Rahim et al., 2010 - Google Patents
System identification of nonlinear autoregressive models in monitoring dengue infectionRahim 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 …
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
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N33/00—Investigating or analysing materials by specific methods not covered by the preceding groups
- G01N33/48—Investigating or analysing materials by specific methods not covered by the preceding groups biological material, e.g. blood, urine; Haemocytometers
- G01N33/50—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
- G01N33/68—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing involving proteins, peptides or amino acids
- G01N33/6893—Chemical 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
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N33/00—Investigating or analysing materials by specific methods not covered by the preceding groups
- G01N33/48—Investigating or analysing materials by specific methods not covered by the preceding groups biological material, e.g. blood, urine; Haemocytometers
- G01N33/50—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
- G01N33/5005—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing involving human or animal cells
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N33/00—Investigating or analysing materials by specific methods not covered by the preceding groups
- G01N33/48—Investigating or analysing materials by specific methods not covered by the preceding groups biological material, e.g. blood, urine; Haemocytometers
- G01N33/50—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
- G01N33/53—Immunoassay; Biospecific binding assay
- G01N33/569—Immunoassay; Biospecific binding assay for micro-organisms, e.g. protozoa, bacteria, viruses
- G01N33/56911—Bacteria
-
- 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
- A61B5/7235—Details of waveform analysis
- A61B5/7264—Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N2800/00—Detection or diagnosis of diseases
-
- 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
- A61B5/41—Detecting, measuring or recording for evaluating the immune or lymphatic systems
-
- 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
- A61B5/08—Detecting, measuring or recording devices for evaluating the respiratory organs
-
- 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
- A61B5/44—Detecting, measuring or recording for evaluating the integumentary system, e.g. skin, hair or nails
- A61B5/441—Skin 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 |