Using machine learning to identify patient characteristics to predict mortality of in-patients with COVID-19 in South Florida

Using machine learning to identify patient characteristics to predict mortality of in-patients with COVID-19 in South Florida

IntroductionThe SARS-CoV-2 (COVID-19) pandemic has created substantial health and economic burdens in the US and worldwide. As new variants continuously emerge, predicting critical clinical events in the context of relevant individual risks is a promising option for reducing the overall burden of CO...

Full description

Bibliographic Details
Main Authors:	Debarshi Datta, Safiya George Dalmida, Laurie Martinez, David Newman, Javad Hashemi, Taghi M. Khoshgoftaar, Connor Shorten, Candice Sareli, Paula Eckardt
Format:	Article
Language:	English
Published:	Frontiers Media S.A. 2023-07-01
Series:	Frontiers in Digital Health
Subjects:	COVID-19 pandemic random forest classifier gini index feature analysis and prediction SHAP (Shapley additive explanation) SMOTE (Synthetic minority over-sampling techniques)
Online Access:	https://www.frontiersin.org/articles/10.3389/fdgth.2023.1193467/full

Similar Items

Development and validation of an interpretable radiomic nomogram for severe radiation proctitis prediction in postoperative cervical cancer patients
by: Chaoyi Wei, et al.
Published: (2023-01-01)

Explainable Machine Learning Model for Predicting First-Time Acute Exacerbation in Patients with Chronic Obstructive Pulmonary Disease
by: Chew-Teng Kor, et al.
Published: (2022-02-01)

Establishment and Validation of Predictive Model of Tophus in Gout Patients
by: Tianyi Lei, et al.
Published: (2023-02-01)

Unboxing Industry-Standard AI Models for Male Fertility Prediction with SHAP
by: Debasmita GhoshRoy, et al.
Published: (2023-03-01)

Comparative analysis of tissue-specific genes in maize based on machine learning models: CNN performs technically best, LightGBM performs biologically soundest
by: Zijie Wang, et al.
Published: (2023-05-01)

Solubilization of inclusion bodies: insights from explainable machine learning approaches
by: Cornelia Walther, et al.
Published: (2023-08-01)

Comparison of Four Machine Learning Techniques for Prediction of Intensive Care Unit Length of Stay in Heart Transplantation Patients
by: Kan Wang, et al.
Published: (2022-06-01)

A clinical prediction model based on interpretable machine learning algorithms for prolonged hospital stay in acute ischemic stroke patients: a real-world study
by: Kai Wang, et al.
Published: (2023-11-01)

Explainable Anomaly Detection Framework for Maritime Main Engine Sensor Data
by: Donghyun Kim, et al.
Published: (2021-07-01)

Severity Prediction of Highway Crashes in Saudi Arabia Using Machine Learning Techniques
by: Ibrahim Aldhari, et al.
Published: (2022-12-01)

Identification of Soil Texture Classes Under Vegetation Cover Based on Sentinel-2 Data With SVM and SHAP Techniques
by: Yanan Zhou, et al.
Published: (2022-01-01)

Explainable Machine Learning-Based Risk Prediction Model for In-Hospital Mortality after Continuous Renal Replacement Therapy Initiation
by: Pei-Shan Hung, et al.
Published: (2022-06-01)

Enhancing prediction and analysis of UK road traffic accident severity using AI: Integration of machine learning, econometric techniques, and time series forecasting in public health research
by: Md Abu Sufian, et al.
Published: (2024-04-01)

Application of SHAP on CatBoost classification for identification of variabels characterizing food insecurity occurrences in Aceh Province households
by: MUHAMMAD SUBIANTO, et al.
Published: (2023-10-01)

Model of Storm Surge Maximum Water Level Increase in a Coastal Area Using Ensemble Machine Learning and Explicable Algorithm
by: Kun Sun, et al.
Published: (2023-12-01)

An Explainable Machine Learning Model to Predict Acute Kidney Injury After Cardiac Surgery: A Retrospective Cohort Study
by: Gao Y, et al.
Published: (2023-12-01)

Development of a Prediction Method of Cell Density in Autotrophic/Heterotrophic Microorganism Mixtures by Machine Learning Using Absorbance Spectrum Data
by: Akihito Nakanishi, et al.
Published: (2022-10-01)

Integration of shapley additive explanations with random forest model for quantitative precipitation estimation of mesoscale convective systems
by: Zheyu He, et al.
Published: (2023-01-01)

SHAP and LIME: An Evaluation of Discriminative Power in Credit Risk
by: Alex Gramegna, et al.
Published: (2021-09-01)

An ensemble classification approach for cervical cancer prediction using behavioral risk factors
by: Md Shahin Ali, et al.
Published: (2024-06-01)

Classification and Explanation for Intrusion Detection System Based on Ensemble Trees and SHAP Method
by: Thi-Thu-Huong Le, et al.
Published: (2022-02-01)

Intelligent Prediction of Maximum Ground Settlement Induced by EPB Shield Tunneling Using Automated Machine Learning Techniques
by: Syed Mujtaba Hussaine, et al.
Published: (2022-12-01)

XML-CIMT: Explainable Machine Learning (XML) Model for Predicting Chemical-Induced Mitochondrial Toxicity
by: Keerthana Jaganathan, et al.
Published: (2022-12-01)

Dynamic Vegetation Responses to Climate and Land Use Changes over the Inner Mongolia Reach of the Yellow River Basin, China
by: Tingxiang Liu, et al.
Published: (2023-07-01)

Cybersecurity Knowledge Extraction Using XAI
by: Ana Šarčević, et al.
Published: (2022-08-01)

Artificial intelligence annotated clinical-pathologic risk model to predict outcomes of advanced gastric cancer
by: Yan Chen, et al.
Published: (2023-03-01)

Evaluation of Local Interpretable Model-Agnostic Explanation and Shapley Additive Explanation for Chronic Heart Disease Detection
by: Tsehay Admassu Assegie
Published: (2023-01-01)

The use of machine learning “black boxes” explanation systems to improve the quality of school education
by: R. Muhamedyev, et al.
Published: (2020-01-01)

Machine Learning Algorithms Associate Case Numbers with SARS-CoV-2 Variants Rather Than with Impactful Mutations
by: Matthieu Vilain, et al.
Published: (2023-05-01)

Predicting risk of obesity in overweight adults using interpretable machine learning algorithms
by: Wei Lin, et al.
Published: (2023-11-01)

Application of Explainable Artificial Intelligence (XAI) in Urban Growth Modeling: A Case Study of Seoul Metropolitan Area, Korea
by: Minjun Kim, et al.
Published: (2023-02-01)

Characteristics of fatal occupational injuries in migrant workers in South Korea: A machine learning study
by: Ju-Yeun Lee, et al.
Published: (2023-09-01)

Analysis of changes in geographical factors affecting sales in commercial alleys after COVID-19 using machine learning techniques
by: Kangjae Lee
Published: (2022-09-01)

Investigation of feature contribution to shield tunneling-induced settlement using Shapley additive explanations method
by: K.K. Pabodha M. Kannangara, et al.
Published: (2022-08-01)

Uncovering the Impact of Local and Global Interests in Artists on Stock Prices of K-Pop Entertainment Companies: A SHAP-XGBoost Analysis
by: Daeun Yu, et al.
Published: (2023-05-01)

An Explainable Ensemble Deep Learning Approach for Intrusion Detection in Industrial Internet of Things
by: Mousa'B Mohammad Shtayat, et al.
Published: (2023-01-01)

Dissecting clinical and biological heterogeneity in clinical states of bipolar disorder: a 10-year retrospective study from China
by: Ting Zhu, et al.
Published: (2023-12-01)

An Integrative Explainable Artificial Intelligence Approach to Analyze Fine-Scale Land-Cover and Land-Use Factors Associated with Spatial Distributions of Place of Residence of Reported Dengue Cases
by: Hsiu Yang, et al.
Published: (2023-04-01)

Spatiotemporal Variation of Evapotranspiration on Different Land Use/Cover in the Inner Mongolia Reach of the Yellow River Basin
by: Xiaojing Zhang, et al.
Published: (2022-09-01)

Machine Learning Models Using SHapley Additive exPlanation for Fire Risk Assessment Mode and Effects Analysis of Stadiums
by: Ying Lu, et al.
Published: (2023-02-01)