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Complaints about electronic health records, including information overload, note bloat, and alert fatigue, are frequent topics of discussion. Despite substantial effort by researchers and industry, complaints continue noting serious adverse effects on patient safety and clinician quality of life. I believe solutions are possible if we can add information to the record that explains the "why" of a patient's care, such as relationships between symptoms, physical findings, diagnostic results, differential [...]

Neural network-based representations ("embeddings") have dramatically advanced natural language processing (NLP) tasks, including clinical NLP tasks such as concept extraction. Recently, however, more advanced embedding methods and representations (eg, ELMo, BERT) have further pushed the state of the art in NLP, yet there are no common best practices for how to integrate these representations into clinical tasks. The purpose of this study, then, is to explore the space of possible [...]

The study sought to explore to what extent geolocation data has been used to study serious mental illness (SMI). SMIs such as bipolar disorder and schizophrenia are characterized by fluctuating symptoms and sudden relapse. Currently, monitoring of people with an SMI is largely done through face-to-face visits. Smartphone-based geolocation sensors create opportunities for continuous monitoring and early intervention.

Clinical genome sequencing laboratories return reports containing clinical testing results, signed by a board-certified clinical geneticist, to the ordering physician. This report is often a PDF, but can also be a paper copy or a structured data file. The reports are frequently modified and reissued due to changes in variant interpretation or clinical attributes.

In multi-label text classification, each textual document is assigned 1 or more labels. As an important task that has broad applications in biomedicine, a number of different computational methods have been proposed. Many of these methods, however, have only modest accuracy or efficiency and limited success in practical use. We propose ML-Net, a novel end-to-end deep learning framework, for multi-label classification of biomedical texts.

The 2018 National NLP Clinical Challenge (2018 n2c2) focused on the task of cohort selection for clinical trials, where participating systems were tasked with analyzing longitudinal patient records to determine if the patients met or did not meet any of the 13 selection criteria. This article describes our participation in this shared task.

Natural language processing (NLP) engines such as the clinical Text Analysis and Knowledge Extraction System are a solution for processing notes for research, but optimizing their performance for a clinical data warehouse remains a challenge. We aim to develop a high throughput NLP architecture using the clinical Text Analysis and Knowledge Extraction System and present a predictive model use case.

In this era of digitized health records, there has been a marked interest in using de-identified patient records for conducting various health related surveys. To assist in this research effort, we developed a novel clinical data representation model entitled medical knowledge-infused convolutional neural network (MKCNN), which is used for learning the clinical trial criteria eligibility status of patients to participate in cohort studies.