Using a Guided Machine Learning Ensemble Model to Predict Discharge Disposition following Meningioma Resection

Objective Machine learning (ML) algorithms are powerful tools for predicting patient outcomes. This study pilots a novel approach to algorithm selection and model creation using prediction of discharge disposition following meningioma resection as a proof of concept. Materials and Methods A diversity of ML algorithms were trained on a single-institution database of meningioma patients to predict discharge disposition. Algorithms were ranked by predictive power and top performers were combined to create an ensemble model. The final ensemble was internally validated on never-before-seen data to demonstrate generalizability. The predictive power of the ensemble was compared with a logistic regression. Further analyses were performed to identify how important variables impact the ensemble. Results Our ensemble model predicted disposition significantly better than a logistic regression (area under the curve of 0.78 and 0.71, respectively, p = 0.01). Tumor size, presentation at the emergency department, body mass index, convexity location, and preoperative motor deficit most strongly influence the model, though the independent impact of individual variables is nuanced. Conclusion Using a novel ML technique, we built a guided ML ensemble model that predicts discharge destination following meningioma resection with greater predictive power than a logistic regression, and that provides greater clinical insight than a univariate analysis. These techniques can be extended to predict many other patient outcomes of interest.

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A proof-of-concept study applying machine learning methods to putative risk factors for eating disorders: results from the multi-centre European project on healthy eating

Psychological Medicine ◽

10.1017/s003329172100489x ◽

2021 ◽

pp. 1-10

Author(s):

I. Krug ◽

J. Linardon ◽

C. Greenwood ◽

G. Youssef ◽

J. Treasure ◽

...

Keyword(s):

Machine Learning ◽

Risk Factors ◽

Logistic Regression ◽

Predictive Accuracy ◽

Area Under The Curve ◽

Prediction Rule ◽

Predictive Performance ◽

Individual Risk ◽

European Project ◽

Wide Range

Abstract Background Despite a wide range of proposed risk factors and theoretical models, prediction of eating disorder (ED) onset remains poor. This study undertook the first comparison of two machine learning (ML) approaches [penalised logistic regression (LASSO), and prediction rule ensembles (PREs)] to conventional logistic regression (LR) models to enhance prediction of ED onset and differential ED diagnoses from a range of putative risk factors. Method Data were part of a European Project and comprised 1402 participants, 642 ED patients [52% with anorexia nervosa (AN) and 40% with bulimia nervosa (BN)] and 760 controls. The Cross-Cultural Risk Factor Questionnaire, which assesses retrospectively a range of sociocultural and psychological ED risk factors occurring before the age of 12 years (46 predictors in total), was used. Results All three statistical approaches had satisfactory model accuracy, with an average area under the curve (AUC) of 86% for predicting ED onset and 70% for predicting AN v. BN. Predictive performance was greatest for the two regression methods (LR and LASSO), although the PRE technique relied on fewer predictors with comparable accuracy. The individual risk factors differed depending on the outcome classification (EDs v. non-EDs and AN v. BN). Conclusions Even though the conventional LR performed comparably to the ML approaches in terms of predictive accuracy, the ML methods produced more parsimonious predictive models. ML approaches offer a viable way to modify screening practices for ED risk that balance accuracy against participant burden.

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Predicting hospitalization following psychiatric crisis care using machine learning

BMC Medical Informatics and Decision Making ◽

10.1186/s12911-020-01361-1 ◽

2020 ◽

Vol 20 (1) ◽

Author(s):

Matthijs Blankers ◽

Louk F. M. van der Post ◽

Jack J. M. Dekker

Keyword(s):

Machine Learning ◽

Logistic Regression ◽

Prediction Models ◽

Learning Algorithms ◽

Nearest Neighbors ◽

Machine Learning Algorithms ◽

Gradient Boosting ◽

Ensemble Model ◽

K Nearest Neighbors ◽

Crisis Care

Abstract Background Accurate prediction models for whether patients on the verge of a psychiatric criseis need hospitalization are lacking and machine learning methods may help improve the accuracy of psychiatric hospitalization prediction models. In this paper we evaluate the accuracy of ten machine learning algorithms, including the generalized linear model (GLM/logistic regression) to predict psychiatric hospitalization in the first 12 months after a psychiatric crisis care contact. We also evaluate an ensemble model to optimize the accuracy and we explore individual predictors of hospitalization. Methods Data from 2084 patients included in the longitudinal Amsterdam Study of Acute Psychiatry with at least one reported psychiatric crisis care contact were included. Target variable for the prediction models was whether the patient was hospitalized in the 12 months following inclusion. The predictive power of 39 variables related to patients’ socio-demographics, clinical characteristics and previous mental health care contacts was evaluated. The accuracy and area under the receiver operating characteristic curve (AUC) of the machine learning algorithms were compared and we also estimated the relative importance of each predictor variable. The best and least performing algorithms were compared with GLM/logistic regression using net reclassification improvement analysis and the five best performing algorithms were combined in an ensemble model using stacking. Results All models performed above chance level. We found Gradient Boosting to be the best performing algorithm (AUC = 0.774) and K-Nearest Neighbors to be the least performing (AUC = 0.702). The performance of GLM/logistic regression (AUC = 0.76) was slightly above average among the tested algorithms. In a Net Reclassification Improvement analysis Gradient Boosting outperformed GLM/logistic regression by 2.9% and K-Nearest Neighbors by 11.3%. GLM/logistic regression outperformed K-Nearest Neighbors by 8.7%. Nine of the top-10 most important predictor variables were related to previous mental health care use. Conclusions Gradient Boosting led to the highest predictive accuracy and AUC while GLM/logistic regression performed average among the tested algorithms. Although statistically significant, the magnitude of the differences between the machine learning algorithms was in most cases modest. The results show that a predictive accuracy similar to the best performing model can be achieved when combining multiple algorithms in an ensemble model.

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Predicting Inpatient Length of Stay After Brain Tumor Surgery: Developing Machine Learning Ensembles to Improve Predictive Performance

Neurosurgery ◽

10.1093/neuros/nyy343 ◽

2018 ◽

Vol 85 (3) ◽

pp. 384-393 ◽

Cited By ~ 12

Author(s):

Whitney E Muhlestein ◽

Dallin S Akagi ◽

Jason M Davies ◽

Lola B Chambless

Keyword(s):

Machine Learning ◽

Brain Tumor ◽

Length Of Stay ◽

Patient Outcomes ◽

External Validation ◽

Training Dataset ◽

Ensemble Model ◽

Improvement Program ◽

Improve Patient ◽

Inpatient Length Of Stay

Abstract BACKGROUND Current outcomes prediction tools are largely based on and limited by regression methods. Utilization of machine learning (ML) methods that can handle multiple diverse inputs could strengthen predictive abilities and improve patient outcomes. Inpatient length of stay (LOS) is one such outcome that serves as a surrogate for patient disease severity and resource utilization. OBJECTIVE To develop a novel method to systematically rank, select, and combine ML algorithms to build a model that predicts LOS following craniotomy for brain tumor. METHODS A training dataset of 41 222 patients who underwent craniotomy for brain tumor was created from the National Inpatient Sample. Twenty-nine ML algorithms were trained on 26 preoperative variables to predict LOS. Trained algorithms were ranked by calculating the root mean square logarithmic error (RMSLE) and top performing algorithms combined to form an ensemble. The ensemble was externally validated using a dataset of 4592 patients from the National Surgical Quality Improvement Program. Additional analyses identified variables that most strongly influence the ensemble model predictions. RESULTS The ensemble model predicted LOS with RMSLE of .555 (95% confidence interval, .553-.557) on internal validation and .631 on external validation. Nonelective surgery, preoperative pneumonia, sodium abnormality, or weight loss, and non-White race were the strongest predictors of increased LOS. CONCLUSION An ML ensemble model predicts LOS with good performance on internal and external validation, and yields clinical insights that may potentially improve patient outcomes. This systematic ML method can be applied to a broad range of clinical problems to improve patient care.

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Predicting mortality in hemodialysis patients using machine learning analysis

Clinical Kidney Journal ◽

10.1093/ckj/sfaa126 ◽

2020 ◽

Author(s):

Victoria Garcia-Montemayor ◽

Alejandro Martin-Malo ◽

Carlo Barbieri ◽

Francesco Bellocchio ◽

Sagrario Soriano ◽

...

Keyword(s):

Machine Learning ◽

Logistic Regression ◽

Random Forest ◽

Prediction Models ◽

Initial Period ◽

Area Under The Curve ◽

Mortality Prediction ◽

Machine Learning Techniques ◽

Prediction Of Mortality ◽

Mortality Prediction Models

Abstract Background Besides the classic logistic regression analysis, non-parametric methods based on machine learning techniques such as random forest are presently used to generate predictive models. The aim of this study was to evaluate random forest mortality prediction models in haemodialysis patients. Methods Data were acquired from incident haemodialysis patients between 1995 and 2015. Prediction of mortality at 6 months, 1 year and 2 years of haemodialysis was calculated using random forest and the accuracy was compared with logistic regression. Baseline data were constructed with the information obtained during the initial period of regular haemodialysis. Aiming to increase accuracy concerning baseline information of each patient, the period of time used to collect data was set at 30, 60 and 90 days after the first haemodialysis session. Results There were 1571 incident haemodialysis patients included. The mean age was 62.3 years and the average Charlson comorbidity index was 5.99. The mortality prediction models obtained by random forest appear to be adequate in terms of accuracy [area under the curve (AUC) 0.68–0.73] and superior to logistic regression models (ΔAUC 0.007–0.046). Results indicate that both random forest and logistic regression develop mortality prediction models using different variables. Conclusions Random forest is an adequate method, and superior to logistic regression, to generate mortality prediction models in haemodialysis patients.

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Predicting the Grade of Prostate Cancer Based on a Biparametric MRI Radiomics Signature

Contrast Media & Molecular Imaging ◽

10.1155/2021/7830909 ◽

2021 ◽

Vol 2021 ◽

pp. 1-10

Author(s):

Li Zhang ◽

Xia Zhe ◽

Min Tang ◽

Jing Zhang ◽

Jialiang Ren ◽

...

Keyword(s):

Prostate Cancer ◽

Machine Learning ◽

Logistic Regression ◽

Learning Algorithm ◽

Area Under The Curve ◽

Machine Learning Algorithms ◽

Support Vector ◽

Low Grade ◽

High Grade ◽

Random Forest Tree

Purpose. This study aimed to investigate the value of biparametric magnetic resonance imaging (bp-MRI)-based radiomics signatures for the preoperative prediction of prostate cancer (PCa) grade compared with visual assessments by radiologists based on the Prostate Imaging Reporting and Data System Version 2.1 (PI-RADS V2.1) scores of multiparametric MRI (mp-MRI). Methods. This retrospective study included 142 consecutive patients with histologically confirmed PCa who were undergoing mp-MRI before surgery. MRI images were scored and evaluated by two independent radiologists using PI-RADS V2.1. The radiomics workflow was divided into five steps: (a) image selection and segmentation, (b) feature extraction, (c) feature selection, (d) model establishment, and (e) model evaluation. Three machine learning algorithms (random forest tree (RF), logistic regression, and support vector machine (SVM)) were constructed to differentiate high-grade from low-grade PCa. Receiver operating characteristic (ROC) analysis was used to compare the machine learning-based analysis of bp-MRI radiomics models with PI-RADS V2.1. Results. In all, 8 stable radiomics features out of 804 extracted features based on T2-weighted imaging (T2WI) and ADC sequences were selected. Radiomics signatures successfully categorized high-grade and low-grade PCa cases ( P < 0.05 ) in both the training and test datasets. The radiomics model-based RF method (area under the curve, AUC: 0.982; 0.918), logistic regression (AUC: 0.886; 0.886), and SVM (AUC: 0.943; 0.913) in both the training and test cohorts had better diagnostic performance than PI-RADS V2.1 (AUC: 0.767; 0.813) when predicting PCa grade. Conclusions. The results of this clinical study indicate that machine learning-based analysis of bp-MRI radiomic models may be helpful for distinguishing high-grade and low-grade PCa that outperformed the PI-RADS V2.1 scores based on mp-MRI. The machine learning algorithm RF model was slightly better.

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Machine Learning Algorithms Using Logistic Regression for Predicting Neurosurgical Outcomes

10.21203/rs.3.rs-37934/v1 ◽

2020 ◽

Author(s):

Nida Fatima

Keyword(s):

Machine Learning ◽

Logistic Regression ◽

Predictive Model ◽

Web Application ◽

Learning Algorithms ◽

Area Under The Curve ◽

Machine Learning Algorithms ◽

Brier Score ◽

Patient Counselling ◽

Selection Of Variables

Abstract Background: Preoperative prognostication of clinical and surgical outcome in patients with neurosurgical diseases can improve the risk stratification, thus can guide in implementing targeted treatment to minimize these events. Therefore, the author aims to highlight the development and validation of predictive models determining neurosurgical outcomes through machine learning algorithms using logistic regression.Methods: Logistic regression (enter, backward and forward) and least absolute shrinkage and selection operator (LASSO) method for selection of variables from selected database can eventually lead to multiple candidate models. The final model with a set of predictive variables must be selected based upon the clinical knowledge and numerical results.Results: The predictive model which performed best on the discrimination, calibration, Brier score and decision curve analysis must be selected to develop machine learning algorithms. Logistic regression should be compared with the LASSO model. Usually for the big databases, the predictive model selected through logistic regression gives higher Area Under the Curve (AUC) than those with LASSO model. The predictive probability derived from the best model could be uploaded to an open access web application which is easily deployed by the patients and surgeons to make a risk assessment world-wide.Conclusions: Machine learning algorithms provide promising results for the prediction of outcomes following cranial and spinal surgery. These algorithms can provide useful factors for patient-counselling, assessing peri-operative risk factors, and predicting post-operative outcomes after neurosurgery.

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Predictive models and under-five mortality determinants in Ethiopia: evidence from the 2016 Ethiopian Demographic and Health Survey

10.21203/rs.2.13113/v1 ◽

2019 ◽

Author(s):

Fikrewold Bitew ◽

Samuel H. Nyarko ◽

Lloyd Potter ◽

Corey S. Sparks

Keyword(s):

Machine Learning ◽

Logistic Regression ◽

Random Forest ◽

Health Survey ◽

Mortality Risk ◽

Predictive Models ◽

Predictive Power ◽

Proportional Hazard ◽

Under Five ◽

Cox Proportional Hazard

Abstract Background There is a dearth of literature on predictive models estimating under-five mortality risk in Ethiopia. In this study, we develop a spatial map and predictive models to predict the sociodemographic determinants of under-five mortality in Ethiopia.Methods The study data were drawn from the 2016 Ethiopian Demographic and Health Survey. We used machine learning algorithms such as random forest, logistic regression, and Cox-proportional hazard models to predict the sociodemographic risks for under-five mortality in Ethiopia. The Receiver Operating Characteristic curve was used to evaluate the predictive power of the models.Results There are considerable regional variations in under-five mortality rates in Ethiopia. The under-five mortality prediction ability was found to be 88.7% for the random forest model, 68.3% for the logistic regression model, and 68.0% for the Cox-Proportional Hazard model. Maternal age at birth, sex of a child, previous birth interval, water source, contraceptive use, health facility delivery services, antenatal and post-natal care checkups have been found to be significantly associated with under-five mortality in Ethiopia.Conclusions The random forest machine learning algorithm produces a higher predictive power for under-five mortality risk factors for the study sample. There is a need to improve the quality and access to health care services to enhance childhood survival chances in the country.

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Construction and Comparison of Predictive Models for Length of Stay after Total Knee Arthroplasty: Regression Model and Machine Learning Analysis Based on 1,826 Cases in a Single Singapore Center

The Journal of Knee Surgery ◽

10.1055/s-0040-1710573 ◽

2020 ◽

Author(s):

Hui Li ◽

Juyang Jiao ◽

Shutao Zhang ◽

Haozheng Tang ◽

Xinhua Qu ◽

...

Keyword(s):

Machine Learning ◽

Total Knee Arthroplasty ◽

Logistic Regression ◽

Length Of Stay ◽

Knee Arthroplasty ◽

Predictive Models ◽

Prediction Models ◽

Univariate Analysis ◽

Machine Learning Algorithms ◽

Total Knee

AbstractThe purpose of this study was to develop a predictive model for length of stay (LOS) after total knee arthroplasty (TKA). Between 2013 and 2014, 1,826 patients who underwent TKA from a single Singapore center were enrolled in the study after qualification. Demographics of patients with normal and prolonged LOS were analyzed. The risk variables that could affect LOS were identified by univariate analysis. Predictive models for LOS after TKA by logistic regression or machine learning were constructed and compared. The univariate analysis showed that age, American Society of Anesthesiologist level, diabetes, ischemic heart disease, congestive heart failure, general anesthesia, and operation duration were risk factors that could affect LOS (p < 0.05). Comparing with logistic regression models, the machine learning model with all variables was the best model to predict LOS after TKA, of whose area of operator characteristic curve was 0.738. Machine learning algorithms improved the predictive performance of LOS prediction models for TKA patients.

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Machine Learning Approach to Differentiation of Peripheral Schwannomas and Neurofibromas: A Multi-Center Study

Neuro-Oncology ◽

10.1093/neuonc/noab211 ◽

2021 ◽

Author(s):

Michael Zhang ◽

Elizabeth Tong ◽

Sam Wong ◽

Forrest Hamrick ◽

Maryam Mohammadzadeh ◽

...

Keyword(s):

Machine Learning ◽

Logistic Regression ◽

Clinical Features ◽

Nearest Neighbor ◽

Motor Deficit ◽

Support Vector ◽

Spontaneous Pain ◽

Learning Approaches ◽

Imaging Data ◽

K Nearest Neighbor

Abstract Background Non-invasive differentiation between schwannomas and neurofibromas is important for appropriate management, preoperative counseling, and surgical planning, but has proven difficult using conventional imaging. The objective of this study was to develop and evaluate machine learning approaches for differentiating peripheral schwannomas from neurofibromas. Methods We assembled a cohort of schwannomas and neurofibromas from 3 independent institutions and extracted high-dimensional radiomic features from gadolinium-enhanced, T1-weighted MRI using the PyRadiomics package on Quantitative Imaging Feature Pipeline. Age, sex, neurogenetic syndrome, spontaneous pain, and motor deficit were recorded. We evaluated the performance of 6 radiomics-based classifier models with and without clinical features and compared model performance against human expert evaluators. Results 107 schwannomas and 59 neurofibroma were included. The primary models included both clinical and imaging data. The accuracy of the human evaluators (0.765) did not significantly exceed the no-information rate (NIR), whereas the Support Vector Machine (0.929), Logistic Regression (0.929), and Random Forest (0.905) classifiers exceeded the NIR. Using the method of DeLong, the AUC for the Logistic Regression (AUC=0.923) and K Nearest Neighbor (AUC=0.923) classifiers was significantly greater than the human evaluators (AUC=0.766; p = 0.041). Conclusions The radiomics-based classifiers developed here proved to be more accurate and had a higher AUC on the ROC curve than expert human evaluators. This demonstrates that radiomics using routine MRI sequences and clinical features can aid in differentiation of peripheral schwannomas and neurofibromas.

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Feature Selection to Win the Point of ATP Tennis Players Using Rally Information

International Journal of Computer Science in Sport ◽

10.2478/ijcss-2020-0003 ◽

2020 ◽

Vol 19 (1) ◽

pp. 37-50

Author(s):

M. Makino ◽

T. Odaka ◽

J. Kuroiwa ◽

I. Suwa ◽

H. Shirai

Keyword(s):

Machine Learning ◽

Logistic Regression ◽

Feature Selection ◽

Area Under The Curve ◽

Prominent Feature ◽

Learning Models ◽

Tennis Players ◽

Machine Learning Models ◽

Tennis Match

AbstractIn tennis, the accumulation of data has progressed and research on tactical analysis has been conducted. Estimating strategically important factors would have the benefit of providing players with useful advice and helping audience members understand what tennis players are good at. Previous research has been conducted into ways of predicting Association of Tennis Professionals (ATP) tennis match outcomes as well as estimating factors that are important for victories using machine learning models. The challenge of previous research is that the victory factor lacks concreteness. Since we thought the root of the abovementioned problem was that previous researchers used game summary as a feature and did not consider the process of rallies between points, this research focused on calculating the frequency of single shots, two-shot patterns, and specific effective shot patterns from each point rally of ATP singles matches. We then used those data to predict point winners and useful features using L1-regularized logistic regression. The highest accuracy obtained was 66.5%, and the area under the curve (AUC) was 0.689. The most prominent feature we found was the ratio of specific shots by specific players. From these results, our method could reveal more concretely tactical factors than previous studies.

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