scholarly journals Derivation of an electronic frailty index for short-term mortality in heart failure: a machine learning approach

2021 ◽  
Author(s):  
Chengsheng Ju ◽  
Jiandong Zhou ◽  
Sharen Lee ◽  
Martin Sebastian Tan ◽  
Ying Liu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Heart Failure ◽  
Logistic Regression ◽  
Decision Tree ◽  
Frailty Index ◽  
Gradient Boosting ◽  
Importance Score ◽  
Multivariate Logistic Regression ◽  
Short Term ◽  
Short Term Mortality

AbstractObjectiveFrailty may be found in heart failure patients especially in the elderly and is associated with a poor prognosis. However, assessment of frailty status is time-consuming and the electronic frailty indices developed using health records have served as useful surrogates. We hypothesized that an electronic frailty index developed using machine learning can improve short-term mortality prediction in patients with heart failure.MethodsThis was a retrospective observational study included patients admitted to nine public hospitals for heart failure from Hong Kong between 2013 and 2017. Age, sex, variables in the modified frailty index, Deyo’s Charlson comorbidity index (≥2), neutrophil-to-lymphocyte ratio (NLR) and prognostic nutritional index (PNI) were analyzed. Gradient boosting, which is a supervised sequential ensemble learning algorithm with weak prediction submodels (typically decision trees), was applied to predict mortality. Comparisons were made with decision tree and multivariate logistic regression.ResultsA total of 8893 patients (median: age 81, Q1-Q3: 71-87 years old) were included, in whom 9% had 30-day mortality and 17% had 90-day mortality. PNI, age and NLR were the most important variables predicting 30-day mortality (importance score: 37.4, 32.1, 20.5, respectively) and 90-day mortality (importance score: 35.3, 36.3, 14.6, respectively). Gradient boosting significantly outperformed decision tree and multivariate logistic regression (area under the curve: 0.90, 0.86 and 0.86 for 30-day mortality; 0.92, 0.89 and 0.86 for 90-day mortality).ConclusionsThe electronic frailty index based on comorbidities, inflammation and nutrition information can readily predict mortality outcomes. Their predictive performances were significantly improved by gradient boosting techniques.

scholarly journals Predicting Metabolic Syndrome With Machine Learning Models Using a Decision Tree Algorithm: Retrospective Cohort Study (Preprint)

2019 ◽  
Author(s):  
Cheng-Sheng Yu ◽  
Yu-Jiun Lin ◽  
Chang-Hsien Lin ◽  
Sen-Te Wang ◽  
Shiyng-Yu Lin ◽  
...  
Keyword(s):  
Machine Learning ◽  
Metabolic Syndrome ◽  
Logistic Regression ◽  
Decision Tree ◽  
Characteristic Curve ◽  
Significant Risk ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Health Examination ◽  
Multivariate Logistic Regression

BACKGROUND Metabolic syndrome is a cluster of disorders that significantly influence the development and deterioration of numerous diseases. FibroScan is an ultrasound device that was recently shown to predict metabolic syndrome with moderate accuracy. However, previous research regarding prediction of metabolic syndrome in subjects examined with FibroScan has been mainly based on conventional statistical models. Alternatively, machine learning, whereby a computer algorithm learns from prior experience, has better predictive performance over conventional statistical modeling. OBJECTIVE We aimed to evaluate the accuracy of different decision tree machine learning algorithms to predict the state of metabolic syndrome in self-paid health examination subjects who were examined with FibroScan. METHODS Multivariate logistic regression was conducted for every known risk factor of metabolic syndrome. Principal components analysis was used to visualize the distribution of metabolic syndrome patients. We further applied various statistical machine learning techniques to visualize and investigate the pattern and relationship between metabolic syndrome and several risk variables. RESULTS Obesity, serum glutamic-oxalocetic transaminase, serum glutamic pyruvic transaminase, controlled attenuation parameter score, and glycated hemoglobin emerged as significant risk factors in multivariate logistic regression. The area under the receiver operating characteristic curve values for classification and regression trees and for the random forest were 0.831 and 0.904, respectively. CONCLUSIONS Machine learning technology facilitates the identification of metabolic syndrome in self-paid health examination subjects with high accuracy.

scholarly journals Predicting Metabolic Syndrome With Machine Learning Models Using a Decision Tree Algorithm: Retrospective Cohort Study

10.2196/17110 ◽  
2020 ◽  
Vol 8 (3) ◽  
pp. e17110 ◽  
Author(s):  
Cheng-Sheng Yu ◽  
Yu-Jiun Lin ◽  
Chang-Hsien Lin ◽  
Sen-Te Wang ◽  
Shiyng-Yu Lin ◽  
...  
Keyword(s):  
Machine Learning ◽  
Metabolic Syndrome ◽  
Logistic Regression ◽  
Decision Tree ◽  
Characteristic Curve ◽  
Significant Risk ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Health Examination ◽  
Multivariate Logistic Regression

Background Metabolic syndrome is a cluster of disorders that significantly influence the development and deterioration of numerous diseases. FibroScan is an ultrasound device that was recently shown to predict metabolic syndrome with moderate accuracy. However, previous research regarding prediction of metabolic syndrome in subjects examined with FibroScan has been mainly based on conventional statistical models. Alternatively, machine learning, whereby a computer algorithm learns from prior experience, has better predictive performance over conventional statistical modeling. Objective We aimed to evaluate the accuracy of different decision tree machine learning algorithms to predict the state of metabolic syndrome in self-paid health examination subjects who were examined with FibroScan. Methods Multivariate logistic regression was conducted for every known risk factor of metabolic syndrome. Principal components analysis was used to visualize the distribution of metabolic syndrome patients. We further applied various statistical machine learning techniques to visualize and investigate the pattern and relationship between metabolic syndrome and several risk variables. Results Obesity, serum glutamic-oxalocetic transaminase, serum glutamic pyruvic transaminase, controlled attenuation parameter score, and glycated hemoglobin emerged as significant risk factors in multivariate logistic regression. The area under the receiver operating characteristic curve values for classification and regression trees and for the random forest were 0.831 and 0.904, respectively. Conclusions Machine learning technology facilitates the identification of metabolic syndrome in self-paid health examination subjects with high accuracy.

scholarly journals Swindling Shonky Anatomization of Credit Card Transactions using Machine Learning

2019 ◽  
Vol 8 (4) ◽  
pp. 1477-1483
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Random Forest ◽  
Decision Tree ◽  
Credit Card ◽  
Naive Bayes ◽  
Gradient Boosting ◽  
Decision Tree Classifier ◽  
Tree Classifier ◽  
Feature Importance

With the fast moving technological advancement, the internet usage has been increased rapidly in all the fields. The money transactions for all the applications like online shopping, banking transactions, bill settlement in any industries, online ticket booking for travel and hotels, Fees payment for educational organization, Payment for treatment to hospitals, Payment for super market and variety of applications are using online credit card transactions. This leads to the fraud usage of other accounts and transaction that result in the loss of service and profit to the institution. With this background, this paper focuses on predicting the fraudulent credit card transaction. The Credit Card Transaction dataset from KAGGLE machine learning Repository is used for prediction analysis. The analysis of fraudulent credit card transaction is achieved in four ways. Firstly, the relationship between the variables of the dataset is identified and represented by the graphical notations. Secondly, the feature importance of the dataset is identified using Random Forest, Ada boost, Logistic Regression, Decision Tree, Extra Tree, Gradient Boosting and Naive Bayes classifiers. Thirdly, the extracted feature importance if the credit card transaction dataset is fitted to Random Forest classifier, Ada boost classifier, Logistic Regression classifier, Decision Tree classifier, Extra Tree classifier, Gradient Boosting classifier and Naive Bayes classifier. Fourth, the Performance Analysis is done by analyzing the performance metrics like Accuracy, FScore, AUC Score, Precision and Recall. The implementation is done by python in Anaconda Spyder Navigator Integrated Development Environment. Experimental Results shows that the Decision Tree classifier have achieved the effective prediction with the precision of 1.0, recall of 1.0, FScore of 1.0 , AUC Score of 89.09 and Accuracy of 99.92%.

Derivation and implementation of a machine learning approach to prompt serious illness conversations among outpatients with cancer.

2019 ◽  
Vol 37 (31_suppl) ◽  
pp. 131-131
Author(s):  
Ravi Bharat Parikh ◽  
Chris Manz ◽  
Corey Chivers ◽  
Susan B Regli ◽  
Jennifer Braun ◽  
...  
Keyword(s):  
Machine Learning ◽  
At Risk ◽  
High Risk ◽  
Risk Group ◽  
Gradient Boosting ◽  
Short Term ◽  
Serious Illness ◽  
Patients With Cancer ◽  
Short Term Mortality ◽  
The Impact

131 Background: Machine learning (ML) algorithms can accurately identify patients with cancer at risk of short-term mortality and facilitate timely conversations about treatment and end-of-life preferences. We developed, validated, and implemented a ML algorithm to predict mortality in a general oncology setting, using electronic health record (EHR) data prior to a clinic visit. Methods: Our cohort consisted of patients aged ≥18 years who had an encounter in outpatient oncology practices within a large academic health system between February 1st and July 1st, 2016. We randomly split the sample into training (70%) and validation (30%) cohorts at the patient-encounter level. We trained three ML algorithms to predict 180-day mortality and describe performance in the holdout validation cohort. From October 2018 to February 2019, we used the best-performing algorithm to generate weekly lists of high-risk patients at a single community oncology practice and studied the impact on rates of documented serious illness conversations (SICs). Results: Among 62,377 encounters used to train the algorithms, 7.4% involved a patient who died within 180 days. Gradient boosting and/or random forest outperformed logistic regression in all metrics (Table), and the gradient boosting model had superior discrimination and calibration. In the gradient boosting model, observed 180-day mortality was 45.5% (95% CI 39.0-52.3%) in the high-risk group vs. 3.3% (95% CI 2.9-3.7%) in the low-risk group. In a survey of oncology clinicians, 59% of patients flagged as high-risk were appropriate for a serious illness conversation in the upcoming week (response rate 52%). Five months after implementing the intervention, average monthly documented SICs increased by 23% (31.7 to 39). Conclusions: A ML algorithm based on EHR data accurately identified patients with cancer at risk of short-term mortality, was concordant with oncologists’ assessments, and was associated with more SICs. [Table: see text]

Association Between 5-Item Modified Frailty Index and Short-term Outcomes in Complex Head and Neck Surgery

2021 ◽  
pp. 019459982110104
Author(s):  
Khodayar Goshtasbi ◽  
Jack L. Birkenbeuel ◽  
Brandon M. Lehrich ◽  
Arash Abiri ◽  
Yarah M. Haidar ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Head And Neck ◽  
Frailty Index ◽  
Medical Complications ◽  
Multivariate Logistic Regression ◽  
Short Term ◽  
Improvement Program ◽  
Acs Nsqip ◽  
Modified Frailty Index ◽  
The Impact

Objectives To evaluate the impact of preoperative frailty on short-term outcomes following complex head and neck surgeries (HNSs). Study Design Cross-sectional database analysis. Setting American College of Surgeons National Surgical Quality Improvement Program (ACS-NSQIP) database. Methods The 2005 to 2017 ACS-NSQIP was queried for patients undergoing complex HNS. Five-item modified frailty index (mFI) was calculated based on functional status and history of diabetes, chronic obstructive pulmonary disease, congestive heart failure, and chronic hypertension. Results A total of 2786 patients (73.1% male) with a mean age of 62.0 ± 11.6 years were included. Compared to nonfrail patients (41.2%), patients with mFI ≥1 (58.8%) had shorter length of operation ( P = .021), longer length of stay (LOS) ( P < .001), and higher rates of 30-day reoperation ( P = .009), medical complications ( P < .001), discharge to nonhome facility (DNHF) ( P < .001), and mortality ( P = .047). These parameters remained statistically significant when compared across all individual mFI scores (all P < .05). After adjusting for age, sex, race, body mass index, smoking, and American Society of Anesthesiologists score via multivariate logistic regression, patients with mFI ≥1 were significantly more likely to undergo reoperation (odds ratio [OR], 1.39), surgical complications (OR, 1.19), medical complications (OR, 1.55), prolonged LOS (OR, 1.29), and DNHF (OR, 1.56) (all P < .05). Multivariate logistic regression also demonstrated that after adjusting for confounders, compared to patients with mFI = 1, patients with mFI = 2-5 (18.7%) were more likely to undergo shorter operations (OR, 0.74), have medical (OR, 1.46) or any complications (OR, 1.27), and have DNHF (OR, 1.62) (all P < .05). Conclusion The 5-point mFI can independently predict short-term surgical outcomes following complex HNS. This simple and reliable metric can potentially lead to improved preoperative counseling and postoperative planning for complex HNS patients.

AN EFFICIENT MACHINE LEARNING MODEL FOR PREDICTION OF ACUTE MYOCARDIAL INFARCTION

2020 ◽  
Vol 13 ◽  
Author(s):  
Dhilsath Fathima.M ◽  
S. Justin Samuel ◽  
R. Hari Haran
Keyword(s):  
Machine Learning ◽  
Myocardial Infarction ◽  
Acute Myocardial Infarction ◽  
Logistic Regression ◽  
Decision Tree ◽  
Learning Model ◽  
Training Dataset ◽  
Data Set ◽  
Machine Learning Model ◽  
Proposed Model

Aim: This proposed work is used to develop an improved and robust machine learning model for predicting Myocardial Infarction (MI) could have substantial clinical impact. Objectives: This paper explains how to build machine learning based computer-aided analysis system for an early and accurate prediction of Myocardial Infarction (MI) which utilizes framingham heart study dataset for validation and evaluation. This proposed computer-aided analysis model will support medical professionals to predict myocardial infarction proficiently. Methods: The proposed model utilize the mean imputation to remove the missing values from the data set, then applied principal component analysis to extract the optimal features from the data set to enhance the performance of the classifiers. After PCA, the reduced features are partitioned into training dataset and testing dataset where 70% of the training dataset are given as an input to the four well-liked classifiers as support vector machine, k-nearest neighbor, logistic regression and decision tree to train the classifiers and 30% of test dataset is used to evaluate an output of machine learning model using performance metrics as confusion matrix, classifier accuracy, precision, sensitivity, F1-score, AUC-ROC curve. Results: Output of the classifiers are evaluated using performance measures and we observed that logistic regression provides high accuracy than K-NN, SVM, decision tree classifiers and PCA performs sound as a good feature extraction method to enhance the performance of proposed model. From these analyses, we conclude that logistic regression having good mean accuracy level and standard deviation accuracy compared with the other three algorithms. AUC-ROC curve of the proposed classifiers is analyzed from the output figure.4, figure.5 that logistic regression exhibits good AUC-ROC score, i.e. around 70% compared to k-NN and decision tree algorithm. Conclusion: From the result analysis, we infer that this proposed machine learning model will act as an optimal decision making system to predict the acute myocardial infarction at an early stage than an existing machine learning based prediction models and it is capable to predict the presence of an acute myocardial Infarction with human using the heart disease risk factors, in order to decide when to start lifestyle modification and medical treatment to prevent the heart disease.

Short-Term Outcomes Following Removal of Infected Hernia Mesh

2021 ◽  
pp. 000313482110241
Author(s):  
Christine Tung ◽  
Junko Ozao-Choy ◽  
Dennis Y. Kim ◽  
Christian de Virgilio ◽  
Ashkan Moazzez
Keyword(s):  
Risk Factors ◽  
Body Mass Index ◽  
Logistic Regression ◽  
Body Mass ◽  
Smoking History ◽  
Multivariate Logistic Regression ◽  
Short Term ◽  
Mesh Removal ◽  
Mesh Placement ◽  
Infected Mesh

There are limited studies regarding outcomes of replacing an infected mesh with another mesh. We reviewed short-term outcomes following infected mesh removal and whether placement of new mesh is associated with worse outcomes. Patients who underwent hernia repair with infected mesh removal were identified from 2005 to 2018 American College of Surgeons-National Surgical Quality Improvement Program database. They were divided into new mesh (Mesh+) or no mesh (Mesh-) groups. Bivariate and multivariate logistic regression analyses were used to compare morbidity between the two groups and to identify associated risk factors. Of 1660 patients, 49.3% received new mesh, with higher morbidity in the Mesh+ (35.9% vs. 30.3%; P = .016), but without higher rates of surgical site infection (SSI) (21.3% vs. 19.7%; P = .465). Mesh+ had higher rates of acute kidney injury (1.3% vs. .4%; P = .028), UTI (3.1% vs. 1.3%, P = .014), ventilator dependence (4.9% vs. 2.4%; P = .006), and longer LOS (8.6 vs. 7 days, P < .001). Multivariate logistic regression showed new mesh placement (OR: 1.41; 95% CI: 1.07-1.85; P = .014), body mass index (OR: 1.02; 95% CI: 1.00-1.03; P = .022), and smoking (OR: 1.43; 95% CI: 1.05-1.95; P = .025) as risk factors independently associated with increased morbidity. New mesh placement at time of infected mesh removal is associated with increased morbidity but not with SSI. Body mass index and smoking history continue to contribute to postoperative morbidity during subsequent operations for complications.

scholarly journals Prediction of short-term mortality in acute heart failure patients using minimal electronic health record data

2021 ◽  
Vol 14 (1) ◽  
Author(s):  
Ashwath Radhachandran ◽  
Anurag Garikipati ◽  
Nicole S. Zelin ◽  
Emily Pellegrini ◽  
Sina Ghandian ◽  
...  
Keyword(s):  
Heart Failure ◽  
Logistic Regression ◽  
Decision Tree ◽  
Risk Stratification ◽  
Acute Heart Failure ◽  
Model Performance ◽  
Machine Learning Techniques ◽  
Electronic Health Record Data ◽  
Test Set ◽  
Patient Disposition

Abstract Background Acute heart failure (AHF) is associated with significant morbidity and mortality. Effective patient risk stratification is essential to guiding hospitalization decisions and the clinical management of AHF. Clinical decision support systems can be used to improve predictions of mortality made in emergency care settings for the purpose of AHF risk stratification. In this study, several models for the prediction of seven-day mortality among AHF patients were developed by applying machine learning techniques to retrospective patient data from 236,275 total emergency department (ED) encounters, 1881 of which were considered positive for AHF and were used for model training and testing. The models used varying subsets of age, sex, vital signs, and laboratory values. Model performance was compared to the Emergency Heart Failure Mortality Risk Grade (EHMRG) model, a commonly used system for prediction of seven-day mortality in the ED with similar (or, in some cases, more extensive) inputs. Model performance was assessed in terms of area under the receiver operating characteristic curve (AUROC), sensitivity, and specificity. Results When trained and tested on a large academic dataset, the best-performing model and EHMRG demonstrated test set AUROCs of 0.84 and 0.78, respectively, for prediction of seven-day mortality. Given only measurements of respiratory rate, temperature, mean arterial pressure, and FiO2, one model produced a test set AUROC of 0.83. Neither a logistic regression comparator nor a simple decision tree outperformed EHMRG. Conclusions A model using only the measurements of four clinical variables outperforms EHMRG in the prediction of seven-day mortality in AHF. With these inputs, the model could not be replaced by logistic regression or reduced to a simple decision tree without significant performance loss. In ED settings, this minimal-input risk stratification tool may assist clinicians in making critical decisions about patient disposition by providing early and accurate insights into individual patient’s risk profiles.

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