Leveraging Clinical Notes for Enhancing Decision-Making Systems with Relevant Patient Information Jo˜ ao Rafael Almeida 1,2(B ) , Jo˜ ao Figueira Silva 1 , Alejandro Pazos Sierra 2 , Sergio Matos 1 , and Jos´ e Lu´ ıs Oliveira 1 1 DETI/IEETA, University of Aveiro, Aveiro, Portugal {joao.rafael.almeida,joaofsilva,aleixomatos,jlo}@ua.pt 2 Department of Information and Communications Technologies, University of A Coru˜ na, A Coru˜ na, Spain alejandro.pazos@udc.es Abstract. Personalised treatment is usually needed for hospitalised patients afflicted by secondary illnesses that demand daily medication. Even though clinical guidelines were designed to consider those circum- stances exist, current decision-support features fail to assimilate detailed relevant patient information. This creates opportunities for the devel- opment of systems capable of performing a real-time evaluation of such data against existing knowledge and providing recommendations during clinical treatments. Herein, we describe a proposal for a new feature to be integrated with electronic health record (EHR) systems which can enrich the health treatment process through the automatic extraction of information from patient medical notes and the aggregation of this novel information in clinical protocols. The purpose of this work is to exploit the historical component of the patient trajectory to improve the performance of clinical decision support systems. Keywords: EHR · CDSS · NLP · Clinical notes · Clinical decision-making · Treatment guidance 1 Introduction Throughout the years technology and its breakthroughs have proved fruitful for the field of medicine and health care, fostering an enhanced quality of life for the general population. Tools and data sources originated from the fusion of technology with medicine have led to improvements in disease prevention, diag- nosis and treatment, and can play a vital role in clinical pipelines by assisting physicians in tasks such as clinical decision making and patient follow-up. More- over, the increased access to medical data enables the shift towards the more patient-centric view of personalised medicine. J. F. Silva—Contributed equally with the first author to this work. c  Springer Nature Switzerland AG 2021 X. Ye et al. (Eds.): BIOSTEC 2020, CCIS 1400, pp. 521–540, 2021. https://doi.org/10.1007/978-3-030-72379-8_26