2022
Sousa, R.; Oliveira, D.; Durães, D.; Neto, C.; Machado, J.
Medical Recommendation System Based on Daily Clinical Reports: A Proposed NLP Approach for Emergency Departments Proceedings Article
Em: M., Stahl F. Bramer (Ed.): pp. 315-320, Springer Science and Business Media Deutschland GmbH, 2022, ISSN: 03029743, (cited By 0; Conference of 42nd SGAI International Conference on Innovative Techniques and Applications of Artificial Intelligence, AI 2022 ; Conference Date: 13 December 2022 Through 15 December 2022; Conference Code:287589).
Resumo | Links | BibTeX | Etiquetas: Artificial intelligence; Diagnosis; Emergency rooms; Natural language processing systems; Recommender systems, Decision support systems, Emergency departments; Emergency health department; Global impacts; Hospital administration; Language processing; Natural language processing; Natural languages; Operational management; Text-mining; Unstructured data
@inproceedings{Sousa2022315,
title = {Medical Recommendation System Based on Daily Clinical Reports: A Proposed NLP Approach for Emergency Departments},
author = {R. Sousa and D. Oliveira and D. Durães and C. Neto and J. Machado},
editor = {Stahl F. Bramer M.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85144818897&doi=10.1007%2f978-3-031-21441-7_24&partnerID=40&md5=c46b6c78b09ded01e7ecd348b5a7dea6},
doi = {10.1007/978-3-031-21441-7_24},
issn = {03029743},
year = {2022},
date = {2022-01-01},
journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
volume = {13652 LNAI},
pages = {315-320},
publisher = {Springer Science and Business Media Deutschland GmbH},
abstract = {The operational management of an emergency department (ED) requires more attention from hospital administration since it can have a global impact on the institution’s management, increasing the probability of adverse events and worsening hospital expenses. Effective management of an ED potentially results in fewer hospitalisations after an ED admission. The purpose of the present study is to perform a multi-class prediction based on: a) structured data and unstructured data in an ED episode; and b) unstructured data generated during the inpatient event, just after the ED episode. The designed prediction model will lay the foundation for an ED Decision Support System based on symptoms and principal diagnoses. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.},
note = {cited By 0; Conference of 42nd SGAI International Conference on Innovative Techniques and Applications of Artificial Intelligence, AI 2022 ; Conference Date: 13 December 2022 Through 15 December 2022; Conference Code:287589},
keywords = {Artificial intelligence; Diagnosis; Emergency rooms; Natural language processing systems; Recommender systems, Decision support systems, Emergency departments; Emergency health department; Global impacts; Hospital administration; Language processing; Natural language processing; Natural languages; Operational management; Text-mining; Unstructured data},
pubstate = {published},
tppubtype = {inproceedings}
}