[en] Currently, there is a growing societal interest in developing automatic systems to assist healthcareprofessionals in their daily tasks, particularly in the field of Evidence Based Medicine (EBM). These systems aim to emulate the clinical reasoning process used by expert doctors to extract relevant information from unstructured clinical texts. Moreover, another objective of them is to present this relevant information in a structured and understandable format for machines, which facilitates automatic analysis. Argument Mining (AM) is a field closely related to EBM dedicated to examine the evidence considered by doctors in a clinical case and the reasoning used. Thus, the AM is responsible for identifying, classifying and evaluating the arguments and the quality of the argumentative structurespresent in the texts. Due to the sensitivity of medical information and patient privacy, the datasets available in the medical field are often limited, which restricts the development of these processes. This research work focuses on developing a AM system that can identify and analyze the arguments which are present in clinical texts, as well as the argumentative relationships between them. This approach is supported by techniques such as Named Entity Recognition (NER) and Question Answering (QA), which have shown superior performance in situations of data scarcity, compared to other methodologies. The present work contributes significantly to the daily tasks of medical staff, which eases the recognition of argumentative structures that approve or disapprove the different possible diagnoses of a patient. The support of this type of intelligent systems alleviates the workload of healthcare professionals and allows them to offer better care to patients. Simultaneously, it represents great improvements in the prevention and early detection of diseases which allows them to be treated in many cases in time. Finally, this system can be considered a first approach to Argument Mining in the clinical field.It may become a system that develops evidence based justifications based on patient information extracted by a machine.

[eu] Gaur egun, gizarteak gero eta interes handiagoa du osasun-profesionalei eguneroko zereginetan laguntzeko sistema automatikoak garatzeko, batez ere, Ebidentzian Oinarritutako Medikuntzaren esparruan (Evidence Based Medicine, EBM). Sistema horiek mediku adituek erabilitako arrazoibideklinikoko prozesua emulatzeko gai dira; egituratu gabeko testu klinikoetatik informazio garrantzitsua atera eta makinentzako formatu egituratu eta ulergarri bihurtzen dute, haien analisi automatikoa erraztuz. Argumentu Meatzaritza (Argument Mining, AM) EBMrekin estuki lotutako alorra da, medikuek kasu kliniko batean aintzat hartutako ebidentziak eta erabilitako arrazoibidea aztertzen dituena. Horrela, AMk testuetako argumentuen eta argudiaketa-egituren kalitatea identifikatu, sailkatu eta ebaluatzen ditu. Informazio medikoaren sentsibilitatea eta pazienteen pribatutasuna direla eta, arlo medikoan eskuragarri dauden datu-multzoak mugatuak dira, eta horrek prozesu horien garapena mugatzendu. Ikerketa-lan honen helburua AM sistema bat garatzea da, testu klinikoetan dauden argudioak identifikatu eta aztertu ahal izateko, bai eta haien arteko argudiaketa-erlazioak ere. Ikuspegi hori hainbat teknikatan oinarritzen da, hala nola, Entitate Izendunen Ezagutza (Named Entity Recognition, NER) eta Galdera-Erantzun (Question Answering, QA) tekniketan. Horiek errendimendu handiagoa erakutsi dute datuen urritasun egoeren aurrean, beste metodologia batzuekin alderatuta. Lan honek nabarmen laguntzen du medikuen eguneroko zereginetan. Izan ere, paziente baten diagnostiko posibleak sostengatzen edo gaitzesten dituzten argudiaketa-egiturak identifikatzea ahalbidetzen du. Horrelako sistema adimenduna erabiltzeak osasun-profesionalen lan-karga arintzea dakar, eta pazienteei arreta hobea eskaintzea posible egiten du. Aldi berean, hobekuntza handiak eragiten ditu gaixotasunen prebentzioan eta horien detekzio goiztiarrean, eta kasu askotan garaiz tratatzeko aukera ematen du. Azkenik, horrelako sistema lehen hurbilketa gisa har daiteke testuinguru klinikoko Argumentu Meatzaritzan. Etorkizunean, ebidentzietan oinarritutako justifikazioak garatuko dituen sistema bihur daiteke, makina batek pazientearen inguruan ateratako informaziotik abiatuz.