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

Author(s)

Zaghir, Jamil ^[1] ; Rodrigues-Jr, Jose F. ; Goeuriot, Lorraine ^[2] ; Amer-Yahia, Sihem ^[2]

Affiliation

^[1] Univ Grenoble Alpes, Grenoble - France

^[2] Univ Grenoble Alpes, CNRS, Grenoble - France

• 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