Publicação
Predicting Cardiovascular Disease from Unstructured Clinical Notes: Application of Advanced Natural Language Processing on MIMIC-IV database
| datacite.subject.fos | Ciências Naturais::Ciências da Computação e da Informação | pt_PT |
| dc.contributor.advisor | Albuquerque, Carina Isabel Andrade | |
| dc.contributor.author | Rashid, Kauser Al | |
| dc.date.accessioned | 2024-10-30T16:03:26Z | |
| dc.date.available | 2025-10-24T00:30:21Z | |
| dc.date.issued | 2024-10-24 | |
| dc.description | Dissertation presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics, specialization in Data Science | pt_PT |
| dc.description.abstract | Cardiovascular disease (CVD) is the leading cause of death globally, significantly impacting mortality and morbidity individual across different demographics. The aim of this study is to leverage attention-based Natural Language Process (NLP) models to predict severe forms of CVD from unstructured clinical notes using discharge summaries of patients in MIMIC-IV dataset. Through a comparative analysis of various models that included LSTM, BERT, clinicalBERT and Clinical LongFormer, as well as modified versions of BERT and clinicalBERT, this research finds that attention-based models outperform traditional deep learning models in handling long and complex unstructured clinical notes, and therefore make better predictions. The best performing model identified in this study is BERT (sliding window), as this model was most accurate (Accuracy: 0.73), well-balanced in predictions (F1-Micro: 0.80) and excelled at correctly predicting specific CVD (AUC: 0.83). Although there are some limitations, this study demonstrates the predictive power of advanced attention-based models in healthcare, which would enable better disease predictions and timely interventions to reduce mortality and morbidity due to CVD. | pt_PT |
| dc.identifier.tid | 203782305 | pt_PT |
| dc.identifier.uri | http://hdl.handle.net/10362/174337 | |
| dc.language.iso | eng | pt_PT |
| dc.subject | Electronic Health Records (EMRs) | pt_PT |
| dc.subject | Clinical Notes | pt_PT |
| dc.subject | Natural Language Processing | pt_PT |
| dc.subject | Transformerbased Methods | pt_PT |
| dc.subject | Cardiovascular Diseases | pt_PT |
| dc.subject | SDG 3 - Good health and well-being | pt_PT |
| dc.title | Predicting Cardiovascular Disease from Unstructured Clinical Notes: Application of Advanced Natural Language Processing on MIMIC-IV database | pt_PT |
| dc.type | master thesis | |
| dspace.entity.type | Publication | |
| rcaap.rights | openAccess | pt_PT |
| rcaap.type | masterThesis | pt_PT |
| thesis.degree.name | Mestrado em Ciência de Dados e Métodos Analíticos Avançados, especialização em Ciência de Dados | pt_PT |