Bhattacharya et al., 2018 - Google Patents
A dual boundary classifier for predicting acute hypotensive episodes in critical careBhattacharya et al., 2018
View HTML- Document ID
- 3624355751265890434
- Author
- Bhattacharya S
- Huddar V
- Rajan V
- Reddy C
- Publication year
- Publication venue
- PloS one
External Links
Snippet
An Acute Hypotensive Episode (AHE) is the sudden onset of a sustained period of low blood pressure and is one among the most critical conditions in Intensive Care Units (ICU). Without timely medical care, it can lead to an irreversible organ damage and death. By identifying …
- 208000001953 Hypotension 0 title abstract description 36
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
-
- 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
- G06F19/10—Bioinformatics, i.e. methods or systems for genetic or protein-related data processing in computational molecular biology
- G06F19/24—Bioinformatics, i.e. methods or systems for genetic or protein-related data processing in computational molecular biology for machine learning, data mining or biostatistics, e.g. pattern finding, knowledge discovery, rule extraction, correlation, clustering or classification
-
- 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
- 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
- G06N5/04—Inference methods or devices
-
- 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
- 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
- G06F17/30286—Information retrieval; Database structures therefor; File system structures therefor in structured data stores
- G06F17/30587—Details of specialised database 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
- G06Q10/00—Administration; Management
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Spencer et al. | Exploring feature selection and classification methods for predicting heart disease | |
Wosiak et al. | Integrating Correlation‐Based Feature Selection and Clustering for Improved Cardiovascular Disease Diagnosis | |
Muhlestein et al. | Predicting inpatient length of stay after brain tumor surgery: developing machine learning ensembles to improve predictive performance | |
Chen et al. | Probabilistic machine learning for healthcare | |
Miller | Machine intelligence in cardiovascular medicine | |
Bromuri et al. | Multi-label classification of chronically ill patients with bag of words and supervised dimensionality reduction algorithms | |
Catling et al. | Temporal convolutional networks allow early prediction of events in critical care | |
Bhattacharya et al. | A dual boundary classifier for predicting acute hypotensive episodes in critical care | |
Moss et al. | Cardiorespiratory dynamics measured from continuous ECG monitoring improves detection of deterioration in acute care patients: A retrospective cohort study | |
Brisimi et al. | Predicting diabetes-related hospitalizations based on electronic health records | |
Yang et al. | A new method for identifying the acute respiratory distress syndrome disease based on noninvasive physiological parameters | |
Salgado et al. | Ensemble fuzzy models in personalized medicine: Application to vasopressors administration | |
Xu et al. | Predictive modeling of the risk of acute kidney injury in critical care: a systematic investigation of the class imbalance problem | |
He et al. | A pyramid-like model for heartbeat classification from ECG recordings | |
Mlakar et al. | Mining telemonitored physiological data and patient-reported outcomes of congestive heart failure patients | |
Michel et al. | A filter approach for feature selection in classification: application to automatic atrial fibrillation detection in electrocardiogram recordings | |
Obaido et al. | An improved ensemble method for predicting hyperchloremia in adults with diabetic ketoacidosis | |
Li et al. | A deep learning system for heart failure mortality prediction | |
Sun et al. | Artificial intelligence and machine learning: Definition of terms and current concepts in critical care research | |
Mantovani et al. | Mining compact predictive pattern sets using classification model | |
Yoshimura et al. | Preoperative echocardiography predictive analytics for postinduction hypotension prediction | |
Chinni et al. | Emerging analytical approaches for personalized medicine using machine learning in pediatric and congenital heart disease | |
Ting et al. | Prehospital factors predict outcomes in pediatric trauma: a principal component analysis | |
Díaz | Artificial intelligence in cardiovascular medicine: Applications in the diagnosis of infarction and prognosis of heart failure | |
Mall et al. | Optimizing Heart Attack Prediction Through OHE2LM: A Hybrid Modelling Strategy. |