Text Classification of Cancer Clinical Trial Eligibility Criteria

Automatic identification of clinical trials for which a patient is eligible is complicated by the fact that trial eligibility is stated in natural language. A potential solution to this problem is to employ text classification methods for common types of eligibility criteria. In this study, we focus on seven common exclusion criteria in cancer trials: prior malignancy, human immunodeficiency virus, hepatitis B, hepatitis C, psychiatric illness, drug/substance abuse, and autoimmune illness. Our dataset consists of 764 phase III cancer trials with these exclusions annotated at the trial level. We experiment with common transformer models as well as a new pre-trained clinical trial BERT model. Our results demonstrate the feasibility of automatically classifying common exclusion criteria. Additionally, we demonstrate the value of a pre-trained language model specifically for clinical trials, which yields the highest average performance across all criteria.Comment: AMIA Annual Symposium Proceedings 202

A data driven semantic framework for clinical trial eligibility criteria

Title from PDF of title page, viewed on January 17, 2012Thesis advisor: Deendayal DinakarpandianVitaIncludes bibliographic references (p. 90-93)Thesis (M.S.)--School of Computing and Engineering. University of Missouri--Kansas City, 2011An important step in the discovery of new treatments for medical conditions is the matching of potential subjects with appropriate clinical trials. Eligibility criteria for clinical trials are typically specified in free text as inclusion and exclusion criteria for each study. While this is sufficient for a human to guide a recruitment interview, it cannot be reliably parsed to identify potential subjects computationally. Standardizing the representation of eligibility criteria can help in increasing the efficiency and accuracy of this process. This thesis proposes a semantic framework for intelligent match matching to determine a minimal set of eligibility criteria with maximal coverage of clinical trials. In contrast to top down existing manual standardization efforts, a bottom-up data driven approach is presented that finds the canonical non-redundant representation of an arbitrary collection of clinical trial criteria set to facilitate intelligent match-making. The approach is based on semantic clustering. The methodology been validated on a corpus of 708 clinical trials related to Generalized Anxiety Disorder containing 2760 inclusion and 4871 exclusion eligibility criteria. This corpus is represented by a relatively small number of 126 inclusion clusters and 175 exclusion clusters, each of which represents a semantically distinct criterion. Internal and external validation measures provide an objective evaluation of the method. Based on the clustering, an eligibility criteria ontology has been constructed. The resulting model has been incorporated into the development of the MindTrial clinical trial recruiting system. The prototype for clinical trial recruitment illustrates the real world effectiveness of the methodology in characterizing clinical trials and subjects, and accurate matching between them.Introduction -- Related work -- Data driven model for clinical trial eligibility criteria -- Creation of mock clinical trial subject database -- Ontology creation for clinical trials -- Case study on clinical trials -- WEB interface for GAD eligibility criteria -- Validation -- Conclusion and future work -- Appendi

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