Journal of biomedical informatics · 2024
Towards the automatic calculation of the EQUAL Candida Score: Extraction of CVC-related information from EMRs of critically ill patients with candidemia in Intensive Care Units.
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Mora Sara, Giacobbe Daniele Roberto, Bartalucci Claudia, Viglietti Giulia, Mikulska Malgorzata, Vena Antonio, Ball Lorenzo, Robba Chiara, Cappello Alice, Battaglini Denise, Brunetti Iole, Pelosi Paolo, Bassetti Matteo, Giacomini Mauro
Аннотация
OBJECTIVES: Candidemia is the most frequent invasive fungal disease and the fourth most frequent bloodstream infection in hospitalized patients. Its optimal management is crucial for improving patients' survival. The quality of candidemia management can be assessed with the EQUAL Candida Score. The objective of this work is to support its automatic calculation by extracting central venous catheter-related information from Italian text in clinical notes of electronic medical records.
MATERIALS AND METHODS: The sample includes 4,787 clinical notes of 108 patients hospitalized between January 2018 to December 2020 in the Intensive Care Units of the IRCCS San Martino Polyclinic Hospital in Genoa (Italy). The devised pipeline exploits natural language processing (NLP) to produce numerical representations of clinical notes used as input of machine learning (ML) algorithms to identify CVC presence and removal. It compares the performances of (i) rule-based method, (ii) count-based method together with a ML algorithm, and (iii) a transformers-based model.
RESULTS: Results, obtained with three different approaches, were evaluated in terms of weighted F1 Score. The random forest classifier showed the higher performance in both tasks reaching 82.35%.
CONCLUSION: The present work constitutes a first step towards the automatic calculation of the EQUAL Candida Score from unstructured daily collected data by combining ML and NLP methods. The automatic calculation of the EQUAL Candida Score could provide crucial real-time feedback on the quality of candidemia management, aimed at further improving patients' health.
MATERIALS AND METHODS: The sample includes 4,787 clinical notes of 108 patients hospitalized between January 2018 to December 2020 in the Intensive Care Units of the IRCCS San Martino Polyclinic Hospital in Genoa (Italy). The devised pipeline exploits natural language processing (NLP) to produce numerical representations of clinical notes used as input of machine learning (ML) algorithms to identify CVC presence and removal. It compares the performances of (i) rule-based method, (ii) count-based method together with a ML algorithm, and (iii) a transformers-based model.
RESULTS: Results, obtained with three different approaches, were evaluated in terms of weighted F1 Score. The random forest classifier showed the higher performance in both tasks reaching 82.35%.
CONCLUSION: The present work constitutes a first step towards the automatic calculation of the EQUAL Candida Score from unstructured daily collected data by combining ML and NLP methods. The automatic calculation of the EQUAL Candida Score could provide crucial real-time feedback on the quality of candidemia management, aimed at further improving patients' health.