Khasha et al., 2021 - Google Patents
Detecting asthma control level using feature-based time series classificationKhasha et al., 2021
- Document ID
- 3670098245821398268
- Author
- Khasha R
- Sepehri M
- Taherkhani N
- Publication year
- Publication venue
- Applied Soft Computing
External Links
Snippet
Asthma symptom control is the best method for asthma treatment. Early detection of asthma control status can provide the time required for presenting preventive treatment programs to reduce the future risk of asthma exacerbation and episodes of poor asthma control …
- 208000006673 Asthma 0 title abstract description 184
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
- G06F19/34—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
- 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
- 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
- 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
- G06F19/30—Medical informatics, i.e. computer-based analysis or dissemination of patient or disease data
- G06F19/36—Computer-assisted acquisition of medical data, e.g. computerised clinical trials or questionnaires
-
- 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
- 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
- G06F17/30289—Database design, administration or maintenance
- G06F17/30303—Improving data quality; Data cleansing
-
- 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
- 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
- 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
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computer systems based on biological models
- G06N3/12—Computer systems based on biological models using genetic models
- G06N3/126—Genetic algorithms, i.e. information processing using digital simulations of the genetic system
-
- 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
- G06K9/6268—Classification techniques relating to the classification paradigm, e.g. parametric or non-parametric approaches
-
- 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
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N7/00—Computer systems based on specific mathematical models
- G06N7/005—Probabilistic networks
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Jiang et al. | Supervised machine learning: a brief primer | |
| Huang et al. | Comparison of three machine learning models to predict suicidal ideation and depression among Chinese adolescents: a cross-sectional study | |
| Khasha et al. | Detecting asthma control level using feature-based time series classification | |
| Ostojic et al. | The challenges of using machine learning models in psychiatric research and clinical practice | |
| Lottering et al. | A model for the identification of students at risk of dropout at a university of technology | |
| Shahriar et al. | A machine learning approach to predict vulnerability to drug addiction | |
| Rahman et al. | Using and comparing different decision tree classification techniques for mining ICDDR, B Hospital Surveillance data | |
| Sharanyaa et al. | Hybrid machine learning techniques for heart disease prediction | |
| Bose et al. | Screening of autism spectrum disorder using machine learning approach in accordance with DSM-5 | |
| Corrales et al. | Colorectal cancer risk mapping through Bayesian networks | |
| Zhang et al. | Benchmarking feature selection methods with different prediction models on large-scale healthcare event data | |
| Nath et al. | Smart web interface for student mental health prediction using machine learning with blockchain technology | |
| Thawnashom et al. | Machine learning's performance in classifying postmenopausal osteoporosis Thai patients | |
| CHALO et al. | A New Preprocessing Method for Diabetes and Biomedical Data Classification | |
| Airlangga | A Comparative Analysis of Machine Learning Models for Obesity Prediction | |
| Preetha | Data Analysis on Student's Performance based on Health status using Genetic Algorithm and Clustering algorithms | |
| Kryvenchuk et al. | Random Forest as a Method of Predicting the Presence of Cardiovasculars Diseases. | |
| Jothi et al. | Predicting generalised anxiety disorder among women using decision tree-based classification | |
| Salman et al. | Data Mining Technique for Diagnosing Autism Spectrum Disorder | |
| Setty | Heart Stroke Risk Analysis Using Machine Learning | |
| Cuizon | Ensemble Predictive Model for Academic Churn Risk Using Plurality Voting | |
| Turganbayeva et al. | Machine learning in psychometry and psyhological research | |
| Steynberg | A framework for identifying the most likely successful underprivileged tertiary bursary applicants | |
| Mehta et al. | Predicting Severity from Electronic Health Records of Leprosy Patients using Ensemble Learning | |
| Shabadi et al. | Autistic Spectrum Disorder Screening: Prediction with Machine Learning Models |