New systematic review: Spasic I, Nenadic G: Clinical Text Data in Machine Learning: Systematic Review. JMIR Med Inform. 2020;8(3):e17984. doi:10.2196/17984 (link).
Extracting patient data from tables in clinical literature: Case study on extraction of BMI, weight and number of patients
Contextualisation of Biomedical Knowledge through Large-scale Processing of Literature, Clinical Narratives and Social Media
Combining knowledge- and data-driven methods for de-identification of clinical narratives
Using local lexicalized rules to identify heart disease risk factors in clinical notes
Healthcare text mining projects: mining clinical narratives and patient-generated data
We currently run a number of projects to extract various structured data from unstructured clinical narratives and electronic healthcare records (EHRs). In previous projects we have developed a combination of rule-based and machine-learning methods to identify diseses that a patient has or does not have (“disease status”), including identification of…
Linked2Safety – a next-generation, secure linked-data medical information space for semantically-interconnecting electronic health records and clinical trials systems
The main aim of the Linked2Safety project is to explore the Semantic Web and Linked Data to facilitate semantic interlinking of electronic health records (EHRs) and clinical trials systems for gathering and sharing knowledge to support decision making in medical and clinical research. The vision is to facilitate early detection…
Clinical Temporal Expression Mining
The aim of this project is to extract mentions of temporal expressions in clinical narratives (and patient-generated data) using a combination of rule-based and machine-learning methods. We also aim to normalise those mentions through mapping them to their value (using the ISO-8601 representation (e.g. “2012-10-31T09:00″) and type (e.g. Date, Duration,…
Natural Language Processing for Clinical Data: Continuous Success at i2b2 Challenges
2011 – This year we took again part in the annual i2b2 shared task, an international text mining challenge in the clinical/health-care domain. The team composed of members from University of Novi Sad (Kovacevic, A.) and University of Manchester (Dehghan, A., Nenadic G. and Keane, J.). The aim of the challenge…