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

Autor(es)

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

Afiliação do(s) autor(es)

^[1] Univ Grenoble Alpes, Grenoble - France

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

• Tipo de documento: Artigo Científico
• Fonte: JOURNAL OF HEALTHCARE INFORMATICS RESEARCH; JUN 2021.
• Citações Web of Science: 0

Resumo

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)

• Processo FAPESP: 18/17620-5 - Medicina preventiva por meio de técnicas de deep learning aplicadas ao prognóstico de saúde
• Beneficiário: José Fernando Rodrigues Júnior
• Modalidade de apoio: Bolsas no Exterior - Pesquisa
• Processo FAPESP: 16/17078-0 - Mineração, indexação e visualização de Big Data no contexto de sistemas de apoio à decisão clínica (MIVisBD)
• Beneficiário: Agma Juci Machado Traina
• Modalidade de apoio: Auxílio à Pesquisa - Temático