AI Medical Compendium Topic:
Logistic Models

Clear Filters Showing 691 to 700 of 1120 articles

A data-driven approach to predicting diabetes and cardiovascular disease with machine learning.

BMC medical informatics and decision making Nov 6, 2019
BACKGROUND: Diabetes and cardiovascular disease are two of the main causes of death in the United States. Identifying and predicting these diseases in patients is the first step towards stopping their progression. We evaluate the capabilities of mach...
Humans Machine Learning Electronic Health Records Logistic Models Nutrition Surveys Diabetes Mellitus Support Vector Machine Predictive Value of Tests Female Male Cardiovascular Diseases ROC Curve
View on PubMed DOI

A machine-learning approach to predict postprandial hypoglycemia.

BMC medical informatics and decision making Nov 6, 2019
BACKGROUND: For an effective artificial pancreas (AP) system and an improved therapeutic intervention with continuous glucose monitoring (CGM), predicting the occurrence of hypoglycemia accurately is very important. While there have been many studies...
Support Vector Machine Hypoglycemia Blood Glucose Self-Monitoring Humans Machine Learning Algorithms Retrospective Studies Adult Sensitivity and Specificity Blood Glucose Logistic Models Hypoglycemic Agents
View on PubMed DOI

Predicting skilled delivery service use in Ethiopia: dual application of logistic regression and machine learning algorithms.

BMC medical informatics and decision making Nov 5, 2019
BACKGROUND: Skilled assistance during childbirth is essential to reduce maternal deaths. However, in Ethiopia, which is among the six countries contributing to more than half of the global maternal deaths, the coverage of births attended by skilled h...
Machine Learning Clinical Decision-Making Pregnancy Female Young Adult Humans Health Surveys Adult Cross-Sectional Studies Logistic Models Middle Aged Ethiopia Maternal Health Services Bayes Theorem Adolescent Delivery, Obstetric
View on PubMed DOI

Rapid identification of pathogenic bacteria using Raman spectroscopy and deep learning.

Nature communications Oct 30, 2019
Raman optical spectroscopy promises label-free bacterial detection, identification, and antibiotic susceptibility testing in a single step. However, achieving clinically relevant speeds and accuracies remains challenging due to weak Raman signal from...
Proteus mirabilis Support Vector Machine Klebsiella Candida Enterococcus Microbial Sensitivity Tests Humans Escherichia coli Bacterial Infections Neural Networks, Computer Streptococcus Staphylococcus aureus Anti-Bacterial Agents Principal Component Analysis Bacterial Typing Techniques Methicillin-Resistant Staphylococcus aureus Deep Learning Single-Cell Analysis Pseudomonas aeruginosa Bacteria Salmonella enterica Logistic Models Spectrum Analysis, Raman
View on PubMed DOI

A question-entailment approach to question answering.

BMC bioinformatics Oct 22, 2019
BACKGROUND: One of the challenges in large-scale information retrieval (IR) is developing fine-grained and domain-specific methods to answer natural language questions. Despite the availability of numerous sources and datasets for answer retrieval, Q...
Deep Learning Logistic Models Information Storage and Retrieval Medical Informatics Unified Medical Language System
View on PubMed DOI

Depression Prediction by Using Ecological Momentary Assessment, Actiwatch Data, and Machine Learning: Observational Study on Older Adults Living Alone.

JMIR mHealth and uHealth Oct 16, 2019
BACKGROUND: Although geriatric depression is prevalent, diagnosis using self-reporting instruments has limitations when measuring the depressed mood of older adults in a community setting. Ecological momentary assessment (EMA) by using wearable devic...
Ecological Momentary Assessment Surveys and Questionnaires Female Wearable Electronic Devices Humans Aged Logistic Models Machine Learning Aged, 80 and over Residence Characteristics Republic of Korea Male Depression
View on PubMed DOI

Atrial Fibrillation Burden Signature and Near-Term Prediction of Stroke: A Machine Learning Analysis.

Circulation. Cardiovascular quality and outcomes Oct 15, 2019
BACKGROUND: Atrial fibrillation (AF) increases the risk of stroke 5-fold and there is rising interest to determine if AF severity or burden can further risk stratify these patients, particularly for near-term events. Using continuous remote monitorin...
Retrospective Studies Electronic Health Records Female Risk Factors Aged Aged, 80 and over Male Middle Aged Risk Assessment Humans Neural Networks, Computer Machine Learning Prognosis Administrative Claims, Healthcare Predictive Value of Tests Telemetry Signal Processing, Computer-Assisted Diagnosis, Computer-Assisted Veterans Health Services Stroke Time Factors Atrial Fibrillation Logistic Models Proof of Concept Study United States
View on PubMed DOI

Choosing Clinical Variables for Risk Stratification Post-Acute Coronary Syndrome.

Scientific reports Oct 10, 2019
Most risk stratification methods use expert opinion to identify a fixed number of clinical variables that have prognostic significance. In this study our goal was to develop improved metrics that utilize a variable number of input parameters. We firs...
Follow-Up Studies Registries Percutaneous Coronary Intervention Middle Aged Patient Selection Risk Assessment Acute Coronary Syndrome Cohort Studies Humans Logistic Models Machine Learning Prognosis Female Risk Factors Aged Male
View on PubMed DOI

ICU staffing feature phenotypes and their relationship with patients' outcomes: an unsupervised machine learning analysis.

Intensive care medicine Oct 8, 2019
PURPOSE: To study whether ICU staffing features are associated with improved hospital mortality, ICU length of stay (LOS) and duration of mechanical ventilation (MV) using cluster analysis directed by machine learning.
Organ Dysfunction Scores Personnel Staffing and Scheduling Physical Therapists Hospital Bed Capacity Logistic Models Cluster Analysis Unsupervised Machine Learning Odds Ratio Humans Physicians Retrospective Studies Intensive Care Units Brazil Length of Stay Nurses Time Factors Hospital Mortality
View on PubMed DOI

Detection of malaria parasites in dried human blood spots using mid-infrared spectroscopy and logistic regression analysis.

Malaria journal Oct 7, 2019
BACKGROUND: Epidemiological surveys of malaria currently rely on microscopy, polymerase chain reaction assays (PCR) or rapid diagnostic test kits for Plasmodium infections (RDTs). This study investigated whether mid-infrared (MIR) spectroscopy couple...
Humans Spectrophotometry, Infrared Dried Blood Spot Testing Logistic Models Plasmodium falciparum Supervised Machine Learning Malaria, Falciparum Tanzania
View on PubMed DOI
Popular Topics
Recent Journals