scholarly journals A Machine-Learning-Based Prediction Method for Hypertension Outcomes Based on Medical Data

Diagnostics ◽  
2019 ◽  
Vol 9 (4) ◽  
pp. 178 ◽  
Author(s):  
Wenbing Chang ◽  
Yinglai Liu ◽  
Yiyong Xiao ◽  
Xinglong Yuan ◽  
Xingxing Xu ◽  
...  
Keyword(s):  
Physical Examination ◽  
Operating Characteristic ◽  
Cross Validation ◽  
Characteristic Curve ◽  
Prediction Method ◽  
Prediction Performance ◽  
Recursive Feature Elimination ◽  
Second Step ◽  
Operating Characteristic Curve ◽  
Key Features

The outcomes of hypertension refer to the death or serious complications (such as myocardial infarction or stroke) that may occur in patients with hypertension. The outcomes of hypertension are very concerning for patients and doctors, and are ideally avoided. However, there is no satisfactory method for predicting the outcomes of hypertension. Therefore, this paper proposes a prediction method for outcomes based on physical examination indicators of hypertension patients. In this work, we divide the patients’ outcome prediction into two steps. The first step is to extract the key features from the patients’ many physical examination indicators. The second step is to use the key features extracted from the first step to predict the patients’ outcomes. To this end, we propose a model combining recursive feature elimination with a cross-validation method and classification algorithm. In the first step, we use the recursive feature elimination algorithm to rank the importance of all features, and then extract the optimal features subset using cross-validation. In the second step, we use four classification algorithms (support vector machine (SVM), C4.5 decision tree, random forest (RF), and extreme gradient boosting (XGBoost)) to accurately predict patient outcomes by using their optimal features subset. The selected model prediction performance evaluation metrics are accuracy, F1 measure, and area under receiver operating characteristic curve. The 10-fold cross-validation shows that C4.5, RF, and XGBoost can achieve very good prediction results with a small number of features, and the classifier after recursive feature elimination with cross-validation feature selection has better prediction performance. Among the four classifiers, XGBoost has the best prediction performance, and its accuracy, F1, and area under receiver operating characteristic curve (AUC) values are 94.36%, 0.875, and 0.927, respectively, using the optimal features subset. This article’s prediction of hypertension outcomes contributes to the in-depth study of hypertension complications and has strong practical significance.

scholarly journals Automatic identification of suspicious bone metastatic lesions in bone scintigraphy using convolutional neural network

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Yemei Liu ◽  
Pei Yang ◽  
Yong Pi ◽  
Lisha Jiang ◽  
Xiao Zhong ◽  
...  
Keyword(s):  
Receiver Operating Characteristic Curve ◽  
Operating Characteristic ◽  
Cross Validation ◽  
Characteristic Curve ◽  
Whole Body ◽  
Bone Lesions ◽  
Average Sensitivity ◽  
Metastatic Lesions ◽  
Operating Characteristic Curve ◽  
Body Bone

Abstract Background We aimed to construct an artificial intelligence (AI) guided identification of suspicious bone metastatic lesions from the whole-body bone scintigraphy (WBS) images by convolutional neural networks (CNNs). Methods We retrospectively collected the 99mTc-MDP WBS images with confirmed bone lesions from 3352 patients with malignancy. 14,972 bone lesions were delineated manually by physicians and annotated as benign and malignant. The lesion-based differentiating performance of the proposed network was evaluated by fivefold cross validation, and compared with the other three popular CNN architectures for medical imaging. The average sensitivity, specificity, accuracy and the area under receiver operating characteristic curve (AUC) were calculated. To delve the outcomes of this study, we conducted subgroup analyses, including lesion burden number and tumor type for the classifying ability of the CNN. Results In the fivefold cross validation, our proposed network reached the best average accuracy (81.23%) in identifying suspicious bone lesions compared with InceptionV3 (80.61%), VGG16 (81.13%) and DenseNet169 (76.71%). Additionally, the CNN model's lesion-based average sensitivity and specificity were 81.30% and 81.14%, respectively. Based on the lesion burden numbers of each image, the area under the receiver operating characteristic curve (AUC) was 0.847 in the few group (lesion number n ≤ 3), 0.838 in the medium group (n = 4–6), and 0.862 in the extensive group (n > 6). For the three major primary tumor types, the CNN-based lesion identifying AUC value was 0.870 for lung cancer, 0.900 for prostate cancer, and 0.899 for breast cancer. Conclusion The CNN model suggests potential in identifying suspicious benign and malignant bone lesions from whole-body bone scintigraphic images.

scholarly journals Tournament leave-pair-out cross-validation for receiver operating characteristic analysis

2018 ◽  
Vol 28 (10-11) ◽  
pp. 2975-2991 ◽  
Author(s):  
Ileana Montoya Perez ◽  
Antti Airola ◽  
Peter J Boström ◽  
Ivan Jambor ◽  
Tapio Pahikkala
Keyword(s):  
Receiver Operating Characteristic Curve ◽  
Receiver Operating Characteristic ◽  
Operating Characteristic ◽  
Cross Validation ◽  
Receiver Operating Characteristic Analysis ◽  
Characteristic Curve ◽  
Diagnostic Systems ◽  
Characteristic Analysis ◽  
Operating Characteristic Curve ◽  
Receiver Operating

Receiver operating characteristic analysis is widely used for evaluating diagnostic systems. Recent studies have shown that estimating an area under receiver operating characteristic curve with standard cross-validation methods suffers from a large bias. The leave-pair-out cross-validation has been shown to correct this bias. However, while leave-pair-out produces an almost unbiased estimate of area under receiver operating characteristic curve, it does not provide a ranking of the data needed for plotting and analyzing the receiver operating characteristic curve. In this study, we propose a new method called tournament leave-pair-out cross-validation. This method extends leave-pair-out by creating a tournament from pair comparisons to produce a ranking for the data. Tournament leave-pair-out preserves the advantage of leave-pair-out for estimating area under receiver operating characteristic curve, while it also allows performing receiver operating characteristic analyses. We have shown using both synthetic and real-world data that tournament leave-pair-out is as reliable as leave-pair-out for area under receiver operating characteristic curve estimation and confirmed the bias in leave-one-out cross-validation on low-dimensional data. As a case study on receiver operating characteristic analysis, we also evaluate how reliably sensitivity and specificity can be estimated from tournament leave-pair-out receiver operating characteristic curves.

scholarly journals Evaluation of corneal topographic, tomographic and biomechanical indices for detecting clinical and subclinical keratoconus: a comprehensive three-device study

2021 ◽  
Vol 14 (2) ◽  
pp. 228-239
Author(s):  
Zahra Heidari ◽  
◽  
Mehrdad Mohammadpour ◽  
Kazem Amanzadeh ◽  
Akbar Fotouhi ◽  
...  
Keyword(s):  
Random Forest ◽  
Early Detection ◽  
Operating Characteristic ◽  
Cross Validation ◽  
Characteristic Curve ◽  
Diagnostic Ability ◽  
Test Study ◽  
Biomechanical Parameters ◽  
Operating Characteristic Curve ◽  
Is Value

AIM: To evaluate the diagnostic ability of topographic and tomographic indices with Pentacam and Sirius as well as biomechanical parameters with Corvis ST for the detection of clinical and subclinical forms of keratoconus (KCN). METHODS: In this prospective diagnostic test study, 70 patients with clinical KCN, 79 patients with abnormal findings in topography and tomography maps with no evidence on clinical examination (subclinical KCN), and 68 normal control subjects were enrolled. The accuracy of topographic, tomographic, and biomechanical parameters was evaluated using the area under the receiver operating characteristic curve (AUC) and cross-validation analysis. The Delong method was used for comparing AUCs. RESULTS: In distinguishing KCN from normal, all parameters showed statistically significant differences between the two groups (P<0.001). Indices with the perfect diagnostic ability (AUC≥0.999) were Sirius KCN vertex of back (KVb), Pentacam random forest index (PRFI), Pentacam index of height decentration (IHD), and Corvis integrated tomographic/biomechanical index (TBI). In distinguishing subclinical KCN from normal, Sirius symmetry index of back (SIb; AUC=0.908), Pentacam inferior-superior difference (IS) value (AUC=0.862), PRFI (AUC=0.847), and Corvis TBI (AUC=0.820) performed best. There were no significant differences between the highest AUCs within keratoconic groups (DeLong, P>0.05). CONCLUSION: In clinical KCN, all topographic, tomographic, and biomechanical indices have acceptable outcomes in terms of sensitivity and specificity. However, in differentiating subclinical forms of KCN from normal corneas, curvature-based parameters (SIb and IS value) followed by integrated indices (PRFI and TBI) are the most powerful tools for early detection of KCN.

Serum miR-21 and miR-26a Levels Negatively Correlate with Severity of Cirrhosis in Patients with Chronic Hepatitis B

MicroRNA ◽  
2018 ◽  
Vol 8 (1) ◽  
pp. 86-92 ◽  
Author(s):  
Shili Jiang ◽  
Wei Jiang ◽  
Ying Xu ◽  
Xiaoning Wang ◽  
Yongping Mu ◽  
...  
Keyword(s):  
Chronic Hepatitis ◽  
Hepatitis B ◽  
Chronic Hepatitis B ◽  
Operating Characteristic ◽  
Characteristic Curve ◽  
Curve Analysis ◽  
Pugh Class ◽  
Operating Characteristic Curve ◽  
Class C ◽  
Circulating Levels

Background and Objective: Accurately evaluating the severity of liver cirrhosis is essential for clinical decision making and disease management. This study aimed to evaluate the value of circulating levels of microRNA (miR)-26a and miR-21 as novel noninvasive biomarkers in detecting severity of cirrhosis in patients with chronic hepatitis B. </P><P> Methods: Thirty patients with clinically diagnosed chronic hepatitis B-related cirrhosis and 30 healthy individuals were selected. The serum levels of miR-26a and miR-21 were quantified by qRT-PCR. Receiver operating characteristic curve analysis was performed to evaluate the sensitivity and specificity of the miRNAs for detecting the severity of cirrhosis. Results: Serum miR-26a and miR-21 levels were found to be significantly downregulated in patients with severe cirrhosis scored at Child-Pugh class C in comparison to healthy controls (miR-26a p<0.01, and miR-21 p<0.001, respectively). The circulating miR-26a and miR-21 levels in patients were positively correlated with serum albumin concentration but negatively correlated with serum total bilirubin concentration and prothrombin time. Receiver operating characteristic curve analysis revealed that both serum miR-26a and miR-21 levels were associated with a high diagnostic accuracy for patients with cirrhosis scored at Child-Pugh class C (miR-26a Cut-off fold change at ≤0.4, Sensitivity: 84.62%, Specificity: 89.36%, P<0.0001; miR-21 Cut-off fold change at ≤0.6, Sensitivity: 84.62%, Specificity: 78.72%, P<0.0001). Our results indicate that the circulating levels of miR-26a and miR-21 are closely related to the extent of liver decompensation, and the decreased levels are capable of discriminating patients with cirrhosis at Child-Pugh class C from the whole cirrhosis cases.

A validated novel preoperative index to predict the extent of intraperitoneal contamination in patients with acute abdominal pathology: A cohort study

2019 ◽  
Vol 30 (7-8) ◽  
pp. 221-228
Author(s):  
Shahab Hajibandeh ◽  
Shahin Hajibandeh ◽  
Nicholas Hobbs ◽  
Jigar Shah ◽  
Matthew Harris ◽  
...  
Keyword(s):  
Receiver Operating Characteristic Curve ◽  
Receiver Operating Characteristic ◽  
Operating Characteristic ◽  
Validation Cohort ◽  
Characteristic Curve ◽  
Curve Analysis ◽  
Emergency Laparotomy ◽  
Contamination Index ◽  
Operating Characteristic Curve ◽  
Receiver Operating

Aims To investigate whether an intraperitoneal contamination index (ICI) derived from combined preoperative levels of C-reactive protein, lactate, neutrophils, lymphocytes and albumin could predict the extent of intraperitoneal contamination in patients with acute abdominal pathology. Methods Patients aged over 18 who underwent emergency laparotomy for acute abdominal pathology between January 2014 and October 2018 were randomly divided into primary and validation cohorts. The proposed intraperitoneal contamination index was calculated for each patient in each cohort. Receiver operating characteristic curve analysis was performed to determine discrimination of the index and cut-off values of preoperative intraperitoneal contamination index that could predict the extent of intraperitoneal contamination. Results Overall, 468 patients were included in this study; 234 in the primary cohort and 234 in the validation cohort. The analyses identified intraperitoneal contamination index of 24.77 and 24.32 as cut-off values for purulent contamination in the primary cohort (area under the curve (AUC): 0.73, P < 0.0001; sensitivity: 84%, specificity: 60%) and validation cohort (AUC: 0.83, P < 0.0001; sensitivity: 91%, specificity: 69%), respectively. Receiver operating characteristic curve analysis also identified intraperitoneal contamination index of 33.70 and 33.41 as cut-off values for feculent contamination in the primary cohort (AUC: 0.78, P < 0.0001; sensitivity: 87%, specificity: 64%) and validation cohort (AUC: 0.79, P < 0.0001; sensitivity: 86%, specificity: 73%), respectively. Conclusions As a predictive measure which is derived purely from biomarkers, intraperitoneal contamination index may be accurate enough to predict the extent of intraperitoneal contamination in patients with acute abdominal pathology and to facilitate decision-making together with clinical and radiological findings.

scholarly journals Evaluation of factors that predict the success rate of trial of labor after the cesarean section

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Yang Mi ◽  
Pengfei Qu ◽  
Na Guo ◽  
Ruimiao Bai ◽  
Jiayi Gao ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Cesarean Section ◽  
Receiver Operating Characteristic Curve ◽  
Success Rate ◽  
Operating Characteristic ◽  
Characteristic Curve ◽  
Predictive Ability ◽  
Training Set ◽  
History Of ◽  
Operating Characteristic Curve

Abstract Background For most women who have had a previous cesarean section, vaginal birth after cesarean section (VBAC) is a reasonable and safe choice, but which will increase the risk of adverse outcomes such as uterine rupture. In order to reduce the risk, we evaluated the factors that may affect VBAC and and established a model for predicting the success rate of trial of the labor after cesarean section (TOLAC). Methods All patients who gave birth at Northwest Women’s and Children’s Hospital from January 2016 to December 2018, had a history of cesarean section and voluntarily chose the TOLAC were recruited. Among them, 80% of the population was randomly assigned to the training set, while the remaining 20% were assigned to the external validation set. In the training set, univariate and multivariate logistic regression models were used to identify indicators related to successful TOLAC. A nomogram was constructed based on the results of multiple logistic regression analysis, and the selected variables included in the nomogram were used to predict the probability of successfully obtaining TOLAC. The area under the receiver operating characteristic curve was used to judge the predictive ability of the model. Results A total of 778 pregnant women were included in this study. Among them, 595 (76.48%) successfully underwent TOLAC, whereas 183 (23.52%) failed and switched to cesarean section. In multi-factor logistic regression, parity = 1, pre-pregnancy BMI < 24 kg/m2, cervical score ≥ 5, a history of previous vaginal delivery and neonatal birthweight < 3300 g were associated with the success of TOLAC. The area under the receiver operating characteristic curve in the prediction and validation models was 0.815 (95% CI: 0.762–0.854) and 0.730 (95% CI: 0.652–0.808), respectively, indicating that the nomogram prediction model had medium discriminative power. Conclusion The TOLAC was useful to reducing the cesarean section rate. Being primiparous, not overweight or obese, having a cervical score ≥ 5, a history of previous vaginal delivery or neonatal birthweight < 3300 g were protective indicators. In this study, the validated model had an approving predictive ability.

The gut hormone GLP-2 predicts cardiovascular risk in patients with acute myocardial infarction

2020 ◽  
Vol 41 (Supplement_2) ◽  
Author(s):  
F Kahles ◽  
R.W Mertens ◽  
M.V Rueckbeil ◽  
M.C Arrivas ◽  
J Moellmann ◽  
...  
Keyword(s):  
Myocardial Infarction ◽  
Cardiovascular Disease ◽  
Acute Myocardial Infarction ◽  
Receiver Operating Characteristic Curve ◽  
Operating Characteristic ◽  
Characteristic Curve ◽  
Funding Source ◽  
Cardiovascular Prognosis ◽  
Operating Characteristic Curve ◽  
Hs Crp

Abstract Background GLP-1 and GLP-2 (glucagon-like peptide-1/2) are gut derived hormones that are co-secreted from intestinal L-cells in response to food intake. While GLP-1 is known to induce postprandial insulin secretion, GLP-2 enhances intestinal nutrient absorption and is clinically used for the treatment of patients with short bowel syndrome. The relevance of the GLP-2 system for cardiovascular disease is unknown. Purpose The aim of this study was to assess the predictive capacity of GLP-2 for cardiovascular prognosis in patients with myocardial infarction. Methods Total GLP-2 levels, NT-proBNP concentrations and the Global Registry of Acute Coronary Events (GRACE) score were assessed at time of admission in 918 patients with myocardial infarction, among them 597 patients with NSTEMI and 321 with STEMI. The primary composite outcome of the study was the first occurrence of cardiovascular death, nonfatal myocardial infarction, or nonfatal stroke (3-P-MACE) with a median follow-up of 311 days. Results Kaplan-Meier survival plots (separated by the median of GLP-2 with a cut-off value of 4.4 ng/mL) and univariable cox regression analyses found GLP-2 values to be associated with adverse outcome (logarithmized GLP-2 values HR: 2.87; 95% CI: 1.75–4.68; p&lt;0.0001). Further adjustment for age, sex, smoking, hypertension, hypercholesterolemia, diabetes mellitus, family history of cardiovascular disease, hs-Troponin T, NT-proBNP and hs-CRP levels did not affect the association of GLP-2 with poor prognosis (logarithmized GLP-2 values HR: 2.96; 95% CI: 1.38–6.34; p=0.0053). Receiver operating characteristic curve (ROC) analyses illustrated that GLP-2 is a strong indicator for cardiovascular events and proved to be comparable to other established risk markers (area under the curve of the combined endpoint at 6 months; GLP-2: 0.72; hs-Troponin: 0.56; NT-proBNP: 0.70; hs-CRP: 0.62). Adjustment of the GRACE risk estimate by GLP-2 increased the area under the receiver-operating characteristic curve for the combined triple endpoint after 6 months from 0.70 (GRACE) to 0.75 (GRACE + GLP-2) in NSTEMI patients. Addition of GLP-2 to a model containing GRACE and NT-proBNP led to a further improvement in model performance (increase in AUC from 0.72 for GRACE + NT-proBNP to 0.77 for GRACE + NT-proBNP + GLP-2). Conclusions In patients admitted with acute myocardial infarction, GLP-2 levels are associated with adverse cardiovascular prognosis. This demonstrates a strong yet not appreciated crosstalk between the heart and the gut with relevance for cardiovascular outcome. Future studies are needed to further explore this crosstalk with the possibility of new treatment avenues for cardiovascular disease. Funding Acknowledgement Type of funding source: Public grant(s) – National budget only. Main funding source(s): German Society of Cardiology (DGK), German Research Foundation (DFG)

scholarly journals Predicting Risk of Antenatal Depression and Anxiety Using Multi-Layer Perceptrons and Support Vector Machines

2021 ◽  
Vol 11 (3) ◽  
pp. 199
Author(s):  
Fajar Javed ◽  
Syed Omer Gilani ◽  
Seemab Latif ◽  
Asim Waris ◽  
Mohsin Jamil ◽  
...  
Keyword(s):  
Low Income ◽  
Operating Characteristic ◽  
Mental Health Problems ◽  
Characteristic Curve ◽  
Antenatal Depression ◽  
Low Income Countries ◽  
Support Vector ◽  
Depression And Anxiety ◽  
Gynecology And Obstetrics ◽  
Operating Characteristic Curve

Perinatal depression and anxiety are defined to be the mental health problems a woman faces during pregnancy, around childbirth, and after child delivery. While this often occurs in women and affects all family members including the infant, it can easily go undetected and underdiagnosed. The prevalence rates of antenatal depression and anxiety worldwide, especially in low-income countries, are extremely high. The wide majority suffers from mild to moderate depression with the risk of leading to impaired child–mother relationship and infant health, few women end up taking their own lives. Owing to high costs and non-availability of resources, it is almost impossible to diagnose every pregnant woman for depression/anxiety whereas under-detection can have a lasting impact on mother and child’s health. This work proposes a multi-layer perceptron based neural network (MLP-NN) classifier to predict the risk of depression and anxiety in pregnant women. We trained and evaluated our proposed system on a Pakistani dataset of 500 women in their antenatal period. ReliefF was used for feature selection before classifier training. Evaluation metrics such as accuracy, sensitivity, specificity, precision, F1 score, and area under the receiver operating characteristic curve were used to evaluate the performance of the trained model. Multilayer perceptron and support vector classifier achieved an area under the receiving operating characteristic curve of 88% and 80% for antenatal depression and 85% and 77% for antenatal anxiety, respectively. The system can be used as a facilitator for screening women during their routine visits in the hospital’s gynecology and obstetrics departments.

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