Medical Information Extraction With NLP-Powered QABots: A Real-World Scenario Article Swipe
Claudio Crema
,
Federico Verde
,
Pietro Tiraboschi
,
Camillo Marra
,
Andrea Arighi
,
Silvia Fostinelli
,
Guido Maria Giuffrè
,
Vera Pacoova Dal Maschio
,
Federica L’Abbate
,
Federica Solca
,
Barbara Poletti
,
Vincenzo Silani
,
Emanuela Rotondo
,
Vittoria Borracci
,
Roberto Vimercati
,
Valeria Crepaldi
,
Emanuela Inguscio
,
Massimo Filippi
,
Francesca Caso
,
Alessandra Maria Rosati
,
Davide Quaranta
,
Giuliano Binetti
,
Ilaria Pagnoni
,
Manuela Morreale
,
Francesca Burgio
,
Michelangelo Stanzani Maserati
,
Sabina Capellari
,
Matteo Pardini
,
Nicola Girtler
,
Federica Piras
,
Fabrizio Piras
,
Stefania Lalli
,
Elena Perdixi
,
Gemma Lombardi
,
Sonia Di Tella
,
Alfredo Costa
,
Marco Capelli
,
Cira Fundarò
,
Marina Manera
,
Cristina Muscio
,
Elisa Pellencin
,
Raffaele Lodi
,
Fabrizio Tagliavini
,
Alberto Redolfi
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1109/jbhi.2024.3450118
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1109/jbhi.2024.3450118
The advent of computerized medical recording systems in healthcare facilities has made data retrieval tasks easier, compared to manual recording. Nevertheless, the potential of the information contained within medical records remains largely untapped, mostly due to the time and effort required to extract data from unstructured documents. Natural Language Processing (NLP) represents a promising solution to this challenge, as it enables the use of automated text-mining tools for clinical practitioners. In this work, we present the architecture of the Virtual Dementia Institute (IVD), a consortium of sixteen Italian hospitals, using the NLP Extraction and Management Tool (NEMT), a (semi-) automated end-to-end pipeline that extracts relevant information from clinical documents and stores it in a centralized REDCap database. After defining a common Case Report Form (CRF) across the IVD hospitals, we implemented NEMT, the core of which is a Question Answering Bot (QABot) based on a modern NLP model. This QABot is fine-tuned on thousands of examples from IVD centers. Detailed descriptions of the process to define a common minimum dataset, Inter-Annotator Agreement calculated on clinical documents, and NEMT results are provided. The best QABot performance show an Exact Match score (EM) of 78.1%, a F1-score of 84.7%, a Lenient Accuracy (LAcc) of 0.834, and a Mean Reciprocal Rank (MRR) of 0.810. EM and F1 scores outperform the same metrics obtained with ChatGPTv3.5 (68.9% and 52.5%, respectively). With NEMT the IVD has been able to populate a database that will contain data from thousands of Italian patients, all screened with the same procedure. NEMT represents an efficient tool that paves the way for medical information extraction and exploitation for new research studies.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1109/jbhi.2024.3450118
- OA Status
- green
- Cited By
- 3
- References
- 47
- Related Works
- 10
- OpenAlex ID
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All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4401908499Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1109/jbhi.2024.3450118Digital Object Identifier
- Title
-
Medical Information Extraction With NLP-Powered QABots: A Real-World ScenarioWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2024Year of publication
- Publication date
-
2024-08-27Full publication date if available
- Authors
-
Claudio Crema, Federico Verde, Pietro Tiraboschi, Camillo Marra, Andrea Arighi, Silvia Fostinelli, Guido Maria Giuffrè, Vera Pacoova Dal Maschio, Federica L’Abbate, Federica Solca, Barbara Poletti, Vincenzo Silani, Emanuela Rotondo, Vittoria Borracci, Roberto Vimercati, Valeria Crepaldi, Emanuela Inguscio, Massimo Filippi, Francesca Caso, Alessandra Maria Rosati, Davide Quaranta, Giuliano Binetti, Ilaria Pagnoni, Manuela Morreale, Francesca Burgio, Michelangelo Stanzani Maserati, Sabina Capellari, Matteo Pardini, Nicola Girtler, Federica Piras, Fabrizio Piras, Stefania Lalli, Elena Perdixi, Gemma Lombardi, Sonia Di Tella, Alfredo Costa, Marco Capelli, Cira Fundarò, Marina Manera, Cristina Muscio, Elisa Pellencin, Raffaele Lodi, Fabrizio Tagliavini, Alberto RedolfiList of authors in order
- Landing page
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https://doi.org/10.1109/jbhi.2024.3450118Publisher landing page
- Open access
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YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://hdl.handle.net/2158/1397416Direct OA link when available
- Concepts
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Computer science, Artificial intelligence, Natural language processing, Information extraction, Information retrievalTop concepts (fields/topics) attached by OpenAlex
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3Total citation count in OpenAlex
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2025: 3Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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