Knowledge-based best of breed approach for automated detection of clinical events based on German free text digital hospital discharge letters

OBJECTIVES: The secondary use of medical data contained in electronic medical records, such as hospital discharge letters, is a valuable resource for the improvement of clinical care (e.g. in terms of medication safety) or for research purposes. However, the automated processing and analysis of medical free text still poses a huge challenge to available natural language processing (NLP) systems. The aim of this study was to implement a knowledge-based best of breed approach, combining a terminology server with integrated ontology, a NLP pipeline and a rules engine. METHODS: We tested the performance of this approach in a use case. The clinical event of interest was the particular drug-disease interaction "proton-pump inhibitor [PPI] use and osteoporosis". Cases were to be identified based on free text digital discharge letters as source of information. Automated detection was validated against a gold standard. RESULTS: Precision of recognition of osteoporosis was 94.19%, and recall was 97.45%. PPIs were detected with 100% precision and 97.97% recall. The F-score for the detection of the given drug-disease-interaction was 96,13%. CONCLUSION: We could show that our approach of combining a NLP pipeline, a terminology server, and a rules engine for the purpose of automated detection of clinical events such as drug-disease interactions from free text digital hospital discharge letters was effective. There is huge potential for the implementation in clinical and research contexts, as this approach enables analyses of very high numbers of medical free text documents within a short time period

Survey of the State of the Art in Natural Language Generation: Core tasks, applications and evaluation

This paper surveys the current state of the art in Natural Language Generation (NLG), defined as the task of generating text or speech from non-linguistic input. A survey of NLG is timely in view of the changes that the field has undergone over the past decade or so, especially in relation to new (usually data-driven) methods, as well as new applications of NLG technology. This survey therefore aims to (a) give an up-to-date synthesis of research on the core tasks in NLG and the architectures adopted in which such tasks are organised; (b) highlight a number of relatively recent research topics that have arisen partly as a result of growing synergies between NLG and other areas of artificial intelligence; (c) draw attention to the challenges in NLG evaluation, relating them to similar challenges faced in other areas of Natural Language Processing, with an emphasis on different evaluation methods and the relationships between them.Comment: Published in Journal of AI Research (JAIR), volume 61, pp 75-170. 118 pages, 8 figures, 1 tabl

Regenerative medicine: Stroke survivor and carer views and motivations towards a proposed stem cell clinical trial using placebo neurosurgery

Background  Few studies explore stroke survivor views and motivations towards stem cell therapy (SCT). This qualitative study explores the views and motivations of both stroke survivors and their partners/carers towards a proposed 2-arm Phase III Randomised Controlled Trial (RCT) comparing intracerebral insertion of stem cells with placebo neurosurgery in stroke survivors with disability.  Objective  To explore views and motivations towards a proposed 2-arm stem cell trial and identify factors that may impede and enhance participation.  Design  This study adopts a naturalistic design to explore the complexity of this field, employing a participatory action-research approach comprising a specialized Conversation (World) Café form of focus group. Data were collected via 5 Conversation Cafés with stroke survivors (age 40-75) and partners/carers between June and October 2016. Of 66 participants, 53 (31 male, 22 female) were stroke survivors and 13 (6 female, 7 male) were partners/carers. Qualitative data were analysed using a thematic approach.  Discussion and Conclusion  Stroke survivor views and motivations reflect anticipation of the personal and future benefits of regenerative medicine. Partners/carers sought to balance the value of stroke survivor hope with carrying the weight of hope as carer, a conflict burden adding to known caregiver burden. All participants expressed the need for during and post-trial psychological support. This study provides a rare opportunity to explore the prospective views and motivations of stroke survivors and their partners/carers towards a proposed Phase III 2-arm RCT. This adds weight to qualitative evidence exploring capacity, consent, decision making, perceptions of treatment risk and supports required for clinical trial participation

Natural Language Processing of Clinical Notes on Chronic Diseases: Systematic Review

Novel approaches that complement and go beyond evidence-based medicine are required in the domain of chronic diseases, given the growing incidence of such conditions on the worldwide population. A promising avenue is the secondary use of electronic health records (EHRs), where patient data are analyzed to conduct clinical and translational research. Methods based on machine learning to process EHRs are resulting in improved understanding of patient clinical trajectories and chronic disease risk prediction, creating a unique opportunity to derive previously unknown clinical insights. However, a wealth of clinical histories remains locked behind clinical narratives in free-form text. Consequently, unlocking the full potential of EHR data is contingent on the development of natural language processing (NLP) methods to automatically transform clinical text into structured clinical data that can guide clinical decisions and potentially delay or prevent disease onset