Rahim et al., 2010 - Google Patents

System identification of nonlinear autoregressive models in monitoring dengue infection

Rahim et al., 2010

View PDF
Document ID
13415163194411461669
Author
Rahim H
Ibrahim F
Taib M
Publication year
Publication venue
International Journal on Smart Sensing and Intelligent Systems

External Links

Snippet

This paper proposes system identification on application of nonlinear AR (NAR) based on Artificial Neural Network (ANN) for monitor of dengue infections. In building the model, three selection criteria, ie the final prediction error (FPE), Akaike's Information Criteria (AIC), and …
Continue reading at sciendo.com (PDF) (other versions)

Classifications

    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06FELECTRICAL DIGITAL DATA PROCESSING
    • G06F19/00Digital computing or data processing equipment or methods, specially adapted for specific applications
    • G06F19/30Medical informatics, i.e. computer-based analysis or dissemination of patient or disease data
    • G06F19/34Computer-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
    • G06F19/345Medical expert systems, neural networks or other automated diagnosis
    • GPHYSICS
    • G01MEASURING; TESTING
    • G01NINVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00Investigating or analysing materials by specific methods not covered by the preceding groups
    • G01N33/48Investigating or analysing materials by specific methods not covered by the preceding groups biological material, e.g. blood, urine; Haemocytometers
    • G01N33/50Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
    • G01N33/68Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing involving proteins, peptides or amino acids
    • G01N33/6893Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing involving proteins, peptides or amino acids related to diseases not provided for elsewhere
    • GPHYSICS
    • G01MEASURING; TESTING
    • G01NINVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00Investigating or analysing materials by specific methods not covered by the preceding groups
    • G01N33/48Investigating or analysing materials by specific methods not covered by the preceding groups biological material, e.g. blood, urine; Haemocytometers
    • G01N33/50Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
    • G01N33/5005Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing involving human or animal cells
    • GPHYSICS
    • G01MEASURING; TESTING
    • G01NINVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00Investigating or analysing materials by specific methods not covered by the preceding groups
    • G01N33/48Investigating or analysing materials by specific methods not covered by the preceding groups biological material, e.g. blood, urine; Haemocytometers
    • G01N33/50Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
    • G01N33/53Immunoassay; Biospecific binding assay
    • G01N33/569Immunoassay; Biospecific binding assay for micro-organisms, e.g. protozoa, bacteria, viruses
    • G01N33/56911Bacteria
    • AHUMAN NECESSITIES
    • A61MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61BDIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00Detecting, measuring or recording for diagnostic purposes; Identification of persons
    • A61B5/72Signal processing specially adapted for physiological signals or for diagnostic purposes
    • A61B5/7235Details of waveform analysis
    • A61B5/7264Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems
    • GPHYSICS
    • G01MEASURING; TESTING
    • G01NINVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N2800/00Detection or diagnosis of diseases
    • AHUMAN NECESSITIES
    • A61MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61BDIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00Detecting, measuring or recording for diagnostic purposes; Identification of persons
    • A61B5/41Detecting, measuring or recording for evaluating the immune or lymphatic systems
    • AHUMAN NECESSITIES
    • A61MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61BDIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00Detecting, measuring or recording for diagnostic purposes; Identification of persons
    • A61B5/08Detecting, measuring or recording devices for evaluating the respiratory organs
    • AHUMAN NECESSITIES
    • A61MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61BDIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00Detecting, measuring or recording for diagnostic purposes; Identification of persons
    • A61B5/44Detecting, measuring or recording for evaluating the integumentary system, e.g. skin, hair or nails
    • A61B5/441Skin evaluation, e.g. for skin disorder diagnosis

Similar Documents

Publication Publication Date Title
Lussier et al. Early detection of mild cognitive impairment with in-home monitoring sensor technologies using functional measures: a systematic review
Schriger et al. Structured clinical decision aids are seldom compared with subjective physician judgment, and are seldom superior
Wettergren et al. The use, feasibility and psychometric properties of an individualised quality-of-life instrument: a systematic review of the SEIQoL-DW
EP3064179B1 (en) Apparatus and method for analysing events from sensor data by optimisation
Berge et al. Marital satisfaction and mental health of couples with children with chronic health conditions.
Musaad et al. Comparison of anthropometric measures of obesity in childhood allergic asthma: central obesity is most relevant
Cornuz et al. Clinical prediction of deep venous thrombosis using two risk assessment methods in combination with rapid quantitative D-dimer testing
Fontaine et al. Artificial intelligence to evaluate postoperative pain based on facial expression recognition
CN111081379B (en) Disease probability decision method and system thereof
Tian et al. Using Z-number to measure the reliability of new information fusion method and its application in pattern recognition
KR20200001777A (en) Method for predicting of depression and device for predicting of depression risk using the same
Dini et al. Detection of Oxygen Levels (SpO2) and Heart Rate Using a Pulse Oximeter for Classification of Hypoxemia Based on Fuzzy Logic
Harish et al. Smart home based prediction of symptoms of Alzheimer’s disease using machine learning and contextual approach
Yahyaoui et al. Deep and machine learning towards pneumonia and asthma detection
Liu et al. A novel interpretable feature set optimization method in blood pressure estimation using photoplethysmography signals
CN114464319B (en) AMS susceptibility assessment system based on slow feature analysis and deep neural network
Festin et al. Non-Invasive Detection of Diabetes Mellitus by Tongue Diagnosis Using Convolutional Neural Network
Rahim et al. System identification of nonlinear autoregressive models in monitoring dengue infection
Irungu et al. A CNN Transfer Learning-Electrocardiogram (ECG) Signal Approach to Predict COVID-19
Naseri et al. Data-efficient machine learning methods in the ME-TIME study: Rationale and design of a longitudinal study to detect atrial fibrillation and heart failure from wearables
CN117116475A (en) Method, system, terminal and storage medium for predicting risk of ischemic cerebral apoplexy
Rahim et al. Application of Bioelectrical Impedance Sensing Techniques for Dengue Infection with Non-linear Autoregressive model
Elakkiya et al. Progressive assessment system for dementia care through smart home
Rahim et al. A novel prediction system in dengue fever using NARMAX model
Sanaei et al. Designing and implementing fuzzy expert system for diagnosis of psoriasis