602 USING DATA MINING ALGORITHMS TO OPTIMIZE EMPIRIC ANTIMICROBIAL THERAPY FOR OUTPATIENT URINARY TRACT INFECTIONS

2010 ◽  
Vol 183 (4S) ◽  
Author(s):  
Hannah Alphs ◽  
John Cashy ◽  
Ophir Frieder ◽  
Anthony Schaeffer
Keyword(s):  
Data Mining ◽  
Urinary Tract ◽  
Urinary Tract Infections ◽  
Antimicrobial Therapy ◽  
Data Mining Algorithms ◽  
Empiric Antimicrobial Therapy ◽  
Using Data ◽  
Tract Infections ◽  
Mining Algorithms

scholarly journals Changes in the management of urinary tract infections in women: impact of the new recommendations on antibiotic prescribing behavior in France, between 2014 and 2019

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Arthur Piraux ◽  
Sébastien Faure ◽  
Kurt G. Naber ◽  
Jakhongir F. Alidjanov ◽  
Aline Ramond-Roquin
Keyword(s):  
Urinary Tract ◽  
Urinary Tract Infections ◽  
Secondary Care ◽  
Administrative Database ◽  
Community Pharmacies ◽  
Antibiotic Prescribing ◽  
Prescribing Behavior ◽  
Using Data ◽  
New Guidelines ◽  
Tract Infections

Abstract Background Urinary tract infections (UTIs) are among the most common infections that require antibiotic therapy. In December 2015, new guidelines for UTI management were published in France with the aim of reducing antibiotic misuse and the risk of antimicrobial resistance. Objectives To analyze changes in antibiotic prescribing behavior for acute uncomplicated UTI in women in France from 2014 to 2019. Methods Retrospective study using data extracted from the medico-administrative database ‘OpenMedic’ that is linked to the French National Health Data System and collects data on the reimbursement of prescribed drugs. The analyses focused on the number of boxes of antibiotics delivered by community pharmacies, the molecule class, and the prescriber’s specialty. Results Overall, antibiotic dispensing by community pharmacies increased by 2% between 2014 and 2019, but with differences in function of the antibiotic class. The use of antibiotics recommended as first-line and second-line treatment increased (+ 41% for fosfomycin and + 7430% for pivmecillinam). Conversely, the dispensing of lomefloxacin and norfloxacin decreased by 80%, and that of ciprofloxacin by 26%. Some antibiotics were mostly prescribed by general practitioners (lomefloxacin, pivmecillinam) and others by secondary care physicians (ofloxacin). Dispensing increased for antibiotics prescribed by secondary care physicians (+ 13% between 2014 and 2019) and decreased for antibiotics prescribed by GPs (− 2% for the same period). Conclusion These data suggest that the new recommendations are followed, as indicated by the increased prescription of fosfomycin and pivmecillinam and decreased prescription of fluoroquinolones. However, the efficient transmission and implementation of new recommendations by practitioners requires time, means and dedicated tools.

scholarly journals Simple Correlation Between Weather and COVID-19 Pandemic Using Data Mining Algorithms

2020 ◽  
Vol 982 ◽  
pp. 012015
Author(s):  
Ari Fadli ◽  
Azis Wisnu Widhi Nugraha ◽  
Muhammad Syaiful Aliim ◽  
Acep Taryana ◽  
Yogiek Indra Kurniawan ◽  
...  
Keyword(s):  
Data Mining ◽  
Simple Correlation ◽  
Data Mining Algorithms ◽  
Using Data ◽  
Mining Algorithms

scholarly journals Osteoporosis Risk Prediction Using Data Mining Algorithms

2020 ◽  
Author(s):  
Efat Jabarpour ◽  
Amin Abedini ◽  
Abbasali Keshtkar
Keyword(s):  
Data Mining ◽  
Personal Information ◽  
Disease Diagnosis ◽  
Support Vector ◽  
Data Mining Algorithms ◽  
Industry Standard ◽  
Disease Information ◽  
Increased Risk ◽  
Using Data ◽  
Mining Algorithms

Introduction: Osteoporosis is a disease that reduces bone density and loses the quality of bone microstructure leading to an increased risk of fractures. It is one of the major causes of inability and death in elderly people. The current study aims at determining the factors influencing the incidence of osteoporosis and providing a predictive model for the disease diagnosis to increase the diagnostic speed and reduce diagnostic costs. Methods: An Individual's data including personal information, lifestyle, and disease information were reviewed. A new model has been presented based on the Cross-Industry Standard Process CRISP methodology. Besides, Support Vector Machine (SVM) and Bayes methods (Tree Augmented Naïve Bayes (TAN)) and Clementine12 have been used as data mining tools. Results: Some features have been detected to affect this disease. The rules have been extracted that can be used as a pattern for the prediction of the patients' status. Classification precision was calculated to be 88.39% for SVM, and 91.29% for  (TAN) when the precision of  TAN  is higher comparing to other methods. Conclusion: The most effective factors concerning osteoporosis are detected and can be used for a new sample with defined characteristics to predict the possibility of osteoporosis in a person.  

Obstetric and Gynecologic Issues Related to Infectious Diseases

2012 ◽  
pp. 464-484
Author(s):  
Kristi L. Boldt
Keyword(s):  
Urinary Tract ◽  
Antibiotic Therapy ◽  
Urinary Tract Infections ◽  
Antimicrobial Therapy ◽  
Placental Transfer ◽  
Serum Levels ◽  
Common Complication ◽  
Excretion In Milk ◽  
Tract Infections ◽  
Preterm Rupture Of Membranes

Infection is the most common complication during pregnancy and the postpartum period. Choices are limited for antibiotic therapy are limited. One must take into account the effect of pregnancy on serum levels, distribution of antibiotics, placental transfer, the fetus, the newborn, excretion in milk, the breast-feeding infant. Antimicrobial therapy is selected on the basis of experience and guidelines. Diagnosis and treatment of urinary tract infections, bacterial vaginosis, preterm labor, preterm rupture of membranes, intra-amniotic infection, and major perinatal and puerperal infections are reviewed.

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