Classifying Cyber-Risky Clinical Notes by Employing Natural Language Processing

Autor: Schmeelk, Suzanna, Dogo, Martins Samuel, Peng, Yifan, Patra, Braja Gopal

Clinical notes, which can be embedded into electronic medical records, document patient care delivery and summarize interactions between healthcare providers and patients. These clinical notes directly inform patient care and can also indirectly info

Externí odkaz: http://arxiv.org/abs/2203.12781

Exploring thematic coherence in fake news

Autor: Dogo, Martins Samuel, Padmanabhan, Deepak, Jurek-Loughrey, Anna

Publikováno v: Dogo, M S, Padmanabhan, D & Jurek-Loughrey, A 2021, Exploring thematic coherence in fake news . in Proccedings of the workshops of the European Conference on Machine Learning and Knowledge Discovery in Databases (ECML PKDD 2020): SoGood 2020, PDFL 2020, MLCS 2020, NFMCP 2020, DINA 2020, EDML 2020, XKDD 2020 and INRA 2020 . Communications in Computer and Information Science, vol. 1323, Springer, Cham, pp. 571-580, 8th International Workshop on News Recommendation and Analytics (INRA 2020), 14/09/2020 . https://doi.org/10.1007/978-3-030-65965-3_40

The spread of fake news remains a serious global issue; understanding and curtailing it is paramount. One way of differentiating between deceptive and truthful stories is by analyzing their coherence. This study explores the use of topic models to an

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=od______2607::77c66b77f8d8801f1da3703bc00f59b3
https://pure.qub.ac.uk/en/publications/0bdb99aa-ea11-49e7-b5cc-7e9b16a4aafe