AIMC Topic: Cross Infection
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Deep Learning versus Conventional Machine Learning for Detection of Healthcare-Associated Infections in French Clinical Narratives.
Methods of information in medicine
Mar 15, 2019
OBJECTIVE: The objective of this article was to compare the performances of health care-associated infection (HAI) detection between deep learning and conventional machine learning (ML) methods in French medical reports.
Statistical outbreak detection by joining medical records and pathogen similarity.
Journal of biomedical informatics
Feb 13, 2019
We present a statistical inference model for the detection and characterization of outbreaks of hospital associated infection. The approach combines patient exposures, determined from electronic medical records, and pathogen similarity, determined by...
Prevalence and predictors of C. difficile infections in hospitalized patients with major surgical procedures in the USA: Analysis using traditional and machine learning methods.
American journal of surgery
Dec 1, 2018
Accuracy of using natural language processing methods for identifying healthcare-associated infections.
International journal of medical informatics
Jun 6, 2018
OBJECTIVE: There is a growing interest in using natural language processing (NLP) for healthcare-associated infections (HAIs) monitoring. A French project consortium, SYNODOS, developed a NLP solution for detecting medical events in electronic medica...
Electronic Surveillance For Catheter-Associated Urinary Tract Infection Using Natural Language Processing.
AMIA ... Annual Symposium proceedings. AMIA Symposium
Apr 16, 2018
Catheter-associated urinary tract infection (CAUTI) is a common and costly healthcare-associated infection, yet measuring it accurately is challenging and resource-intensive. Electronic surveillance promises to make this task more objective and effic...
Feasibility of a real-time hand hygiene notification machine learning system in outpatient clinics.
The Journal of hospital infection
Apr 9, 2018
BACKGROUND: Various technologies have been developed to improve hand hygiene (HH) compliance in inpatient settings; however, little is known about the feasibility of machine learning technology for this purpose in outpatient clinics.
Healthcare-associated ventriculitis and meningitis in a neuro-ICU: Incidence and risk factors selected by machine learning approach.
Journal of critical care
Mar 23, 2018
PURPOSE: To define the incidence of healthcare-associated ventriculitis and meningitis (HAVM) in the neuro-ICU and to identify HAVM risk factors using tree-based machine learning (ML) algorithms.
Estimating Local Costs Associated With Clostridium difficile Infection Using Machine Learning and Electronic Medical Records.
Infection control and hospital epidemiology
Nov 6, 2017
BACKGROUND Reported per-patient costs of Clostridium difficile infection (CDI) vary by 2 orders of magnitude among different hospitals, implying that infection control officers need precise, local analyses to guide rational decision making between in...
Female
Aged
Aged, 80 and over
Young Adult
Clostridioides difficile
Humans
Adult
Health Care Costs
Machine Learning
Male
Retrospective Studies
Middle Aged
Electronic Health Records
New York
Adolescent
Length of Stay
Cross Infection
Logistic Models
Clostridium Infections
Tertiary Care Centers
Propensity Score
Development and validation of a fast micro-extraction by packed sorbent UHPLC-PDA method for the simultaneous determination of linezolid and ciprofloxacin in human plasma from patients with hospital-acquired pneumonia.
Talanta
Nov 15, 2016
An ultra high-performance liquid chromatographic (UHPLC) method with PDA detection was developed and validated for the simultaneous quantification of linezolid and ciprofloxacin in human plasma and applied in hospital acquired pneumonia patients (HAP...
Detecting borderline infection in an automated monitoring system for healthcare-associated infection using fuzzy logic.
Artificial intelligence in medicine
Apr 28, 2016
BACKGROUND: Many electronic infection detection systems employ dichotomous classification methods, classifying patient data as pathological or normal with respect to one or several types of infection. An electronic monitoring and surveillance system ...
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