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Validation of an Automated System for the Extraction of a Wide Dataset for Clinical Studies Aimed at Improving the Early Diagnosis of Candidemia.
- Source :
- Diagnostics (2075-4418); Mar2023, Vol. 13 Issue 5, p961, 9p
- Publication Year :
- 2023
-
Abstract
- There is increasing interest in assessing whether machine learning (ML) techniques could further improve the early diagnosis of candidemia among patients with a consistent clinical picture. The objective of the present study is to validate the accuracy of a system for the automated extraction from a hospital laboratory software of a large number of features from candidemia and/or bacteremia episodes as the first phase of the AUTO-CAND project. The manual validation was performed on a representative and randomly extracted subset of episodes of candidemia and/or bacteremia. The manual validation of the random extraction of 381 episodes of candidemia and/or bacteremia, with automated organization in structured features of laboratory and microbiological data resulted in ≥99% correct extractions (with confidence interval < ±1%) for all variables. The final automatically extracted dataset consisted of 1338 episodes of candidemia (8%), 14,112 episodes of bacteremia (90%), and 302 episodes of mixed candidemia/bacteremia (2%). The final dataset will serve to assess the performance of different ML models for the early diagnosis of candidemia in the second phase of the AUTO-CAND project. [ABSTRACT FROM AUTHOR]
- Subjects :
- CANDIDEMIA
EARLY diagnosis
MACHINE learning
HOSPITAL laboratories
BACTEREMIA
Subjects
Details
- Language :
- English
- ISSN :
- 20754418
- Volume :
- 13
- Issue :
- 5
- Database :
- Complementary Index
- Journal :
- Diagnostics (2075-4418)
- Publication Type :
- Academic Journal
- Accession number :
- 162348940
- Full Text :
- https://doi.org/10.3390/diagnostics13050961