Building clinical registries is an important step in improving the quality and safety of patient care. With the growing size of medical records, manual abstraction becomes more and more infeasible and impractical. On the other hand, Natural Language Processing Techniques have shown promising results in extracting valuable information from unstructured clinical notes. However, the structure and nature of clinical notes are very different from regular text that state-of-the- art NLP models are trained and tested on and they have their own set of challenges. In this study, we propose SE-K, an efficient and interpretable classification approach for extracting information from clinical notes, and show that it outperforms current state-of-the-art models in text classification. We use this approach to generate a 20- year comprehensive registry of anterior cruciate ligament reconstruction operations, one of the most common orthopedics operations among children and young adults. This registry can help us better understand the outcomes of this surgery and identify potential areas for improvement which can ultimately lead to better treatment outcomes.

The legalizations of medical and recreational cannabis have generated a great deal of interest in studying the health impacts of cannabis products. Despite increases in cannabis use, its documentation during clinical visits is not yet mainstream. This lack of information hampers efforts to study cannabis effects on health outcomes. A clear and in-depth understanding of current trends in cannabis use documentation is necessary to develop proper guidelines to screen and document cannabis use. Here we have developed and used a hierarchical natural language processing pipeline (AUROC=0.94) to evaluate the trends and disparities in cannabis documentation on more than 23 million notes from a large cohort of 370,087 patients seen in a high-volume multi-site pediatric and young adult clinic over a period of 21 years. Our findings show a very low but growing rate of cannabis use documentation (<2%) in electronic health records with significant demographic and socioeconomic disparities in both documentation and use, which requires further attention.

The legalizations of medical and recreational cannabis have generated a great deal of interest in studying the health impacts of cannabis products. Despite increases in cannabis use, its documentation during clinical visits is not yet mainstream. This lack of information hampers efforts to study cannabis effects on health outcomes. A clear and in-depth understanding of current trends in cannabis use documentation is necessary to develop proper guidelines to screen and document cannabis use. Here we have developed and used a hierarchical natural language processing pipeline (AUROC=0.94) to evaluate the trends and disparities in cannabis documentation on more than 23 million notes from a large cohort of 370,087 patients seen in a high-volume multi-site pediatric and young adult clinic over a period of 21 years. Our findings show a very low but growing rate of cannabis use documentation (<2%) in electronic health records with significant demographic and socioeconomic disparities in both documentation and use, which requires further attention.

The legalizations of medical and recreational cannabis have generated a great deal of interest in studying the health impacts of cannabis products. Despite increases in cannabis use, its documentation during clinical visits is not yet mainstream. This lack of information hampers efforts to study cannabis’s effects on health outcomes. A clear and in-depth understanding of current trends in cannabis use documentation is necessary to develop proper guidelines to screen and document cannabis use. Here we have developed and used a natural language processing pipeline to evaluate the trends and disparities in cannabis documentation. The pipeline includes a screening step to identify clinical notes with cannabis use documentation which is then fed into a BERT-based classifier to confirm positive use. This pipeline is applied to more than 23 million notes from a large cohort of 370,087 patients seen in a high-volume multi-site pediatric and young adult clinic over a period of 21 years. Our findings show a very low but growing rate of cannabis use documentation (<2%) in electronic health records with significant demographic and socioeconomic disparities in both documentation and positive use, which requires further attention.

Current empirical and semi-empirical based design manuals are restricted to the analysis of simple building configurations against blast loading. Prediction of blast loads for complex geometries is typically carried out with computational fluid dynamics solvers, which are known for their high computational cost. The combination of high-fidelity simulations with machine learning tools may significantly accelerate processing time, but the efficacy of such tools must be investigated. The present study evaluates various machine learning algorithms to predict peak overpressure and impulse on a protruded structure exposed to blast loading. A dataset with over 250,000 data points extracted from ProSAir simulations is used to train, validate, and test the models. Among the machine learning algorithms, gradient boosting models outperformed neural networks, demonstrating high predictive power. These models required significantly less time for hyperparameter optimization, and the randomized search approach achieved relatively similar results to that of grid search. Based on permutation feature importance studies, the protrusion length was considered a significantly more influential parameter in the construction of decision trees than building height.