Problematic opioid use is a major crisis, especially among Veterans of the U.S. Military. In previous work we developed a natural language processing tool to identify problematic opioid use in Veterans Affairs clinical notes. In this work, we developed an application that identifies clinical notes associated with patients who were documented as experiencing problematic opioid use only in clinical notes, as compared to patients who had received a relevant ICD code for problematic opioid use. The application used topic and topic word output from topic modeling to train deep neural network models. The models performed well, achieving area under the curve values of 82% or above, exceeding the performance of baseline models using only topics or topic words. We also computed impact scores, an explainable artificial intelligence method that identifies critical features in training deep learning models. The impact scores extended additional understanding to the data and outcomes.

The field of clinical natural language processing (NLP) has been built on the analysis of clinical sublanguage characteristics. It is well recognized that not only does clinical sublanguage differ from general English (or other languages) but also clinical sublanguage differs among clinical subspecialties and among corpora originating from different healthcare systems. A less recognized aspect is that clinical sublanguage, like all languages, evolves over time. This paper analyses the evolution of clinical sublanguage using a large, national clinical text corpus spanning 15 years. Through the analyses of document types, length, ngrams, and concepts, we found strong evidence that clinical sublanguage does evolve and such changes have implications for NLP development and maintenance. Although the analysis is performed on one corpus, our observations of sublanguage changes are generalizable.

LGBT Patients bear a disproportional burden of health disparities. Sexual orientation and gender identity is of clinical relevance to healthcare providers and data scientists. However, little work has been done to identify LGBT patients, especially in data derived from electronic health record notes. We developed a prototype application that leverages machine learning and rule-based pattern matching methods to identify LGBT patients in a large data source, Veterans Health Administration electronic health record notes. This application achieved 88.2% sensitivity, 91.5% specificity, and 85.9% positive predictive value in a binary classification task for three random document test sets. This work has implications in both improved healthcare and data research.

Irregular spellings in clinical free text present challenges to natural language processing. A number of spelling correction tools exist, but automated spelling correction of clinical text is not a routine practice due to the risk of introducing new errors. We developed a novel spelling analysis application that combines Word2Vec and Levenshtein Edit Distance Constraints to identify variant forms of words. The use case applied to this study was that of discovering herbal and dietary supplements that interact with prescription medications in clinical text. The prototype application processed a large corpus (approximately 1.6 million records), achieving a positive predictive value of 0.9322, in identifying spelling variants, outperforming two baseline methods that achieved positive predictive values of 0.0348 and 0.0067. Our findings suggest that this prototype application provides a more efficient method for researchers and clinicians to find valid misspellings of terms in clinical text.

Sublanguages are domain-centered subsets of general or colloquial language. Their identification drives several language analysis tasks, but it is difficult to discern separate sublanguages in large clinical corpora. We applied k-means clustering of semantic properties, and a novel implementation of relative entropy as an information gain indicator, to identify sublanguages within a large clinical corpus (~1.6 million documents), visualizing the results in a heat map. Patterns both within and across clusters reveal sublanguage trends. These findings are significant in sublanguage analysis, and have implications on both regional and international levels.

Opioid Use Disorder is an international crisis, affecting many populations. Deep learning models can potentially predict opioid use disorder, but provide little insight to how predictions are derived. Impact scores, a new development in explainable artificial intelligence, measure how individual features affect deep learning outcomes. We modeled clinical visits to predict opioid use disorder, computed impact scores, and compared them to odds log ratios from logistic regression. Impact scores were generally comparable to odds log ratios, in providing insight to opioid abuse risk, but from a better-performing method than logistic regression.

Analysis of clinical temporal data can be difficult due to natural properties that often characterize it. The large number of variables, missing values, and other characteristics lead to issues of sparsity and high dimensional complexity. We hypothesized that a pipeline application implementing relevant deep learning methods could sequentially address these difficulties, demonstrating their combined utility in a classification task. We implemented Word2Vec, t-distributed stochastic neighbor embedding, and a convolutional neural network in a pipeline application. To test the pipeline, we applied it to a simple, binary classification task to identify patient encounter care setting. In preliminary testing, the pipeline application achieved 92% accuracy. It also produced temporal data cubes indicative of clinical encounters in intensive care unit (ICU) and non-ICU care settings. A deep learning pipeline process combining multiple methods holds promise in improving analytical tasks of clinical temporal data.

Several researchers have studied serendipitous knowledge discovery in information‐seeking behavior. Electronic data in the form of semantic predications have a potential role in literature‐based discovery, which can be guided by serendipitous knowledge discovery research findings. We sought to model information‐seeking behavior within the context of serendipitous knowledge discovery by leveraging existing research. These efforts were done with an eye for a potential literature‐based discovery application that utilizes semantic predications. We performed a literature search, reviewed the results, and applied the findings in developing a model for serendipitous knowledge discovery as an information‐seeking behavior. The literature review indicated four important themes in serendipitous knowledge discovery: iteration, change or clarification, a seeker's prior knowledge, and the role of information organization and presentation. The Interaction Flow in Serendipitous Knowledge Discovery (IF‐SKD) model includes these themes, and accommodates iterative, evolving search interests. Output can be presented in a manner to enhance short‐term memory conceptualization and connections with prior knowledge. Although the IF‐SKD model is currently a theoretical structure, its utility is demonstrated through replicating a literature‐based discovery event, using a documented search method within the model's steps. The IF‐SKD model can potentially serve as the foundation for future literature‐based discovery applications.