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Cohort selection for clinical trials is a key step for clinical research. We proposed a hierarchical neural network to determine whether a patient satisfied selection criteria or not.

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.

Our objective is to develop algorithms for encoding clinical text into representations that can be used for a variety of phenotyping tasks.

Case management programs for high-need high-cost patients are spreading rapidly among health systems. PCORNet has substantial potential to support learning health systems in rapidly evaluating these programs, but access to complete patient data on health care utilization is limited as PCORNet is based on electronic health records not health insurance claims data. Because matching cases to comparison patients on baseline utilization is often a critical component of high-quality observational comparative [...]

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.

HIV infection risk can be estimated based on not only individual features but also social network information. However, there have been insufficient studies using n machine learning methods that can maximize the utility of such information. Leveraging a state-of-the-art network topology modeling method, graph convolutional networks (GCN), our main objective was to include network information for the task of detecting previously unknown HIV infections.

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.

The study sought to present the findings of a systematic review of studies involving secondary analyses of data coded with standardized nursing terminologies (SNTs) retrieved from electronic health records (EHRs).

Clinical trials, prospective research studies on human participants carried out by a distributed team of clinical investigators, play a crucial role in the development of new treatments in health care. This is a complex and expensive process where investigators aim to enroll volunteers with predetermined characteristics, administer treatment(s), and collect safety and efficacy data. Therefore, choosing top-enrolling investigators is essential for efficient clinical trial execution and is 1 of the [...]