[go: up one dir, main page]

Fialho et al., 2013 - Google Patents

Disease-based modeling to predict fluid response in intensive care units

Fialho et al., 2013

View PDF
Document ID
16306868278777001639
Author
Fialho A
Celi L
Cismondi F
Vieira S
Reti S
Sousa J
Finkelstein S
Publication year
Publication venue
Methods of information in medicine

External Links

Snippet

Objective: To compare general and disease-based modeling for fluid resuscitation and vasopressor use in intensive care units. Methods: Retrospective cohort study in-volving 2944 adult medical and surgical intensive care unit (ICU) patients receiving fluid …
Continue reading at pmc.ncbi.nlm.nih.gov (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
    • 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
    • G06F19/3431—Calculating a health index for the patient, e.g. for risk assessment
    • 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
    • G06F19/3443—Medical data mining, e.g. in previous cases of different patients
    • 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
    • G06F19/3437—Medical simulation or modelling, e.g. simulating the evolution of medical disorders
    • 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
    • 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
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Detecting, measuring or recording for diagnostic purposes; Identification of persons
    • A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
    • A61B5/7271—Specific aspects of physiological measurement analysis
    • A61B5/7275—Determining trends in physiological measurement data; Predicting development of a medical condition based on physiological measurements, e.g. determining a risk factor
    • 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

Similar Documents

Publication Publication Date Title
Fialho et al. Disease-based modeling to predict fluid response in intensive care units
Fialho et al. Data mining using clinical physiology at discharge to predict ICU readmissions
David et al. HEART DISEASE PREDICTION USING DATA MINING TECHNIQUES.
Miao et al. Predictive modeling of hospital mortality for patients with heart failure by using an improved random survival forest
Silva et al. Predicting in-hospital mortality of icu patients: The physionet/computing in cardiology challenge 2012
Son et al. Decision-making model for early diagnosis of congestive heart failure using rough set and decision tree approaches
Ghassemi et al. Predicting intervention onset in the ICU with switching state space models
Guidi et al. A machine learning system to improve heart failure patient assistance
Gholipour et al. Using an artificial neural networks (ANNs) model for prediction of intensive care unit (ICU) outcome and length of stay at hospital in traumatic patients
Moss et al. Cardiorespiratory dynamics measured from continuous ECG monitoring improves detection of deterioration in acute care patients: A retrospective cohort study
Agrawal et al. Heart failure prediction using machine learning with exploratory data analysis
Souza et al. Identifying risk factors for heart failure: A case study employing data mining algorithms
De Pasquale et al. Hemorrhage prediction models in surgical intensive care: bedside monitoring data adds information to lab values
Mena et al. Machine learning approach to extract diagnostic and prognostic thresholds: application in prognosis of cardiovascular mortality
Pereira et al. Fuzzy modeling to predict severely depressed left ventricular ejection fraction following admission to the intensive care unit using clinical physiology
Asgari et al. The comparison of selected machine learning techniques and correlation matrix in ICU mortality risk prediction
Eskandari et al. Detection of sepsis using biomarkers based on machine learning
Amrollahi et al. Predicting hospital readmission among patients with sepsis using clinical and wearable data
Fialho et al. Fuzzy modeling to predict administration of vasopressors in intensive care unit patients
Cismondi et al. Predicting laboratory testing in intensive care using fuzzy and neural modeling
Golovco et al. Acute kidney injury prediction with gradient boosting decision trees enriched with temporal features
Morales et al. Applying conditional independence maps to improve sepsis prognosis
Fialho et al. Predicting intensive care unit readmissions using probabilistic fuzzy systems
Mucksavage et al. A pilot study of end-tidal carbon dioxide in prediction of inhospital cardiac arrests
Panyamit et al. Identification of factors in the survival rate of heart failure patients using machine learning models and principal component analysis