From the study, we can determine whether ML surpasses traditional MLR and rank the clinical variables and compare them with previous reports.In total, 796 T2DM, including 368 men and 528 women, were enrolled. In addition to traditional MLR, classification and regression tree (CART), random forest (RF), stochastic gradient boosting (SGB) and eXtreme gradient boosting (XGBoost) were also used to analyze abnormal Th-201 scans. Stress sum score was used as the endpoint (dependent variable). Our findings show that all four root mean square errors of ML are smaller than with MLR, which implies that ML is more precise than MLR in determining abnormal Th-201 scans by using clinical parameters. The first seven factors, from the most important to the least are:body mass index, hemoglobin, age, glycated hemoglobin, Creatinine, systolic and diastolic blood pressure. In conclusion, ML is not inferior to traditional MLR in predicting abnormal Th-201 scans, and the most important factors are body mass index, hemoglobin, age, glycated hemoglobin, creatinine, systolic and diastolic blood pressure. ML methods are superior in these kinds of studies.

• machine learning
• nephropathy
• type 2 diabetes
• urine albumin-creatinine ratio

Using machine learning method to predict and judge unknown data offers opportunity to improve accuracy by exploring complex interactions between risk factors. Therefore, we evaluate the performance of machine learning (ML) algorithms and to compare them with logistic regression for predicting the risk of renal function decline (RFD) using routine clinical data.

This retrospective cohort study includes datasets from 2166 subjects, aged 35–74 years old, provided by an adult health screening follow-up program between 2010 and 2020. Seven different ML models were considered – random forest, gradient boosting, multilayer perceptron, support vector machine, K-nearest neighbors, adaptive boosting, and decision tree - and were compared with standard logistic regression. There were 24 independent variables, and the baseline estimate glomerular filtration rate (eGFR) was used as the predictive variable.

A total of 2166 participants (mean age 49.2±11.2 years old, 63.3% males) were enrolled and randomly divided into a training set (n=1732) and a test set (n=434). The area under receiver operating characteristic curve (AUROC) for detecting RFD corresponding to the different models were above 0.85 during the training phase. The gradient boosting algorithms exhibited the best average prediction accuracy (AUROC: 0.914) among all algorithms validated in this study. Based on AUROC, the ML algorithms improved the RFD prediction performance, compared to logistic regression model (AUROC:0.882), except the K-nearest neighbors and decision tree algorithms (AUROC:0.854 and 0.824, respectively). However, the improvement differences with logistic regression were small (less than 4%) and nonsignificant.

Our results indicate that the proposed health screening dataset-based RFD prediction model using ML algorithms is readily applicable, produces validated results. But logistic regression yields as good performance as ML models to predict the risk of RFD with simple clinical predictors.

Patients with hypertension and glucose metabolism disorder (GMD) are at high risk of developing kidney dysfunction (KD). Therefore, we aimed to develop a nomogram for predicting individuals’ 5-year risk of KD in hypertensives with GMD.

In total, 1961 hypertensives with GMD were consecutively included. Baseline data were extracted from medical electronic system, and follow-up data were obtained using annual health check-ups or hospital readmission. KD was defined as estimated glomerular filtration rate (eGFR) <60 mL/min/1.73m². Subjects were randomly divided into training and validation sets with a ratio of 7 to 3. Least absolute shrinkage and selection operator method was used to identify potential predictors. Cox proportional hazard model was applied to build a nomogram for predicting KD risk. The discriminative ability, calibration and usefulness of the model were evaluated. The prediction model was verified by internal validation.

During the follow-up of 5351 person-years with a median follow-up of 32 (range: 3–91) months, 130 patients developed KD. Age, sex, ethnicity, hemoglobin A1c, uric acid, and baseline eGFR were identified as significant predictors for incident KD and used for establishing nomogram. The prediction model displayed good discrimination with C-index of 0.770 (95% CI: 0.712–0.828) and 0.763 (95% CI: 0.704–0.823) in training and validation sets, respectively. Calibration curve indicated good agreement between the predicted and actual probabilities. The decision curve analysis demonstrated that the model was clinically useful.

The prediction nomogram, including six common easy-to-obtain factors, shows good performance for predicting 5-year risk of KD in hypertensives with GMD. This quantitative tool could help clinicians, and even primary care providers, recognize potential KD patients early and make strategy for prevention and management.

• the National Key R&D Program of China

• decision tree
• intensive care unit
• machine learning
• myasthenia gravis
• predication

• Data Mining
• Electronic Health Records
• Humans
• Hypertension/diagnosis
• Hypertension/therapy
• Mental Disorders/diagnosis
• Mental Disorders/therapy
• Renal Insufficiency, Chronic/diagnosis
• Renal Insufficiency, Chronic/therapy

Previous studies on CKD patients have mostly been retrospective, cross-sectional studies. Few studies have assessed the longitudinal assessment of patients over an extended period. In consideration of the heterogeneity of CKD progression. It’s critical to develop a longitudinal diagnosis and prognosis for CKD patients. We proposed an auto Machine Learning (ML) scheme in this study. It consists of four main parts: classification pipeline, cross-validation (CV), Taguchi method and improve strategies. This study includes datasets from 50,174 patients, data were collected from 32 chain clinics and three special physical examination centers, between 2015 and 2019. The proposed auto-ML scheme can auto-select the level of each strategy to associate with a classifier which finally shows an acceptable testing accuracy of 86.17%, balanced accuracy of 84.08%, sensitivity of 90.90% and specificity of 77.26%, precision of 88.27%, and F1 score of 89.57%. In addition, the experimental results showed that age, creatinine, high blood pressure, smoking are important risk factors, and has been proven in previous studies. Our auto-ML scheme light on the possibility of evaluation for the effectiveness of one or a combination of those risk factors. This methodology may provide essential information and longitudinal change for personalized treatment in the future.

• chronic kidney disease
• clinical decision-making
• machine learning
• risk prediction

• Clinical Decision-Making
• Cross-Sectional Studies
• Humans
• Machine Learning
• Renal Insufficiency, Chronic/diagnosis
• Renal Insufficiency, Chronic/epidemiology
• Retrospective Studies

• chronic kidney disease
• education
• health screening
• machine learning algorithms
• risk indicators assessment

Early detection is important in glaucoma management. By using optical coherence tomography (OCT), the subtle structural changes caused by glaucoma can be detected. Though OCT provided abundant parameters for comprehensive information, clinicians may be confused once the results conflict. Machine learning classifiers (MLCs) are good tools for considering numerous parameters and generating reliable diagnoses in glaucoma practice. Here we aim to compare different MLCs based on Spectralis OCT parameters, including circumpapillary retinal nerve fiber layer (cRNFL) thickness, Bruch’s membrane opening-minimum rim width (BMO-MRW), Early Treatment Diabetes Retinopathy Study (ETDRS) macular thickness, and posterior pole asymmetry analysis (PPAA), in discriminating normal from glaucomatous eyes. Five MLCs were proposed, namely conditional inference trees (CIT), logistic model tree (LMT), C5.0 decision tree, random forest (RF), and extreme gradient boosting (XGBoost). Logistic regression (LGR) was used as a benchmark for comparison. RF was shown to be the best model. Ganglion cell layer measurements were the most important predictors in early glaucoma detection and cRNFL measurements were more important as the glaucoma severity increased. The global, temporal, inferior, superotemporal, and inferotemporal sites were relatively influential locations among all parameters. Clinicians should cautiously integrate the Spectralis OCT results into the entire clinical picture when diagnosing glaucoma.

• glaucoma
• machine learning
• optical coherence tomography (OCT)

Due to the high effectiveness of cancer screening and therapies, the diagnosis of second primary cancers (SPCs) has increased in women with endometrial cancer (EC). However, previous studies providing adequate evidence to support screening for SPCs in endometrial cancer are lacking. This study aimed to develop effective risk prediction models of second primary endometrial cancer (SPEC) in women with obesity (body mass index (BMI) > 25) and included datasets on the incidence of SPEC and the other risks of SPEC in 4480 primary cancer survivors from a hospital-based cancer registry database. We found that obesity plays a key role in SPEC. We used 10 independent variables as predicting variables, which correlated to obesity, and so should be monitored for the early detection of SPEC in endometrial cancer. Our proposed scheme is promising for SPEC prediction and demonstrates the important influence of obesity and clinical data representation in all cases following primary treatments. Our results suggest that obesity is still a crucial risk factor for SPEC in endometrial cancer.

• endometrial cancer (EC)
• risk prediction
• second primary cancers (SPCs)
• second primary endometrial cancer (SPEC)

Unlike most daily decisions, medical decision making often has substantial consequences and trade-offs. Recently, big data analytics techniques such as statistical analysis, data mining, machine learning and deep learning can be applied to construct innovative decision models. With complex decision making, it can be difficult to comprehend and compare the benefits and risks of all available options to make a decision. For these reasons, this Special Issue focuses on the use of big data analytics and forms of public health decision making based on the decision model, spanning from theory to practice. A total of 64 submissions were carefully blind peer reviewed by at least two referees and, finally, 23 papers were selected for this Special Issue.

• big data
• decision models
• public health

• Big Data
• Clinical Decision-Making
• Data Mining
• Machine Learning
• Public Health