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

Knowledge will Propel Machine Understanding of Content: Extrapolating from Current Examples

Machine Learning has been a big success story during the AI resurgence. One particular stand out success relates to learning from a massive amount of data. In spite of early assertions of the unreasonable effectiveness of data, there is increasing recognition for utilizing knowledge whenever it is available or can be created purposefully. In this paper, we discuss the indispensable role of knowledge for deeper understanding of content where (i) large amounts of training data are unavailable, (ii) the objects to be recognized are complex, (e.g., implicit entities and highly subjective content), and (iii) applications need to use complementary or related data in multiple modalities/media. What brings us to the cusp of rapid progress is our ability to (a) create relevant and reliable knowledge and (b) carefully exploit knowledge to enhance ML/NLP techniques. Using diverse examples, we seek to foretell unprecedented progress in our ability for deeper understanding and exploitation of multimodal data and continued incorporation of knowledge in learning techniques.Comment: Pre-print of the paper accepted at 2017 IEEE/WIC/ACM International Conference on Web Intelligence (WI). arXiv admin note: substantial text overlap with arXiv:1610.0770

Information extraction from medication leaflets

Tese de mestrado integrado. Engenharia Informática e Computação. Faculdade de Engenharia. Universidade do Porto. 201

Doctor of Philosophy

dissertationManual annotation of clinical texts is often used as a method of generating reference standards that provide data for training and evaluation of Natural Language Processing (NLP) systems. Manually annotating clinical texts is time consuming, expensive, and requires considerable cognitive effort on the part of human reviewers. Furthermore, reference standards must be generated in ways that produce consistent and reliable data but must also be valid in order to adequately evaluate the performance of those systems. The amount of labeled data necessary varies depending on the level of analysis, the complexity of the clinical use case, and the methods that will be used to develop automated machine systems for information extraction and classification. Evaluating methods that potentially reduce cost, manual human workload, introduce task efficiencies, and reduce the amount of labeled data necessary to train NLP tools for specific clinical use cases are active areas of research inquiry in the clinical NLP domain. This dissertation integrates a mixed methods approach using methodologies from cognitive science and artificial intelligence with manual annotation of clinical texts. Aim 1 of this dissertation identifies factors that affect manual annotation of clinical texts. These factors are further explored by evaluating approaches that may introduce efficiencies into manual review tasks applied to two different NLP development areas - semantic annotation of clinical concepts and identification of information representing Protected Health Information (PHI) as defined by HIPAA. Both experiments integrate iv different priming mechanisms using noninteractive and machine-assisted methods. The main hypothesis for this research is that integrating pre-annotation or other machineassisted methods within manual annotation workflows will improve efficiency of manual annotation tasks without diminishing the quality of generated reference standards

Adverse Drug Event Detection, Causality Inference, Patient Communication and Translational Research

Adverse drug events (ADEs) are injuries resulting from a medical intervention related to a drug. ADEs are responsible for nearly 20% of all the adverse events that occur in hospitalized patients. ADEs have been shown to increase the cost of health care and the length of stays in hospital. Therefore, detecting and preventing ADEs for pharmacovigilance is an important task that can improve the quality of health care and reduce the cost in a hospital setting. In this dissertation, we focus on the development of ADEtector, a system that identifies ADEs and medication information from electronic medical records and the FDA Adverse Event Reporting System reports. The ADEtector system employs novel natural language processing approaches for ADE detection and provides a user interface to display ADE information. The ADEtector employs machine learning techniques to automatically processes the narrative text and identify the adverse event (AE) and medication entities that appear in that narrative text. The system will analyze the entities recognized to infer the causal relation that exists between AEs and medications by automating the elements of Naranjo score using knowledge and rule based approaches. The Naranjo Adverse Drug Reaction Probability Scale is a validated tool for finding the causality of a drug induced adverse event or ADE. The scale calculates the likelihood of an adverse event related to drugs based on a list of weighted questions. The ADEtector also presents the user with evidence for ADEs by extracting figures that contain ADE related information from biomedical literature. A brief summary is generated for each of the figures that are extracted to help users better comprehend the figure. This will further enhance the user experience in understanding the ADE information better. The ADEtector also helps patients better understand the narrative text by recognizing complex medical jargon and abbreviations that appear in the text and providing definitions and explanations for them from external knowledge resources. This system could help clinicians and researchers in discovering novel ADEs and drug relations and also hypothesize new research questions within the ADE domain

Ontology-Based Clinical Information Extraction Using SNOMED CT

Extracting and encoding clinical information captured in unstructured clinical documents with standard medical terminologies is vital to enable secondary use of clinical data from practice. SNOMED CT is the most comprehensive medical ontology with broad types of concepts and detailed relationships and it has been widely used for many clinical applications. However, few studies have investigated the use of SNOMED CT in clinical information extraction. In this dissertation research, we developed a fine-grained information model based on the SNOMED CT and built novel information extraction systems to recognize clinical entities and identify their relations, as well as to encode them to SNOMED CT concepts. Our evaluation shows that such ontology-based information extraction systems using SNOMED CT could achieve state-of-the-art performance, indicating its potential in clinical natural language processing