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Population-level prevention activities are often publicly invisible and excluded in planning and policymaking. This creates an incomplete picture of prevention service-related inputs, particularly at the local level. We describe the process and lessons learned by the Public Health Activities and Services Tracking team in promoting adoption of standardized service delivery measures developed to assess public health inputs and guide system transformations. The 3 factors depicted in our Public Health Activities [...]

We describe the use of an online patient portal to recruit and enroll primary care patients in a randomized trial testing the effectiveness of a colorectal cancer (CRC) screening decision support program. We use multiple logistic regression to identify patient characteristics associated with trial recruitment, enrollment, and engagement. We found that compared to Whites, Blacks had lower odds of viewing the portal message (OR = 0.46, 95% CI = 0.37-0.57), opening the attached link [...]

Mobile health (mHealth) interventions have demonstrated promise in improving outcomes by motivating patients to adopt and maintain healthy lifestyle changes as well as improve adherence to guideline-directed medical therapy. Early results combining behavioral economic strategies with mHealth delivery have demonstrated mixed results. In reviewing these studies, we propose that the success of a mHealth intervention links more strongly with how well it connects patients back to routine clinical care, rather [...]

We assessed whether machine learning can be utilized to allow efficient extraction of infectious disease activity information from online media reports.

Active Learning (AL) attempts to reduce annotation cost (ie, time) by selecting the most informative examples for annotation. Most approaches tacitly (and unrealistically) assume that the cost for annotating each sample is identical. This study introduces a cost-aware AL method, which simultaneously models both the annotation cost and the informativeness of the samples and evaluates both via simulation and user studies.

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.

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.