Mining clinical relationships from patient narratives

Background The Clinical E-Science Framework (CLEF) project has built a system to extract clinically significant information from the textual component of medical records in order to support clinical research, evidence-based healthcare and genotype-meets-phenotype informatics. One part of this system is the identification of relationships between clinically important entities in the text. Typical approaches to relationship extraction in this domain have used full parses, domain-specific grammars, and large knowledge bases encoding domain knowledge. In other areas of biomedical NLP, statistical machine learning (ML) approaches are now routinely applied to relationship extraction. We report on the novel application of these statistical techniques to the extraction of clinical relationships. Results We have designed and implemented an ML-based system for relation extraction, using support vector machines, and trained and tested it on a corpus of oncology narratives hand-annotated with clinically important relationships. Over a class of seven relation types, the system achieves an average F1 score of 72%, only slightly behind an indicative measure of human inter annotator agreement on the same task. We investigate the effectiveness of different features for this task, how extraction performance varies between inter- and intra-sentential relationships, and examine the amount of training data needed to learn various relationships. Conclusion We have shown that it is possible to extract important clinical relationships from text, using supervised statistical ML techniques, at levels of accuracy approaching those of human annotators. Given the importance of relation extraction as an enabling technology for text mining and given also the ready adaptability of systems based on our supervised learning approach to other clinical relationship extraction tasks, this result has significance for clinical text mining more generally, though further work to confirm our encouraging results should be carried out on a larger sample of narratives and relationship types

SPEECH TO CHART: SPEECH RECOGNITION AND NATURAL LANGUAGE PROCESSING FOR DENTAL CHARTING

Typically, when using practice management systems (PMS), dentists perform data entry by utilizing an assistant as a transcriptionist. This prevents dentists from interacting directly with the PMSs. Speech recognition interfaces can provide the solution to this problem. Existing speech interfaces of PMSs are cumbersome and poorly designed. In dentistry, there is a desire and need for a usable natural language interface for clinical data entry. Objectives. (1) evaluate the efficiency, effectiveness, and user satisfaction of the speech interfaces of four dental PMSs, (2) develop and evaluate a speech-to-chart prototype for charting naturally spoken dental exams. Methods. We evaluated the speech interfaces of four leading PMSs. We manually reviewed the capabilities of each system and then had 18 dental students chart 18 findings via speech in each of the systems. We measured time, errors, and user satisfaction. Next, we developed and evaluated a speech-to-chart prototype which contained the following components: speech recognizer; post-processor for error correction; NLP application (ONYX) and; graphical chart generator. We evaluated the accuracy of the speech recognizer and the post-processor. We then performed a summative evaluation on the entire system. Our prototype charted 12 hard tissue exams. We compared the charted exams to reference standard exams charted by two dentists. Results. Of the four systems, only two allowed both hard tissue and periodontal charting via speech. All interfaces required using specific commands directly comparable to using a mouse. The average time to chart the nine hard tissue findings was 2:48 and the nine periodontal findings was 2:06. There was an average of 7.5 errors per exam. We created a speech-to-chart prototype that supports natural dictation with no structured commands. On manually transcribed exams, the system performed with an average 80% accuracy. The average time to chart a single hard tissue finding with the prototype was 7.3 seconds. An improved discourse processor will greatly enhance the prototype's accuracy. Conclusions. The speech interfaces of existing PMSs are cumbersome, require using specific speech commands, and make several errors per exam. We successfully created a speech-to-chart prototype that charts hard tissue findings from naturally spoken dental exams

Enhancing the interactivity of a clinical decision support system by using knowledge engineering and natural language processing

Mental illness is a serious health problem and it affects many people. Increasingly,Clinical Decision Support Systems (CDSS) are being used for diagnosis and it is important to improve the reliability and performance of these systems. Missing a potential clue or a wrong diagnosis can have a detrimental effect on the patient's quality of life and could lead to a fatal outcome. The context of this research is the Galatean Risk and Safety Tool (GRiST), a mental-health-risk assessment system. Previous research has shown that success of a CDSS depends on its ease of use, reliability and interactivity. This research addresses these concerns for the GRiST by deploying data mining techniques. Clinical narratives and numerical data have both been analysed for this purpose.Clinical narratives have been processed by natural language processing (NLP)technology to extract knowledge from them. SNOMED-CT was used as a reference ontology and the performance of the different extraction algorithms have been compared. A new Ensemble Concept Mining (ECM) method has been proposed, which may eliminate the need for domain specific phrase annotation requirements. Word embedding has been used to filter phrases semantically and to build a semantic representation of each of the GRiST ontology nodes.The Chi-square and FP-growth methods have been used to find relationships between GRiST ontology nodes. Interesting patterns have been found that could be used to provide real-time feedback to clinicians. Information gain has been used efficaciously to explain the differences between the clinicians and the consensus risk. A new risk management strategy has been explored by analysing repeat assessments. A few novel methods have been proposed to perform automatic background analysis of the patient data and improve the interactivity and reliability of GRiST and similar systems