Machine-Learning Techniques for Feature Selection and Prediction of Mortality in Elderly CABG Patients

Coronary artery bypass surgery grafting (CABG) is a commonly efficient treatment for coronary artery disease patients. Even if we know the underlying disease, and advancing age is related to survival, there is no research using the one year before surgery and operation-associated factors as predicting elements. This research used different machine-learning methods to select the features and predict older adults’ survival (more than 65 years old). This nationwide population-based cohort study used the National Health Insurance Research Database (NHIRD), the largest and most complete dataset in Taiwan. We extracted the data of older patients who had received their first CABG surgery criteria between January 2008 and December 2009 (n = 3728), and we used five different machine-learning methods to select the features and predict survival rates. The results show that, without variable selection, XGBoost had the best predictive ability. Upon selecting XGBoost and adding the CHA2DS score, acute pancreatitis, and acute kidney failure for further predictive analysis, MARS had the best prediction performance, and it only needed 10 variables. This study’s advantages are that it is innovative and useful for clinical decision making, and machine learning could achieve better prediction with fewer variables. If we could predict patients’ survival risk before a CABG operation, early prevention and disease management would be possible.

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COVID Mortality Prediction with Machine Learning Methods: A Systematic Review and Critical Appraisal

Journal of Personalized Medicine ◽

10.3390/jpm11090893 ◽

2021 ◽

Vol 11 (9) ◽

pp. 893

Author(s):

Francesca Bottino ◽

Emanuela Tagliente ◽

Luca Pasquini ◽

Alberto Di Napoli ◽

Martina Lucignani ◽

...

Keyword(s):

Machine Learning ◽

Intensive Care Unit ◽

Clinical Decision Making ◽

Clinical Decision ◽

Mortality Prediction ◽

Machine Learning Techniques ◽

Learning Methods ◽

Machine Learning Methods ◽

Learning Techniques ◽

Ventilation Modes

More than a year has passed since the report of the first case of coronavirus disease 2019 (COVID), and increasing deaths continue to occur. Minimizing the time required for resource allocation and clinical decision making, such as triage, choice of ventilation modes and admission to the intensive care unit is important. Machine learning techniques are acquiring an increasingly sought-after role in predicting the outcome of COVID patients. Particularly, the use of baseline machine learning techniques is rapidly developing in COVID mortality prediction, since a mortality prediction model could rapidly and effectively help clinical decision-making for COVID patients at imminent risk of death. Recent studies reviewed predictive models for SARS-CoV-2 diagnosis, severity, length of hospital stay, intensive care unit admission or mechanical ventilation modes outcomes; however, systematic reviews focused on prediction of COVID mortality outcome with machine learning methods are lacking in the literature. The present review looked into the studies that implemented machine learning, including deep learning, methods in COVID mortality prediction thus trying to present the existing published literature and to provide possible explanations of the best results that the studies obtained. The study also discussed challenging aspects of current studies, providing suggestions for future developments.

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Machine-learning based prediction of Cushing’s syndrome in dogs attending UK primary-care veterinary practice

Scientific Reports ◽

10.1038/s41598-021-88440-z ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Imogen Schofield ◽

David C. Brodbelt ◽

Noel Kennedy ◽

Stijn J. M. Niessen ◽

David B. Church ◽

...

Keyword(s):

Machine Learning ◽

Cushing’S Syndrome ◽

Clinical Decision Making ◽

Predictive Performance ◽

Clinical Decision ◽

Cushing's Syndrome ◽

Machine Learning Algorithms ◽

Learning Methods ◽

Machine Learning Methods ◽

Clinical Records

AbstractCushing’s syndrome is an endocrine disease in dogs that negatively impacts upon the quality-of-life of affected animals. Cushing’s syndrome can be a challenging diagnosis to confirm, therefore new methods to aid diagnosis are warranted. Four machine-learning algorithms were applied to predict a future diagnosis of Cushing's syndrome, using structured clinical data from the VetCompass programme in the UK. Dogs suspected of having Cushing's syndrome were included in the analysis and classified based on their final reported diagnosis within their clinical records. Demographic and clinical features available at the point of first suspicion by the attending veterinarian were included within the models. The machine-learning methods were able to classify the recorded Cushing’s syndrome diagnoses, with good predictive performance. The LASSO penalised regression model indicated the best overall performance when applied to the test set with an AUROC = 0.85 (95% CI 0.80–0.89), sensitivity = 0.71, specificity = 0.82, PPV = 0.75 and NPV = 0.78. The findings of our study indicate that machine-learning methods could predict the future diagnosis of a practicing veterinarian. New approaches using these methods could support clinical decision-making and contribute to improved diagnosis of Cushing’s syndrome in dogs.

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The rise and fall of machine learning methods in biomedical research

F1000Research ◽

10.12688/f1000research.13016.1 ◽

2017 ◽

Vol 6 ◽

pp. 2012 ◽

Cited By ~ 6

Author(s):

Hashem Koohy

Keyword(s):

Machine Learning ◽

Biomedical Research ◽

Life Sciences ◽

Biological Data ◽

Research Note ◽

Machine Learning Techniques ◽

Learning Methods ◽

The Past ◽

Machine Learning Methods ◽

Learning Techniques

In the era of explosion in biological data, machine learning techniques are becoming more popular in life sciences, including biology and medicine. This research note examines the rise and fall of the most commonly used machine learning techniques in life sciences over the past three decades.

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Effects of Brain Atlases and Machine Learning Methods on the Discrimination of Schizophrenia Patients: A Multimodal MRI Study

Frontiers in Neuroscience ◽

10.3389/fnins.2021.697168 ◽

2021 ◽

Vol 15 ◽

Author(s):

Jinyu Zang ◽

Yuanyuan Huang ◽

Lingyin Kong ◽

Bingye Lei ◽

Pengfei Ke ◽

...

Keyword(s):

Machine Learning ◽

Dimensionality Reduction ◽

Cross Validation ◽

Integrated Model ◽

Machine Learning Techniques ◽

Brain Atlas ◽

First Episode ◽

Learning Methods ◽

Machine Learning Methods ◽

Brain Atlases

Recently, machine learning techniques have been widely applied in discriminative studies of schizophrenia (SZ) patients with multimodal magnetic resonance imaging (MRI); however, the effects of brain atlases and machine learning methods remain largely unknown. In this study, we collected MRI data for 61 first-episode SZ patients (FESZ), 79 chronic SZ patients (CSZ) and 205 normal controls (NC) and calculated 4 MRI measurements, including regional gray matter volume (GMV), regional homogeneity (ReHo), amplitude of low-frequency fluctuation and degree centrality. We systematically analyzed the performance of two classifications (SZ vs NC; FESZ vs CSZ) based on the combinations of three brain atlases, five classifiers, two cross validation methods and 3 dimensionality reduction algorithms. Our results showed that the groupwise whole-brain atlas with 268 ROIs outperformed the other two brain atlases. In addition, the leave-one-out cross validation was the best cross validation method to select the best hyperparameter set, but the classification performances by different classifiers and dimensionality reduction algorithms were quite similar. Importantly, the contributions of input features to both classifications were higher with the GMV and ReHo features of brain regions in the prefrontal and temporal gyri. Furthermore, an ensemble learning method was performed to establish an integrated model, in which classification performance was improved. Taken together, these findings indicated the effects of these factors in constructing effective classifiers for psychiatric diseases and showed that the integrated model has the potential to improve the clinical diagnosis and treatment evaluation of SZ.

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MR Image-Based Attenuation Correction of Brain PET Imaging: Review of Literature on Machine Learning Approaches for Segmentation

Journal of Digital Imaging ◽

10.1007/s10278-020-00361-x ◽

2020 ◽

Vol 33 (5) ◽

pp. 1224-1241 ◽

Cited By ~ 1

Author(s):

Imene Mecheter ◽

Lejla Alic ◽

Maysam Abbod ◽

Abbes Amira ◽

Jim Ji

Keyword(s):

Machine Learning ◽

Image Segmentation ◽

Attenuation Correction ◽

Machine Learning Techniques ◽

Mr Image ◽

Learning Methods ◽

Simple Method ◽

Machine Learning Methods ◽

Brain Pet ◽

Mr Image Segmentation

Abstract Recent emerging hybrid technology of positron emission tomography/magnetic resonance (PET/MR) imaging has generated a great need for an accurate MR image-based PET attenuation correction. MR image segmentation, as a robust and simple method for PET attenuation correction, has been clinically adopted in commercial PET/MR scanners. The general approach in this method is to segment the MR image into different tissue types, each assigned an attenuation constant as in an X-ray CT image. Machine learning techniques such as clustering, classification and deep networks are extensively used for brain MR image segmentation. However, only limited work has been reported on using deep learning in brain PET attenuation correction. In addition, there is a lack of clinical evaluation of machine learning methods in this application. The aim of this review is to study the use of machine learning methods for MR image segmentation and its application in attenuation correction for PET brain imaging. Furthermore, challenges and future opportunities in MR image-based PET attenuation correction are discussed.

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Combining Correlation-Based Feature and Machine Learning for Sensory Evaluation of Saigon Beer

International Journal of Knowledge and Systems Science ◽

10.4018/ijkss.2020040104 ◽

2020 ◽

Vol 11 (2) ◽

pp. 71-85

Author(s):

Nhat-Vinh Lu ◽

Trong-Nhan Vuong ◽

Duy-Tai Dinh

Keyword(s):

Machine Learning ◽

Sensory Evaluation ◽

Machine Learning Techniques ◽

Support Vector ◽

Learning Methods ◽

Feature Selection Technique ◽

Machine Learning Methods ◽

Learning Techniques ◽

Correlation Based Feature Selection ◽

Positive Results

Sensory evaluation plays an important role in the food and consumer goods industry. In recent years, the application of machine learning techniques to support food sensory evaluation has become popular. Many different machine learning methods have been applied and produced positive results in this field. In this article, the authors propose a new method to support sensory evaluation on multiple criteria based on the use of a correlation-based feature selection technique, combined with machine learning methods such as linear regression, multilayer perceptron, support vector machine, and random forest. Experimental results are based on considering the correlation between physicochemical components and sensory factors on the Saigon beer dataset.

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Review of machine learning methods in soft robotics

PLoS ONE ◽

10.1371/journal.pone.0246102 ◽

2021 ◽

Vol 16 (2) ◽

pp. e0246102

Author(s):

Daekyum Kim ◽

Sang-Hun Kim ◽

Taekyoung Kim ◽

Brian Byunghyun Kang ◽

Minhyuk Lee ◽

...

Keyword(s):

Machine Learning ◽

Soft Materials ◽

Research Field ◽

Machine Learning Techniques ◽

Learning Approaches ◽

Learning Methods ◽

Soft Robots ◽

Machine Learning Methods ◽

Wearable Robots ◽

Soft Actuators

Soft robots have been extensively researched due to their flexible, deformable, and adaptive characteristics. However, compared to rigid robots, soft robots have issues in modeling, calibration, and control in that the innate characteristics of the soft materials can cause complex behaviors due to non-linearity and hysteresis. To overcome these limitations, recent studies have applied various approaches based on machine learning. This paper presents existing machine learning techniques in the soft robotic fields and categorizes the implementation of machine learning approaches in different soft robotic applications, which include soft sensors, soft actuators, and applications such as soft wearable robots. An analysis of the trends of different machine learning approaches with respect to different types of soft robot applications is presented; in addition to the current limitations in the research field, followed by a summary of the existing machine learning methods for soft robots.

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Recent Progress in Smart Electronic Nose Technologies Enabled with Machine Learning Methods

Sensors ◽

10.3390/s21227620 ◽

2021 ◽

Vol 21 (22) ◽

pp. 7620

Author(s):

Zhenyi Ye ◽

Yuan Liu ◽

Qiliang Li

Keyword(s):

Machine Learning ◽

Feature Extraction ◽

Gas Sensor ◽

Electronic Nose ◽

Recent Progress ◽

Machine Learning Techniques ◽

Learning Methods ◽

Machine Learning Methods ◽

Sensor Drift ◽

Drift Compensation

Machine learning methods enable the electronic nose (E-Nose) for precise odor identification with both qualitative and quantitative analysis. Advanced machine learning methods are crucial for the E-Nose to gain high performance and strengthen its capability in many applications, including robotics, food engineering, environment monitoring, and medical diagnosis. Recently, many machine learning techniques have been studied, developed, and integrated into feature extraction, modeling, and gas sensor drift compensation. The purpose of feature extraction is to keep robust pattern information in raw signals while removing redundancy and noise. With the extracted feature, a proper modeling method can effectively use the information for prediction. In addition, drift compensation is adopted to relieve the model accuracy degradation due to the gas sensor drifting. These recent advances have significantly promoted the prediction accuracy and stability of the E-Nose. This review is engaged to provide a summary of recent progress in advanced machine learning methods in E-Nose technologies and give an insight into new research directions in feature extraction, modeling, and sensor drift compensation.

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Identification of Duplication in Questions Posed on Knowledge Sharing Platform Quora using Machine Learning Techniques

International Journal of Innovative Technology and Exploring Engineering - Special Issue ◽

10.35940/ijitee.l3017.1081219 ◽

2019 ◽

Vol 8 (12) ◽

pp. 2444-2451

Keyword(s):

Machine Learning ◽

Question Answering ◽

Contextual Information ◽

Machine Learning Techniques ◽

Learning Methods ◽

Machine Learning Methods ◽

Comparison Methods ◽

Learning Techniques ◽

Letter Comparison ◽

Lower Accuracy

Quora, an online question-answering platform has a lot of duplicate questions i.e. questions that convey the same meaning. Since it is open to all users, anyone can pose a question any number of times this increases the count of duplicate questions. This paper uses a dataset comprising of question pairs (taken from the Quora website) in different columns with an indication of whether the pair of questions are duplicates or not. Traditional comparison methods like Sequence matcher perform a letter by letter comparison without understanding the contextual information, hence they give lower accuracy. Machine learning methods predict the similarity using features extracted from the context. Both the traditional methods as well as the machine learning methods were compared in this study. The features for the machine learning methods are extracted using the Bag of Words models- Count-Vectorizer and TFIDF-Vectorizer. Among the traditional comparison methods, Sequence matcher gave the highest accuracy of 65.29%. Among the machine learning methods XGBoost gave the highest accuracy, 80.89% when Count-Vectorizer is used and 80.12% when TFIDF-Vectorizer is used.

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Machine Learning Methods for Prediction of Hospital Mortality in Patients with Coronary Heart Disease after Coronary Artery Bypass Grafting

Kardiologiia ◽

10.18087/cardio.2020.10.n1170 ◽

2020 ◽

Vol 60 (10) ◽

pp. 38-46

Author(s):

B. I. Geltser ◽

K. J. Shahgeldyan ◽

V. Y. Rublev ◽

V. N. Kotelnikov ◽

A. B. Krieger ◽

...

Keyword(s):

Machine Learning ◽

Heart Disease ◽

Clinical Laboratory ◽

Artery Bypass ◽

Fatal Outcome ◽

Left Ventricular ◽

Learning Technologies ◽

Relative Mass ◽

Learning Methods ◽

Machine Learning Methods

Aim To compare the accuracy of predicting an in-hospital fatal outcome for models based on current machine-learning technologies in patients with ischemic heart disease (IHD) after coronary bypass (CB) surgery.Material and methods A retrospective analysis of 866 electronic medical records was performed for patients (685 men and 181 women) who have had a CB surgery for IHD in 2008–2018. Results of clinical, laboratory, and instrumental evaluations obtained prior to the CB surgery were analyzed. Patients were divided into two groups: group 1 included 35 (4 %) patients who died within the first 20 days of CB, and group 2 consisted of 831 (96 %) patients with a beneficial outcome of the surgery. Predictors of the in-hospital fatal outcome were identified by a multistep selection procedure with analysis of statistical hypotheses and calculation of weight coefficients. For construction of models and verification of predictors, machine-learning methods were used, including the multifactorial logistic regression (LR), random forest (RF), and artificial neural networks (ANN). Model accuracy was evaluated by three metrics: area under the ROC curve (AUC), sensitivity, and specificity. Cross validation of the models was performed on test samples, and the control validation was performed on a cohort of patients with IHD after CB, whose data were not used in development of the models.Results The following 7 risk factors for in-hospital fatal outcome with the greatest predictive potential were isolated from the EuroSCORE II scale: ejection fraction (EF) <30 %, EF 30-50 %, age of patients with recent MI, damage of peripheral arterial circulation, urgency of CB, functional class III-IV chronic heart failure, and 5 additional predictors, including heart rate, systolic blood pressure, presence of aortic stenosis, posterior left ventricular (LV) wall relative thickness index (RTI), and LV relative mass index (LVRMI). The models developed by the authors using LR, RF and ANN methods had higher AUC values and sensitivity compared to the classical EuroSCORE II scale. The ANN models including the RTI and LVRMI predictors demonstrated a maximum level of prognostic accuracy, which was illustrated by values of the quality metrics, AUC 93 %, sensitivity 90 %, and specificity 96 %. The predictive robustness of the models was confirmed by results of the control validation.Conclusion The use of current machine-learning technologies allowed developing a novel algorithm for selection of predictors and highly accurate models for predicting an in-hospital fatal outcome after CB.

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