NLP of Medical RecordsProfessor Jon Patrick Tuesday 8th May 2007 at 11am
AbstractThe medical record contains most of its worthwhile content in natural language and so building a domain specific processor would seem to be a useful and straightforward task. Unfortunately that is far from the real situation. The effective extraction of useful content from the medical record requires coming to terms with large medical ontologies, serious gradations of grammaticality, and documents of different registers, for example discharge summaries, clinical notes, and pathology reports. The motivations for processing the medical record are also diverse and leave open a series of design questions about the nature of how to target the NLP for the post processing systems. Some of these processing systems are decision support systems to guide patient care, auditing patient care against clinical protocols, extracting knowledge from published texts, or for epidemiological analysis. The task is further complicated by the need to extract the data from large legacy hospital information systems and in some cases to bolt on new technology to old systems. These problems will be discussed with illustrations from the large range of projects oriented around the needs of our health collaborators. Short resumeJon Patrick first built language technology systems in the 1980s to record the descriptions of teams sports in real-time. His early systems were adopted by television for Australian Rules football, NRL clubs of Rugby, WACB for cricket, and the Australian Institute of Sport for water polo. In the early 90s he trained in psychotherapy and developed a computational approach to analysing therapeutic language to asses its effectiveness. Since moving to the University of Sydney in 1998 he has published a grammar reference book of the Basque language, and developed an active learning system for converting multi-lingual dictionaries into XML knowledgebases. He was director of the successful Scamseek Project which is a semantic analyser developed for ASIC that detects financial scams on the Internet. He was awarded the Eureka Science Prize in 2005 for this work. |