Machine Learning in Medicine

Cleophas, Ton J.; Zwinderman, Aeilko H.

doi:10.1007/978-94-007-7869-6

Cited by 25 publications

(34 citation statements)

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“…11,12 ML methods are more precise and accurate in terms of prediction abilities compared with traditional statistical methods, because complex intervariable interactions are taken into account in ML only. 13 XGBoost is a well-known ML technique that utilizes boosting decision trees algorithm to build a prediction model and has been utilized to predict the outcome of cystic fibrosis based on spirometry measures. 14,15 Amaral et al 16…”

Section: Machine Learningmentioning

confidence: 99%

“…Machine learning (ML) is an algorithm‐based novel modeling technique that has been introduced recently to pulmonary research including chronic obstructive pulmonary disease (COPD) . ML methods are more precise and accurate in terms of prediction abilities compared with traditional statistical methods, because complex intervariable interactions are taken into account in ML only . XGBoost is a well‐known ML technique that utilizes boosting decision trees algorithm to build a prediction model and has been utilized to predict the outcome of cystic fibrosis based on spirometry measures .…”

Section: Introductionmentioning

confidence: 99%

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Diagnostic value of spirometry vs impulse oscillometry: A comparative study in children with sickle cell disease

Mondal

Yirinec

Midya

et al. 2019

Pediatric Pulmonology

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Background Spirometry is conventionally used to diagnose airway diseases in children with sickle cell disease (C‐SCD). However, spirometry is difficult for younger children to perform, is effort dependent, and it provides limited information on respiratory mechanics. Impulse oscillometry (IOS) is an effort‐independent pulmonary function test (PFT), which measures total airway resistance (R5Hz) and reactance (AX). IOS could be advantageous without certain limitations of spirometry. Aim To compare the accuracy of IOS vs spirometry in making the diagnosis of asthma and assessing age‐related pulmonary changes in C‐SCD. Study design Retrospective chart review. Subject selection Fifty‐six C‐SCD and thirty‐six controls (asthmatics without SCD) followed at Penn State with PFTs obtained during the initial pulmonary evaluation. Methodology We grouped C‐SCD into asthmatics and non‐asthmatics based on pre‐referral diagnosis and compared PFTs between two groups. Receiver operating characteristic (ROC) curve analyses and machine learning tools (XGBoost and artificial neural network) were used to rank the spirometry and IOS measures based on their ability to predict a diagnosis of asthma. Robust linear regression was used to analyze association among height/age with various PFT measures. Results Both ROC and XGBoost indicated that FEF25‐75%, forced expiratory volume in 1 second (FEV1)/forced vital capacity, and R5Hz(%) were the top three predictors for asthma diagnosis. R5Hz(%) and AX had superior bronchodilator response (BDR) than FEV1. IOS parameters had significant association with height/age in C‐SCD (possibly due to the stiff lungs) but not in controls. Conclusion IOS had advantages over spirometry in C‐SCD because it is feasible in early childhood, provides insights into the pulmonary mechanics, and is more sensitive to detect BDR.

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Section: Machine Learningmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Diagnostic value of spirometry vs impulse oscillometry: A comparative study in children with sickle cell disease

Mondal

Yirinec

Midya

et al. 2019

Pediatric Pulmonology

View full text Add to dashboard Cite

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“…Recent applications in materials science include texture analysis in micrographs18192021222324. However, compared to the widespread use in other domains such as medical diagnostics2526, computational finances2728, and natural language processing29, by and large, applications of machine learning in materials science are in their infancy. This is particularly true for exploratory learning from experimental data, which has been mostly limited to simple latent variable analysis techniques such as principle component analysis.…”

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confidence: 99%

On-the-fly machine-learning for high-throughput experiments: search for rare-earth-free permanent magnets

Kusne

Gao

Mehta

et al. 2014

Sci Rep

241

168

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Advanced materials characterization techniques with ever-growing data acquisition speed and storage capabilities represent a challenge in modern materials science, and new procedures to quickly assess and analyze the data are needed. Machine learning approaches are effective in reducing the complexity of data and rapidly homing in on the underlying trend in multi-dimensional data. Here, we show that by employing an algorithm called the mean shift theory to a large amount of diffraction data in high-throughput experimentation, one can streamline the process of delineating the structural evolution across compositional variations mapped on combinatorial libraries with minimal computational cost. Data collected at a synchrotron beamline are analyzed on the fly, and by integrating experimental data with the inorganic crystal structure database (ICSD), we can substantially enhance the accuracy in classifying the structural phases across ternary phase spaces. We have used this approach to identify a novel magnetic phase with enhanced magnetic anisotropy which is a candidate for rare-earth free permanent magnet.

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“…The preliminary simulation results of these ML methods have been encouraging and motivated the use of these methods for this study. Many examples of the application of ML are existing in medical literature (Kononenko, ; Cleophas, Zwinderman, & Cleophas‐Allers, ; Deo, ; Obermeyer & Emanuel, ). A detailed coverage of the ML methods tested is out of the scope of this paper; however, details on these methods can be referenced from studies such as, Kononenko (), Waljee and Higgins (), and Alpaydin () amongst others.…”

Section: Predictive Models Developmentmentioning

confidence: 99%

Identifying mortality risk factors amongst acute coronary syndrome patients admitted to Arabian Gulf hospitals using machine‐learning methods

et al. 2019

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Acute coronary syndrome (ACS) is a leading cause of mortality and morbidity in the Arabian Gulf. In this study, the in‐hospital mortality amongst patients admitted with ACS to Arabian Gulf hospitals is predicted using a comprehensive modelling framework that combines powerful machine‐learning methods such as support‐vector machine (SVM), Naïve Bayes (NB), artificial neural networks (NN), and decision trees (DT). The performance of the machine‐learning methods is compared with that of the performance of a commonly used statistical method, namely, logistic regression (LR). The study follows the current practise of computing mortality risk using risk scores such as the Global Registry of Acute Coronary Events (GRACE) score, which has not been validated for Arabian Gulf patients. Cardiac registry data of 7,000 patients from 65 hospitals located in Arabian Gulf countries are used for the study. This study is unique as it uses a contemporary data analytics framework. A k‐fold (k = 10) cross‐validation is utilized to generate training and validation samples from the GRACE dataset. The machine‐learning‐based predictive models often incur prejudgments for imbalanced training data patterns. To mitigate the data imbalance due to scarce observations for in‐hospital mortalities, we have utilized specialized methods such as random undersampling (RUS) and synthetic minority over sampling technique (SMOTE). A detailed simulation experimentation is carried out to build models with each of the five predictive methods (LR, NN, NB, SVM, and DT) for the each of the three datasets k‐fold subsamples generated. The predictive models are developed under three schemes of the k‐fold samples that include no data imbalance, RUS, and SMOTE. We have implemented an information fusion method rooted in computing weighted impact scores obtain for an individual medical history attributes from each of the predictive models simulated for a collective recommendation based on an impact score specific to a predictor. Finally, we grouped the predictors using fuzzy c‐mean clustering method into three categories, high‐, medium‐, and low‐risk factors for in‐hospital mortality due to ACS. Our study revealed that patients with medical history related to the presences of peripheral artery disease, congestive heart failure, cardiovascular transient ischemic attack valvular disease, and coronary artery bypass grafting amongst others have the most risk for in‐hospital mortality.

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Machine Learning in Medicine

Cited by 25 publications

References 0 publications

Diagnostic value of spirometry vs impulse oscillometry: A comparative study in children with sickle cell disease

Diagnostic value of spirometry vs impulse oscillometry: A comparative study in children with sickle cell disease

On-the-fly machine-learning for high-throughput experiments: search for rare-earth-free permanent magnets

Identifying mortality risk factors amongst acute coronary syndrome patients admitted to Arabian Gulf hospitals using machine‐learning methods

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