Elhadary et al., 2023 - Google Patents
Applications of machine learning in chronic myeloid leukemiaElhadary et al., 2023
View HTML- Document ID
- 11787335882197752152
- Author
- Elhadary M
- Elsabagh A
- Ferih K
- Elsayed B
- Elshoeibi A
- Kaddoura R
- Akiki S
- Ahmed K
- Yassin M
- Publication year
- Publication venue
- Diagnostics
External Links
Snippet
Chronic myeloid leukemia (CML) is a myeloproliferative neoplasm characterized by dysregulated growth and the proliferation of myeloid cells in the bone marrow caused by the BCR-ABL1 fusion gene. Clinically, CML demonstrates an increased production of mature …
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
- G06F19/32—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
- G06F19/345—Medical expert systems, neural networks or other automated diagnosis
-
- 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
- G06F19/28—Bioinformatics, i.e. methods or systems for genetic or protein-related data processing in computational molecular biology for programming tools or database systems, e.g. ontologies, heterogeneous data integration, data warehousing or computing architectures
-
- 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
- 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
-
- 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
- G06Q50/01—Social networking
-
- 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
- G06Q10/10—Office automation, e.g. computer aided management of electronic mail or groupware; Time management, e.g. calendars, reminders, meetings or time accounting
-
- 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
-
- 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
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Ahsan et al. | Covid-19 symptoms detection based on nasnetmobile with explainable ai using various imaging modalities | |
Krishnamurthy et al. | Machine learning prediction models for chronic kidney disease using national health insurance claim data in Taiwan | |
Chaves et al. | Data mining techniques for early diagnosis of diabetes: a comparative study | |
Kitamura et al. | Deep learning could diagnose diabetic nephropathy with renal pathological immunofluorescent images | |
Huyut et al. | Diagnosis and Prognosis of COVID-19 disease using routine blood values and LogNNet neural network | |
Khan et al. | Using a deep learning model to explore the impact of clinical data on COVID-19 diagnosis using chest X-ray | |
Góngora Alonso et al. | Comparison of machine learning algorithms in the prediction of hospitalized patients with schizophrenia | |
Elhadary et al. | Applications of machine learning in chronic myeloid leukemia | |
Goździkiewicz et al. | The use of artificial intelligence algorithms in the diagnosis of urinary tract infections—a literature review | |
Attai et al. | A systematic review of applications of machine learning and other soft computing techniques for the diagnosis of tropical diseases | |
Laddha et al. | COVID-19 diagnosis and classification using radiological imaging and deep learning techniques: a comparative study | |
Alanazi et al. | Machine Learning for Early Prediction of Sepsis in Intensive Care Unit (ICU) Patients | |
Kakoly et al. | Data-driven diabetes risk factor prediction using machine learning algorithms with feature selection technique | |
Oei et al. | Progression-free survival prediction in patients with nasopharyngeal carcinoma after intensity-modulated radiotherapy: machine learning vs. traditional statistics | |
Mendoza-Larios et al. | Association between suicide and Toxoplasma gondii seropositivity | |
Exarchos et al. | Recent advances of artificial intelligence applications in interstitial lung diseases | |
Ayadi et al. | COVID-AleXception: A deep learning model based on a deep feature concatenation approach for the detection of COVID-19 from chest X-ray images | |
Giełczyk et al. | A novel lightweight approach to COVID-19 diagnostics based on chest X-ray images | |
Mohammedqasim et al. | Diagnosing coronary artery disease on the basis of hard ensemble voting optimization | |
Pullakhandam et al. | Classification and Explanation of Iron Deficiency Anemia from Complete Blood Count Data Using Machine Learning | |
Tore et al. | Diagnosis of endometriosis based on comorbidities: a machine learning Approach | |
Suo et al. | Discrepancy analysis between histology and molecular diagnoses in kidney allograft biopsies: a single-center experience | |
Vigia et al. | Pancreas Rejection in the Artificial Intelligence Era: New Tool for Signal Patients at Risk | |
Qiao et al. | An end-to-end pipeline for early diagnosis of acute promyelocytic leukemia based on a compact CNN model | |
Scanlon et al. | Developing an agnostic risk prediction model for early AKI detection in cancer patients |