A Corpus for Evidence Based Medicine Summarisation

Background Automated text summarisers that find the best clinical evidence reported in collections of medical literature are of potential benefit for the practice of Evidence Based Medicine (EBM). Research and development of text summarisers for EBM, however, is impeded by the lack of corpora to train and test such systems. Aims To produce a corpus for research in EBM summarisation. Method We sourced the “Clinical Inquiries” section of the Journal of Family Practice (JFP) and obtained a sizeable sample of questions and evidence based summaries. We further processed the summaries by combining automated techniques, human annotations, and crowdsourcing techniques to identify the PubMed IDs of the references. Results The corpus has 456 questions, 1,396 answer components, 3,036 answer justifications, and 2,908 references. Conclusion The corpus is now available for the research community at http://sourceforge.net/projects/ebmsumcorpus

Extractive Summarisation of Medical Documents

Background Evidence Based Medicine (EBM) practice requires practitioners to extract evidence from published medical research when answering clinical queries. Due to the time-consuming nature of this practice, there is a strong motivation for systems that can automatically summarise medical documents and help practitioners find relevant information. Aim The aim of this work is to propose an automatic query-focused, extractive summarisation approach that selects informative sentences from medical documents. MethodWe use a corpus that is specifically designed for summarisation in the EBM domain. We use approximately half the corpus for deriving important statistics associated with the best possible extractive summaries. We take into account factors such as sentence position, length, sentence content, and the type of the query posed. Using the statistics from the first set, we evaluate our approach on a separate set. Evaluation of the qualities of the generated summaries is performed automatically using ROUGE, which is a popular tool for evaluating automatic summaries. Results Our summarisation approach outperforms all baselines (best baseline score: 0.1594; our score 0.1653). Further improvements are achieved when query types are taken into account. Conclusion The quality of extractive summarisation in the medical domain can be significantly improved by incorporating domain knowledge and statistics derived from a specialised corpus. Such techniques can therefore be applied for content selection in end-to-end summarisation systems

Extractive Summarisation of Medical Documents

Background Evidence Based Medicine (EBM) practice requires practitioners to extract evidence from published medical research when answering clinical queries. Due to the time-consuming nature of this practice, there is a strong motivation for systems that can automatically summarise medical documents and help practitioners find relevant information. Aim The aim of this work is to propose an automatic query-focused, extractive summarisation approach that selects informative sentences from medical documents. Method We use a corpus that is specifically designed for summarisation in the EBM domain. We use approximately half the corpus for deriving important statistics associated with the best possible extractive summaries. We take into account factors such as sentence position, length, sentence content, and the type of the query posed. Using the statistics from the first set, we evaluate our approach on a separate set. Evaluation of the qualities of the generated summaries is performed automatically using ROUGE, which is a popular tool for evaluating automatic summaries. Results Our summarisation approach outperforms all baselines (best baseline score: 0.1594; our score 0.1653). Further improvements are achieved when query types are taken into account. Conclusion The quality of extractive summarisation in the medical domain can be significantly improved by incorporating domain knowledge and statistics derived from a specialised corpus. Such techniques can therefore be applied for content selection in end-to-end summarisation systems

Methods and Applications for Summarising Free-Text Narratives in Electronic Health Records

As medical services move towards electronic health record (EHR) systems the breadth and depth of data stored at each patient encounter has increased. This growing wealth of data and investment in care systems has arguably put greater strain on services, as those at the forefront are pushed towards greater time spent in front of computers over their patients. To minimise the use of EHR systems clinicians often revert to using free-text data entry to circumvent the structured input fields. It has been estimated that approximately 80% of EHR data is within the free-text portion. Outside of their primary use, that is facilitating the direct care of the patient, secondary use of EHR data includes clinical research, clinical audits, service improvement research, population health analysis, disease and patient phenotyping, clinical trial recruitment to name but a few.This thesis presents a number of projects, previously published and original work in the development, assessment and application of summarisation methods for EHR free-text. Firstly, I introduce, define and motivate EHR free-text analysis and summarisation methods of open-domain text and how this compares to EHR free-text. I then introduce a subproblem in natural language processing (NLP) that is the recognition of named entities and linking of the entities to pre-existing clinical knowledge bases (NER+L). This leads to the first novel contribution the Medical Concept Annotation Toolkit (MedCAT) that provides a software library workflow for clinical NER+L problems. I frame the outputs of MedCAT as a form of summarisation by showing the tools contributing to published clinical research and the application of this to another clinical summarisation use-case ‘clinical coding’. I then consider methods for the textual summarisation of portions of clinical free-text. I show how redundancy in clinical text is empirically different to open-domain text discussing how this impacts text-to-text summarisation. I then compare methods to generate discharge summary sections from previous clinical notes using methods presented in prior chapters via a novel ‘guidance’ approach.I close the thesis by discussing my contributions in the context of state-of-the-art and how my work fits into the wider body of clinical NLP research. I briefly describe the challenges encountered throughout, offer my perspectives on the key enablers of clinical informatics research, and finally the potential future work that will go towards translating research impact to real-world benefits to healthcare systems, workers and patients alike

Knowledge representation and text mining in biomedical, healthcare, and political domains

Knowledge representation and text mining can be employed to discover new knowledge and develop services by using the massive amounts of text gathered by modern information systems. The applied methods should take into account the domain-specific nature of knowledge. This thesis explores knowledge representation and text mining in three application domains. Biomolecular events can be described very precisely and concisely with appropriate representation schemes. Protein–protein interactions are commonly modelled in biological databases as binary relationships, whereas the complex relationships used in text mining are rich in information. The experimental results of this thesis show that complex relationships can be reduced to binary relationships and that it is possible to reconstruct complex relationships from mixtures of linguistically similar relationships. This encourages the extraction of complex relationships from the scientific literature even if binary relationships are required by the application at hand. The experimental results on cross-validation schemes for pair-input data help to understand how existing knowledge regarding dependent instances (such those concerning protein–protein pairs) can be leveraged to improve the generalisation performance estimates of learned models. Healthcare documents and news articles contain knowledge that is more difficult to model than biomolecular events and tend to have larger vocabularies than biomedical scientific articles. This thesis describes an ontology that models patient education documents and their content in order to improve the availability and quality of such documents. The experimental results of this thesis also show that the Recall-Oriented Understudy for Gisting Evaluation measures are a viable option for the automatic evaluation of textual patient record summarisation methods and that the area under the receiver operating characteristic curve can be used in a large-scale sentiment analysis. The sentiment analysis of Reuters news corpora suggests that the Western mainstream media portrays China negatively in politics-related articles but not in general, which provides new evidence to consider in the debate over the image of China in the Western media