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).
Combining rules and machine learning for extraction of temporal expressions and events from clinical narratives
Topic Categorisation of Statements in Suicide Notes with Integrated Rules and Machine Learning
Combining Lexical Profiling, Rules and Machine Learning for Disease Prediction from Hospital Discharge Summaries
Identification of transcription factor contexts in literature using machine learning approaches
Identification of transcription factor contexts in literature using machine learning approaches
DEPEND
Mining free-text patient feedback comments As part of the NIHR-funded project Developing and Enhancing the Usefulness of Patient Experience and Narrative Data (DEPEaND), we have developed a text mining software to analyse themes and sentiment expressed in free-text patient service feedback comments (e.g. Family and Friends Test). The topic-specific opinion…
Learning to identify Protected Health Information in free text
Learning to identify Protected Health Information by integrating knowledge- and data-driven algorithms: A case study on psychiatric evaluation notes The paper presents our experience in learning to identify personal information as part of the 2016 CEGS N-GRID Shared Tasks Track 1, which evaluated de-identification methods on a set of psychiatric…
Temporal expression extraction with extensive feature type selection and a posteriori label adjustment
CliNER
About CliNER CliNER is a command line tool for identification of mentions of four categories of clinically relevant events: Problems, Tests, Treatments and Clinical Departments. It also recognises and normalises clinical temporal expressions. It was developed as part of the i2b2 2012 text mining challenge and therefore has been trained…