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(Reference retrieved automatically from Web of Science through information on FAPESP grant and its corresponding number as mentioned in the publication by the authors.)

Real-world Patient Trajectory Prediction from Clinical Notes Using Artificial Neural Networks and UMLS-Based Extraction of Concepts

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Author(s):
Zaghir, Jamil [1] ; Rodrigues-Jr, Jose F. ; Goeuriot, Lorraine [2] ; Amer-Yahia, Sihem [2]
Total Authors: 4
Affiliation:
[1] Univ Grenoble Alpes, Grenoble - France
[2] Univ Grenoble Alpes, CNRS, Grenoble - France
Total Affiliations: 2
Document type: Journal article
Source: JOURNAL OF HEALTHCARE INFORMATICS RESEARCH; JUN 2021.
Web of Science Citations: 0
Abstract

As more data is generated from medical attendances and as Artificial Neural Networks gain momentum in research and industry, computer-aided medical prognosis has become a promising technology. A common approach to perform automated prognoses relies on textual clinical notes extracted from Electronic Health Records (EHRs). Data from EHRs are fed to neural networks that produce a set with the most probable medical problems to which a patient is subject in her/his clinical future, including clinical conditions, mortality, and readmission. Following this research line, we introduce a methodology that takes advantage of the unstructured text found in clinical notes by applying preprocessing, concepts extraction, and fine-tuned neural networks to predict the most probable medical problems to follow in a patient's clinical trajectory. Different from former works that focus on word embeddings and raw sets of extracted concepts, we generate a refined set of Unified Medical Language System (UMLS) concepts by applying a similarity threshold filter and a list of acceptable concept types. In our prediction experiments, our method demonstrated AUC-ROC performance of 0.91 for diagnosis codes, 0.93 for mortality, and 0.72 for readmission, determining an efficacy that rivals state-of-the-art works. Our findings contribute to the development of automated prognosis systems in hospitals where text is the main source of clinical history. (AU)

FAPESP's process: 18/17620-5 - Preventive medicine by means of deep learning techniques applied in healthcare prognosis
Grantee:José Fernando Rodrigues Júnior
Support Opportunities: Scholarships abroad - Research
FAPESP's process: 16/17078-0 - Mining, indexing and visualizing Big Data in clinical decision support systems (MIVisBD)
Grantee:Agma Juci Machado Traina
Support Opportunities: Research Projects - Thematic Grants