Clinical Tractor: A Framework for Automatic Natural Language Understanding of Clinical Practice Guidelines.

Computational representations of the semantic knowledge embedded within clinical practice guidelines (CPGs) may be a significant aid in creating computer interpretable guidelines (CIGs). Formalizing plain text CPGs into CIGs manually is a laborious and burdensome task, even using CIG tools and languages designed to improve the process. Natural language understanding (NLU) systems perform automated reading comprehension, parsing text and using reasoning to convert syntactic information from unstructured text into semantic information. Influenced by successful systems used in other domains, we present the architecture for a system which uses NLU approaches to create semantic representations of entire CPGs. In the future, these representations may be used to generate CIGs.

Comparison of Changes in the Number of Included Patients Between Interventional Trials and Observational Studies Published from 1995 to 2014 in Three Leading Journals.

INTRODUCTION: Since the late 1990s, research and administrative institutions have been developing health data warehouses and increasingly reusing claims data. The impact of these changes is not yet completely quantified. Our objective was to compare the change in the number of patients included per study between observational and interventional studies over a 20-year period starting in 1995. MATERIALS AND METHODS: We extracted all abstracts from studies published in three leading medical journals over the period 1995-2014 (18,107 studies). Then, we divided our study into two steps. First, we constructed an SVM-based predictive model to categorize each abstract into "observational", "interventional" or "other" studies. In a second step, we built an algorithm based on regular expressions to automatically extract the number of included patients. RESULTS: During the investigated period, the median number of enrolled patients per study increased for interventional studies, from 282 in 1995-1999 to 629 in 2010-2014. In the same time, the median number of patients increased more for observational studies, from 368 in 1995-1999 to 2078 in 2010-2014. DISCUSSION: The routine storage of an increasing amount of data (from data warehouses or claims data) has had an impact in recent years on the number of patients included in observational studies. The recent development of "randomized registry trials" combining, on the one hand, an intervention and, on the other hand, the identification of the outcome through data reuse, may also have an impact, over the next decade, on the number of patients included in randomized clinical trials.

Artificial Intelligence: Bayesian versus Heuristic Method for Diagnostic Decision Support.

Evoking strength is one of the important contributions of the field of Biomedical Informatics to the discipline of Artificial Intelligence. The University at Buffalo's Orthopedics Department wanted to create an expert system to assist patients with self-diagnosis of knee problems and to thereby facilitate referral to the right orthopedic subspecialist. They had two independent sports medicine physicians review 469 cases. A board-certified orthopedic sports medicine practitioner, L.B., reviewed any disagreements until a gold standard diagnosis was reached. For each case, the patients entered 126 potential answers to 26 questions into a Web interface. These were modeled by an expert sports medicine physician and the answers were reviewed by L.B. For each finding, the clinician specified the sensitivity (term frequency) and both specificity (Sp) and the heuristic evoking strength (ES). Heuristics are methods of reasoning with only partial evidence. An expert system was constructed that reflected the posttest odds of disease-ranked list for each case. We compare the accuracy of using Sp to that of using ES (original model, p < 0.0008; term importance * disease importance [DItimesTI] model, p < 0.0001: Wilcoxon ranked sum test). For patient referral assignment, Sp in the DItimesTI model was superior to the use of ES. By the fifth diagnosis, the advantage was lost and so there is no difference between the techniques when serving as a reminder system.

HTP-NLP: A New NLP System for High Throughput Phenotyping.

Secondary use of clinical data for research requires a method to quickly process the data so that researchers can quickly extract cohorts. We present two advances in the High Throughput Phenotyping NLP system which support the aim of truly high throughput processing of clinical data, inspired by a characterization of the linguistic properties of such data. Semantic indexing to store and generalize partially-processed results and the use of compositional expressions for ungrammatical text are discussed, along with a set of initial timing results for the system.

Automatically Expanding the Synonym Set of SNOMED CT using Wikipedia.

Clinical terminologies and ontologies are often used in natural language processing/understanding tasks as a method for semantically tagging text. One ontology commonly used for this task is SNOMED CT. Natural language is rich and varied: many different combinations of words may be used to express the same idea. It is therefore essential that ontologies and terminologies have a rich set of synonyms. One source of synonyms is Wikipedia. We examine methods for aligning concepts in SNOMED CT with articles in Wikipedia so that newly-found synonyms may be added to SNOMED CT. Our experiments show promising results and provide guidance to researchers who wish to use Wikipedia for similar tasks.