Biomedical Question Answering: A Survey of Approaches and Challenges

Automatic Question Answering (QA) has been successfully applied in various domains such as search engines and chatbots. Biomedical QA (BQA), as an emerging QA task, enables innovative applications to effectively perceive, access and understand complex biomedical knowledge. There have been tremendous developments of BQA in the past two decades, which we classify into 5 distinctive approaches: classic, information retrieval, machine reading comprehension, knowledge base and question entailment approaches. In this survey, we introduce available datasets and representative methods of each BQA approach in detail. Despite the developments, BQA systems are still immature and rarely used in real-life settings. We identify and characterize several key challenges in BQA that might lead to this issue, and discuss some potential future directions to explore.Comment: In submission to ACM Computing Survey

A Transformational-based Learner for Dependency Grammars in Discharge Summaries

NLP systems will be more portable among medical domains if acquisition of semantic lexicons can be facilitated. We are pursuing lexical acquisition through the syntactic relationships of words in medical corpora. Therefore we require a syntactic parser which is flexible, portable, captures head-modifier pairs and does not require a large training set. We have designed a dependency grammar parser that learns through a transformational-based algorithm. We propose a novel design for templates and transformations which capitalize on the dependency structure directly and produces human-readable rules. Our parser achieved a 77% accurate parse training on only 830 sentences. Further work will evaluate the usefulness of this parse for lexical acquisition.

Real-time classifiers from free-text for continuous surveillance of small animal disease

A wealth of information of epidemiological importance is held within unstructured narrative clinical records. Text mining provides computational techniques for extracting usable information from the language used to communicate between humans, including the spoken and written word. The aim of this work was to develop text-mining methodologies capable of rendering the large volume of information within veterinary clinical narratives accessible for research and surveillance purposes. The free-text records collated within the dataset of the Small Animal Veterinary Surveillance Network formed the development material and target of this work. The efficacy of pre-existent clinician-assigned coding applied to the dataset was evaluated and the nature of notation and vocabulary used in documenting consultations was explored and described. Consultation records were pre-processed to improve human and software readability, and software was developed to redact incidental identifiers present within the free-text. An automated system able to classify for the presence of clinical signs, utilising only information present within the free-text record, was developed with the aim that it would facilitate timely detection of spatio-temporal trends in clinical signs. Clinician-assigned main reason for visit coding provided a poor summary of the large quantity of information exchanged during a veterinary consultation and the nature of the coding and questionnaire triggering further obfuscated information. Delineation of the previously undocumented veterinary clinical sublanguage identified common themes and their manner of documentation, this was key to the development of programmatic methods. A rule-based classifier using logically-chosen dictionaries, sequential processing and data-masking redacted identifiers while maintaining research usability of records. Highly sensitive and specific free-text classification was achieved by applying classifiers for individual clinical signs within a context-sensitive scaffold, this permitted or prohibited matching dependent on the clinical context in which a clinical sign was documented. The mean sensitivity achieved within an unseen test dataset was 98.17 (74.47, 99.9)% and mean specificity 99.94 (77.1, 100.0)%. When used in combination to identify animals with any of a combination of gastrointestinal clinical signs, the sensitivity achieved was 99.44% (95% CI: 98.57, 99.78)% and specificity 99.74 (95% CI: 99.62, 99.83). This work illustrates the importance, utility and promise of free-text classification of clinical records and provides a framework within which this is possible whilst respecting the confidentiality of client and clinician