scholarly journals Machine Learning Approach to Optimize Sedation Use in Endoscopic Procedures

2021 ◽  
Author(s):  
Shorabuddin Syed ◽  
Mahanazuddin Syed ◽  
Fred Prior ◽  
Meredith Zozus ◽  
Hafsa Bareen Syeda ◽  
...  
Keyword(s):  
Machine Learning ◽  
Deep Sedation ◽  
Characteristic Curve ◽  
Decision Support Tool ◽  
University Of Arkansas ◽  
Medical Sciences ◽  
Moderate Sedation ◽  
Support Tool ◽  
Proposed Model ◽  
Machine Learning Approach

Endoscopy procedures are often performed with either moderate or deep sedation. While deep sedation is costly, procedures with moderate sedation are not always well tolerated resulting in patient discomfort, and are often aborted. Due to lack of clear guidelines, the decision to utilize moderate sedation or anesthesia for a procedure is made by the providers, leading to high variability in clinical practice. The objective of this study was to build a Machine Learning (ML) model that predicts if a colonoscopy can be successfully completed with moderate sedation based on patients’ demographics, comorbidities, and prescribed medications. XGBoost model was trained and tested on 10,025 colonoscopies (70% – 30%) performed at University of Arkansas for Medical Sciences (UAMS). XGBoost achieved average area under receiver operating characteristic curve (AUC) of 0.762, F1-score to predict procedures that need moderate sedation was 0.85, and precision and recall were 0.81 and 0.89 respectively. The proposed model can be employed as a decision support tool for physicians to bolster their confidence while choosing between moderate sedation and anesthesia for a colonoscopy procedure.

Telugu News Data Classification Using Machine Learning Approach

2022 ◽  
pp. 181-194
Author(s):  
Bala Krishna Priya G. ◽  
Jabeen Sultana ◽  
Usha Rani M.
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Research Work ◽  
Learning Approach ◽  
Fake News ◽  
Learning Models ◽  
Machine Learning Classifiers ◽  
Proposed Model ◽  
Machine Learning Approach ◽  
Machine Learning Models

Mining Telugu news data and categorizing based on public sentiments is quite important since a lot of fake news emerged with rise of social media. Identifying whether news text is positive, negative, or neutral and later classifying the data in which areas they fall like business, editorial, entertainment, nation, and sports is included throughout this research work. This research work proposes an efficient model by adopting machine learning classifiers to perform classification on Telugu news data. The results obtained by various machine-learning models are compared, and an efficient model is found, and it is observed that the proposed model outperformed with reference to accuracy, precision, recall, and F1-score.

scholarly journals A machine learning approach to predict ethnicity using personal name and census location in Canada

PLoS ONE ◽  
2020 ◽  
Vol 15 (11) ◽  
pp. e0241239
Author(s):  
Kai On Wong ◽  
Osmar R. Zaïane ◽  
Faith G. Davis ◽  
Yutaka Yasui
Keyword(s):  
Machine Learning ◽  
First Nations ◽  
Predictive Value ◽  
Large Scale ◽  
Performance Metrics ◽  
Characteristic Curve ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Learning Approach ◽  
Machine Learning Approach

Background Canada is an ethnically-diverse country, yet its lack of ethnicity information in many large databases impedes effective population research and interventions. Automated ethnicity classification using machine learning has shown potential to address this data gap but its performance in Canada is largely unknown. This study conducted a large-scale machine learning framework to predict ethnicity using a novel set of name and census location features. Methods Using census 1901, the multiclass and binary class classification machine learning pipelines were developed. The 13 ethnic categories examined were Aboriginal (First Nations, Métis, Inuit, and all-combined)), Chinese, English, French, Irish, Italian, Japanese, Russian, Scottish, and others. Machine learning algorithms included regularized logistic regression, C-support vector, and naïve Bayes classifiers. Name features consisted of the entire name string, substrings, double-metaphones, and various name-entity patterns, while location features consisted of the entire location string and substrings of province, district, and subdistrict. Predictive performance metrics included sensitivity, specificity, positive predictive value, negative predictive value, F1, Area Under the Curve for Receiver Operating Characteristic curve, and accuracy. Results The census had 4,812,958 unique individuals. For multiclass classification, the highest performance achieved was 76% F1 and 91% accuracy. For binary classifications for Chinese, French, Italian, Japanese, Russian, and others, the F1 ranged 68–95% (median 87%). The lower performance for English, Irish, and Scottish (F1 ranged 63–67%) was likely due to their shared cultural and linguistic heritage. Adding census location features to the name-based models strongly improved the prediction in Aboriginal classification (F1 increased from 50% to 84%). Conclusions The automated machine learning approach using only name and census location features can predict the ethnicity of Canadians with varying performance by specific ethnic categories.

scholarly journals An Ensemble Prediction Model for Potential Student Recommendation Using Machine Learning

Symmetry ◽  
2020 ◽  
Vol 12 (5) ◽  
pp. 728 ◽  
Author(s):  
Lijuan Yan ◽  
Yanshen Liu
Keyword(s):  
Machine Learning ◽  
Student Performance ◽  
Prediction Models ◽  
Characteristic Curve ◽  
Machine Learning Algorithms ◽  
Ensemble Prediction ◽  
Support Vector ◽  
Proposed Model ◽  
Importance Analysis ◽  
Better Than

Student performance prediction has become a hot research topic. Most of the existing prediction models are built by a machine learning method. They are interested in prediction accuracy but pay less attention to interpretability. We propose a stacking ensemble model to predict and analyze student performance in academic competition. In this model, student performance is classified into two symmetrical categorical classes. To improve accuracy, three machine learning algorithms, including support vector machine (SVM), random forest, and AdaBoost are established in the first level and then integrated by logistic regression via stacking. A feature importance analysis was applied to identify important variables. The experimental data were collected from four academic years in Hankou University. According to comparative studies on five evaluation metrics (precision, recall, F1, error, and area   under   the   receiver   operating   characteristic   curve ( AUC ) in this analysis, the proposed model generally performs better than compared models. The important variables identified from the analysis are interpretable, they can be used as guidance to select potential students.

Using Prognostics and Health Monitoring Data in Load Distribution Optimization Problems

2020 ◽  
Author(s):  
Leonardo R. Rodrigues ◽  
Vandilberto Pereira Pinto
Keyword(s):  
Load Distribution ◽  
Optimization Problems ◽  
Health Management ◽  
Short Interval ◽  
Decision Support Tool ◽  
Industrial Applications ◽  
Remaining Useful Life ◽  
Support Tool ◽  
Multidimensional Knapsack ◽  
Proposed Model

The use of Remaining Useful Life (RUL) predictions as a decision support tool has increased in recent years. The RUL predictions can be obtained from Prognostics and Health Management (PHM) systems that monitor the health status and estimate the failure instant of components and systems. An example of a decision-making problem that can benet from RUL predictions is the load distribution problem, which is a common problem that appears in many industrial applications. It consists in dening how to distribute a task among a set of components. In this paper, a model to solve load distribution optimization problems is proposed. The proposed model considers the RUL prediction of each component in its formulation. Also, the proposed model assumes that the predicted RUL of each component is a function of the load assigned to that component. Thus, it is possible to distribute the load to avoid multiplecomponents to fail in a short interval. An approach based on the MMKP (Multiple-choice Multidimensional Knapsack Problem) is adopted. The proposed model nds a load distribution that minimizes the operational cost subject to a maintenance personnel capacity constraint, i.e. there is a maximum number of components that can be simultaneously on repair. A numerical case study considering a gas compressor station is presented to illustrate the application of theproposed model.

A Machine-Learning Clinical Decision Support Tool for Myocardial Infarction Diagnosis

2021 ◽  
Vol 28 ◽  
pp. S13
Author(s):  
Saarang Panchavati ◽  
Carson Lam ◽  
Anurag Garikipati ◽  
Nicole Zelin ◽  
Emily Pellegrini ◽  
...  
Keyword(s):  
Machine Learning ◽  
Myocardial Infarction ◽  
Decision Support ◽  
Clinical Decision Support ◽  
Decision Support Tool ◽  
Clinical Decision ◽  
Support Tool ◽  
Clinical Decision Support Tool

scholarly journals Predicting the naturalistic course in anxiety disorders using clinical and biological markers: a machine learning approach

2020 ◽  
pp. 1-11
Author(s):  
Wicher A. Bokma ◽  
Paul Zhutovsky ◽  
Erik J. Giltay ◽  
Robert A. Schoevers ◽  
Brenda W.J.H. Penninx ◽  
...  
Keyword(s):  
Machine Learning ◽  
Anxiety Disorders ◽  
Common Mental Disorders ◽  
Characteristic Curve ◽  
Chronically Ill ◽  
Learning Approach ◽  
Machine Learning Approach ◽  
And Biological Markers

Abstract Background Disease trajectories of patients with anxiety disorders are highly diverse and approximately 60% remain chronically ill. The ability to predict disease course in individual patients would enable personalized management of these patients. This study aimed to predict recovery from anxiety disorders within 2 years applying a machine learning approach. Methods In total, 887 patients with anxiety disorders (panic disorder, generalized anxiety disorder, agoraphobia, or social phobia) were selected from a naturalistic cohort study. A wide array of baseline predictors (N = 569) from five domains (clinical, psychological, sociodemographic, biological, lifestyle) were used to predict recovery from anxiety disorders and recovery from all common mental disorders (CMDs: anxiety disorders, major depressive disorder, dysthymia, or alcohol dependency) at 2-year follow-up using random forest classifiers (RFCs). Results At follow-up, 484 patients (54.6%) had recovered from anxiety disorders. RFCs achieved a cross-validated area-under-the-receiving-operator-characteristic-curve (AUC) of 0.67 when using the combination of all predictor domains (sensitivity: 62.0%, specificity 62.8%) for predicting recovery from anxiety disorders. Classification of recovery from CMDs yielded an AUC of 0.70 (sensitivity: 64.6%, specificity: 62.3%) when using all domains. In both cases, the clinical domain alone provided comparable performances. Feature analysis showed that prediction of recovery from anxiety disorders was primarily driven by anxiety features, whereas recovery from CMDs was primarily driven by depression features. Conclusions The current study showed moderate performance in predicting recovery from anxiety disorders over a 2-year follow-up for individual patients and indicates that anxiety features are most indicative for anxiety improvement and depression features for improvement in general.

scholarly journals Development and Validation of a Machine-Learning-Based Decision Support Tool for Residency Applicant Screening and Review

2021 ◽  
Vol Publish Ahead of Print ◽  
Author(s):  
Jesse Burk-Rafel ◽  
Ilan Reinstein ◽  
James Feng ◽  
Moosun Brad Kim ◽  
Louis H. Miller ◽  
...  
Keyword(s):  
Machine Learning ◽  
Decision Support ◽  
Decision Support Tool ◽  
Support Tool ◽  
Applicant Screening ◽  
Development And Validation

scholarly journals Student risk identification learning model using machine learning approach

2019 ◽  
Vol 9 (5) ◽  
pp. 3872
Author(s):  
Susheelamma K. H. ◽  
K. M. Ravikumar
Keyword(s):  
Machine Learning ◽  
Imbalanced Data ◽  
Learning Model ◽  
Risk Identification ◽  
Learning Approach ◽  
Applied Machine Learning ◽  
Proposed Model ◽  
Machine Learning Approach ◽  
Significant Performance ◽  
Student Risk

<p class="Abstract">Several challenges are associated with online based learning systems, the most important of which is the lack of student motivation in various course materials and for various course activities. Further, it is important to identify student who are at risk of failing to complete the course on time. The existing models applied machine learning approach for solving it. However, these models are not efficient as they are trained using legacy data and also failed to address imbalanced data issues for both training and testing the classification approach. Further, they are not efficient for classifying new courses. For overcoming these research challenges, this work presented a novel design by training the learning model for identifying risk using current courses. Further, we present an XGBoost classification algorithm that can classify risk for new courses. Experiments are conducted to evaluate performance of proposed model. The outcome shows the proposed model attain significant performance over stat-of-art model in terms of ROC, F-measure, Precision and Recall.</p>

scholarly journals Clinician Pre-Implementation Perspectives of a Decision-Support Tool for Prediction of Cardiac Arrhythmia based on Machine Learning: Near-Live Feasibility and Qualitative Study (Preprint)

10.2196/26964 ◽  
2021 ◽  
Author(s):  
Stina Matthiesen ◽  
Søren Zöga Diederichsen ◽  
Mikkel Klitzing Hartmann Hansen ◽  
Christina Villumsen ◽  
Mats Christian Højbjerg Lassen ◽  
...  
Keyword(s):  
Machine Learning ◽  
Decision Support ◽  
Qualitative Study ◽  
Cardiac Arrhythmia ◽  
Decision Support Tool ◽  
Support Tool
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