Roque et al., 2024 - Google Patents
Forecasting Risk Pregnancies in Peru using Machine LearningRoque et al., 2024
- Document ID
- 6945938898579025374
- Author
- Roque A
- Huamanzana J
- Mauricio D
- Publication year
- Publication venue
- 2024 10th International Conference on Optimization and Applications (ICOA)
External Links
Snippet
In Peru, between 2017 and 2021, there has been an upward trend in maternal deaths attributed to preeclampsia, reaching an average of 393 cases. Recognizing the importance of early detection of risks associated with preeclampsia, a predictive model was developed …
- 230000035935 pregnancy 0 title abstract description 28
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
- G06F19/3437—Medical simulation or modelling, e.g. simulating the evolution of medical disorders
-
- 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
- G06F19/3487—Medical report generation
-
- 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
- G06F19/3443—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
- G06F19/10—Bioinformatics, i.e. methods or systems for genetic or protein-related data processing in computational molecular biology
- G06F19/18—Bioinformatics, i.e. methods or systems for genetic or protein-related data processing in computational molecular biology for functional genomics or proteomics, e.g. genotype-phenotype associations, linkage disequilibrium, population genetics, binding site identification, mutagenesis, genotyping or genome annotation, protein-protein interactions or protein-nucleic acid interactions
-
- 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
- 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
-
- 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
- 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
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| LaFreniere et al. | Using machine learning to predict hypertension from a clinical dataset | |
| Aşuroğlu et al. | A deep learning approach for sepsis monitoring via severity score estimation | |
| Ding et al. | Mortality prediction for ICU patients combining just-in-time learning and extreme learning machine | |
| Khan et al. | A Comparative Study of Machine Learning classifiers to analyze the Precision of Myocardial Infarction prediction | |
| Shamsollahi et al. | Using combined descriptive and predictive methods of data mining for coronary artery disease prediction: a case study approach | |
| Noviyanti et al. | Early detection of diabetes using Random Forest algorithm | |
| Kavitha et al. | Cardiovascular disease prediction using LSTM algorithm based on cytokines | |
| CN114974585A (en) | A method for constructing an early risk prediction and assessment model for metabolic syndrome in pregnancy | |
| Sujatha et al. | Heart Failure Patient Survival Analysis with Multi Kernel Support Vector Machine. | |
| Cao et al. | Intelligent antepartum fetal monitoring via deep learning and fusion of cardiotocographic signals and clinical data | |
| Bahuguna et al. | Statistical Analysis and Prediction of Heart Disease Using Machine Learning | |
| Dhillon et al. | Machine learning based approach using XGboost for heart stroke prediction | |
| Eskandari et al. | Detection of sepsis using biomarkers based on machine learning | |
| Alqahtani et al. | Machine Learning for Predicting Intradialytic Hypotension: A Survey Review. | |
| Saleena | Analysis of machine learning and deep learning prediction models for sepsis and neonatal sepsis: A systematic review | |
| Singh et al. | A Comparative Study of Machine Learning and Deep Learning Methods for Detecting Thyroid Disease: An Experimental Investigation | |
| Tiwari et al. | Diagnosis of Brain’s Health Condition through Smart ML Algorithm through Brain Waves | |
| CN118098597A (en) | Risk prediction method for gestational diabetes mellitus and specific birth defects | |
| Roque et al. | Forecasting Risk Pregnancies in Peru using Machine Learning | |
| Akoosh et al. | Anticipating the Nearness of Coronary Heart Infection Utilizing Machine Learning Classifiers | |
| Aljameel et al. | An automated system for early prediction of miscarriage in the first trimester using machine learning | |
| CN117116475A (en) | Method, system, terminal and storage medium for predicting risk of ischemic cerebral apoplexy | |
| Hang et al. | Electronic medical record based machine learning methods for adverse pregnancy outcome prediction | |
| Angayarkanni et al. | Selection OF features associated with coronary artery diseases (cad) using feature selection techniques | |
| AlSaad et al. | Artificial intelligence models for predicting the mode of delivery in maternal care |