Automated Generation of Clinical Reports Using Sensing Technologies with Deep Learning Techniques
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http://hdl.handle.net/10045/142644
Title: | Automated Generation of Clinical Reports Using Sensing Technologies with Deep Learning Techniques |
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Authors: | Cabello Collado, Celia | Rodríguez Juan, Javier | Ortiz Pérez, David | Garcia-Rodriguez, Jose | Tomás, David | Vizcaya-Moreno, M. Flores |
Research Group/s: | Procesamiento del Lenguaje y Sistemas de Información (GPLSI) | Arquitecturas Inteligentes Aplicadas (AIA) | Enfermería Clínica (EC) |
Center, Department or Service: | Universidad de Alicante. Departamento de Lenguajes y Sistemas Informáticos | Universidad de Alicante. Departamento de Tecnología Informática y Computación | Universidad de Alicante. Departamento de Enfermería |
Keywords: | Text summarization | Healthcare | Multimodal data | Audio sensors | Transformers |
Issue Date: | 25-Apr-2024 |
Publisher: | MDPI |
Citation: | Sensors. 2024, 24(9): 2751. https://doi.org/10.3390/s24092751 |
Abstract: | This study presents a pioneering approach that leverages advanced sensing technologies and data processing techniques to enhance the process of clinical documentation generation during medical consultations. By employing sophisticated sensors to capture and interpret various cues such as speech patterns, intonations, or pauses, the system aims to accurately perceive and understand patient–doctor interactions in real time. This sensing capability allows for the automation of transcription and summarization tasks, facilitating the creation of concise and informative clinical documents. Through the integration of automatic speech recognition sensors, spoken dialogue is seamlessly converted into text, enabling efficient data capture. Additionally, deep models such as Transformer models are utilized to extract and analyze crucial information from the dialogue, ensuring that the generated summaries encapsulate the essence of the consultations accurately. Despite encountering challenges during development, experimentation with these sensing technologies has yielded promising results. The system achieved a maximum ROUGE-1 metric score of 0.57, demonstrating its effectiveness in summarizing complex medical discussions. This sensor-based approach aims to alleviate the administrative burden on healthcare professionals by automating documentation tasks and safeguarding important patient information. Ultimately, by enhancing the efficiency and reliability of clinical documentation, this innovative method contributes to improving overall healthcare outcomes. |
Sponsor: | We would like to thank “A way of making Europe” European Regional Development Fund (ERDF) and MCIN/AEI/10.13039/501100011033 for supporting this work under the “CHAN-TWIN” project (grant TED2021-130890B-C21. HORIZON-MSCA-2021-SE-0 action number: 101086387, REMARKABLE, Rural Environmental Monitoring via ultra wide-ARea networKs And distriButed federated Learning; CIAICO/2022/132 Consolidated group project “AI4Health” funded by the Valencian government and International Center for Aging Research ICAR funded project “IASISTEM.” This work has also been supported by a Valencian government grant for PhD studies, CIACIF/2022/175 and a research initiation grant from the University of Alicante, AII23-12. |
URI: | http://hdl.handle.net/10045/142644 |
ISSN: | 1424-8220 |
DOI: | 10.3390/s24092751 |
Language: | eng |
Type: | info:eu-repo/semantics/article |
Rights: | © 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
Peer Review: | si |
Publisher version: | https://doi.org/10.3390/s24092751 |
Appears in Collections: | Research funded by the EU INV - Enfermería Clínica - Artículos de Revistas INV - AIA - Artículos de Revistas INV - GPLSI - Artículos de Revistas |
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