ArticleDiagnostics (Basel, Switzerland)2023
Validation of an Automated System for the Extraction of a Wide Dataset for Clinical Studies Aimed at Improving the Early Diagnosis of Candidemia.
Article in Diagnostics (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Deep Learning for the Early Diagnosis of Candidemia.Infectious diseases and therapy · 2025Article
- Prediction of candidemia with machine learning techniques: state of the art.Future microbiology · 2024Review
- Automated extraction of standardized antibiotic resistance and prescription data from laboratory information systems and electronic health records: a narrative review.Frontiers in antibiotics · 2024Review
- Explainable artificial intelligence and machine learning: novel approaches to face infectious diseases challenges.Annals of medicine · 2023Article
- Early diagnosis of candidemia with explainable machine learning on automatically extracted laboratory and microbiological data: results of the AUTO-CAND project.Annals of medicine · 2023Article
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15 authors.
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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.
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