[go: up one dir, main page]
More Web Proxy on the site http://driver.im/

Sabahi et al., 2023 - Google Patents

In-hospital mortality prediction model of heart failure patients using imbalanced registry data: A machine learning approach

Sabahi et al., 2023

View PDF
Document ID
7235655177878784470
Author
Sabahi H
Vali M
Shafie D
Publication year
Publication venue
Scientia Iranica

External Links

Snippet

Heart failure (HF) is a cardiac dysfunction disease with a high mortality rate that is mostly calculated via registry data. The objective of this work was to predict in-hospital mortality in patients hospitalized with HF utilizing their before-hospitalization registry data. The data …
Continue reading at scientiairanica.sharif.edu (PDF) (other versions)

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F19/00—Digital computing or data processing equipment or methods, specially adapted for specific applications
    • G06F19/30—Medical informatics, i.e. computer-based analysis or dissemination of patient or disease data
    • G06F19/34—Computer-assisted medical diagnosis or treatment, e.g. computerised prescription or delivery of medication or diets, computerised local control of medical devices, medical expert systems or telemedicine
    • G06F19/345—Medical expert systems, neural networks or other automated diagnosis
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F19/00—Digital computing or data processing equipment or methods, specially adapted for specific applications
    • G06F19/30—Medical informatics, i.e. computer-based analysis or dissemination of patient or disease data
    • G06F19/32—Medical data management, e.g. systems or protocols for archival or communication of medical images, computerised patient records or computerised general medical references
    • G06F19/322—Management of patient personal data, e.g. patient records, conversion of records or privacy aspects
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06Q—DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Systems or methods specially adapted for a specific business sector, e.g. utilities or tourism
    • G06Q50/10—Services
    • G06Q50/22—Health care, e.g. hospitals; Social work
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N99/00—Subject matter not provided for in other groups of this subclass
    • G06N99/005—Learning machines, i.e. computer in which a programme is changed according to experience gained by the machine itself during a complete run
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N5/00—Computer systems utilising knowledge based models
    • G06N5/02—Knowledge representation
    • G06N5/022—Knowledge engineering, knowledge acquisition
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N5/00—Computer systems utilising knowledge based models
    • G06N5/04—Inference methods or devices
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/62—Methods or arrangements for recognition using electronic means
    • G06K9/6267—Classification techniques
    • G06K9/6279—Classification techniques relating to the number of classes
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/30—Information retrieval; Database structures therefor; File system structures therefor
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/62—Methods or arrangements for recognition using electronic means
    • G06K9/6217—Design or setup of recognition systems and techniques; Extraction of features in feature space; Clustering techniques; Blind source separation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F15/00—Digital computers in general; Data processing equipment in general
    • G06F15/18—Digital computers in general; Data processing equipment in general in which a programme is changed according to experience gained by the computer itself during a complete run; Learning machines
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06Q—DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computer systems based on biological models
    • G06N3/02—Computer systems based on biological models using neural network models
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06Q—DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q40/00—Finance; Insurance; Tax strategies; Processing of corporate or income taxes

Similar Documents

Publication Publication Date Title
Muntasir Nishat et al. A Comprehensive Investigation of the Performances of Different Machine Learning Classifiers With SMOTE‐ENN Oversampling Technique and Hyperparameter Optimization for Imbalanced Heart Failure Dataset
Tripoliti et al. Heart failure: diagnosis, severity estimation and prediction of adverse events through machine learning techniques
Dissanayake et al. Comparative study on heart disease prediction using feature selection techniques on classification algorithms
Senan et al. Score and Correlation Coefficient‐Based Feature Selection for Predicting Heart Failure Diagnosis by Using Machine Learning Algorithms
Ghasemieh et al. A novel machine learning model with Stacking Ensemble Learner for predicting emergency readmission of heart-disease patients
Peter et al. Study and development of novel feature selection framework for heart disease prediction
Li et al. Predicting clinical outcomes with patient stratification via deep mixture neural networks
Thakkar et al. A comparative analysis of machine learning classifiers for robust heart disease prediction
Saeed et al. Cardiac disease prediction using AI algorithms with SelectKBest
Tsarapatsani et al. Machine learning models for cardiovascular disease events prediction
Bhushan et al. Machine learning and deep learning techniques for the analysis of heart disease: a systematic literature review, open challenges and future directions
Sabahi et al. In-hospital mortality prediction model of heart failure patients using imbalanced registry data: A machine learning approach
Joshi et al. An accurate prediction of coronary heart disease using ensemble algorithms
Pereira et al. Fuzzy modeling to predict severely depressed left ventricular ejection fraction following admission to the intensive care unit using clinical physiology
Akter et al. CKD. Net: A novel deep learning hybrid model for effective, real-time, automated screening tool towards prediction of multi stages of CKD along with eGFR and creatinine
Rahman et al. Automatic classification of patients with myocardial infarction or myocarditis based only on clinical data: A quick response
Hasan et al. Cardiovascular disease prediction through comparative analysis of machine learning models
Alemerien et al. Diagnosing Cardiovascular Diseases using Optimized Machine Learning Algorithms with GridSearchCV
Mahmood et al. Enhancing Coronary Artery Disease Prognosis: A Novel Dual-Class Boosted Decision Trees Strategy for Robust Optimization
Baswaraj et al. An Efficient Proposal for Deep Learning-Based Diabetes Prediction
Deivendran et al. Coronary illness prediction using the AdaBoost algorithm
Joshi et al. Coronary artery disease prediction techniques: A survey
Werner et al. The study of preprocessing methods’ utility in analysis of multidimensional and highly imbalanced medical data
Yahaya et al. A SURVEY OF PERFORMANCES OF SOME SELECTED MACHINE LEARNING ALGORITHMS FOR CARDIOVASCULAR DISEASE PREDICTION
Rahman et al. Cardiovascular disease prediction utilizing machine learning and feature selection with clonal selection algorithm