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AI Tackles Hospital Infections: Machine Learning Is Helping Clinicians.

IEEE pulse
For Ashley Zappia (Figure 1), getting her hands dirty was part of her job. Even though she always tried to remain as clean as possible, her work as a nursing aide at a Southern California hospital required a lot of diapering, changing, and other hand...

Estimating Local Costs Associated With Clostridium difficile Infection Using Machine Learning and Electronic Medical Records.

Infection control and hospital epidemiology
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...

Electronic Surveillance For Catheter-Associated Urinary Tract Infection Using Natural Language Processing.

AMIA ... Annual Symposium proceedings. AMIA Symposium
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...

Arden Syntax MLM Building Blocks for Microbiological Concepts and Their Application in Infection Surveillance.

Studies in health technology and informatics
BACKGROUND: The diagnosis - and hence definitions - of healthcare-associated infections (HAIs) rely on microbiological laboratory test results in specific constellations.

Detecting borderline infection in an automated monitoring system for healthcare-associated infection using fuzzy logic.

Artificial intelligence in medicine
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 ...

Use of an Artificial Neural Network to Construct a Model of Predicting Deep Fungal Infection in Lung Cancer Patients.

Asian Pacific journal of cancer prevention : APJCP
BACKGROUND: The statistical methods to analyze and predict the related dangerous factors of deep fungal infection in lung cancer patients were several, such as logic regression analysis, meta-analysis, multivariate Cox proportional hazards model anal...