Impact of community-based technology training with low-income older adults.

OBJECTIVES: Compared with younger and middle-aged adults, older adults are less likely to adopt new computer technology, potentially limiting access to healthcare and many other important resources available online. This limitation could impact cognitive abilities, well-being, and mental health outcomes of older adults. The aims of the present study were to increase access to online county and healthcare resources, while also assessing the impact of technology access on cognitive functioning and multiple well-being domains. METHODS: A pilot community collaboration provided a two-month tablet training intervention, focused on increasing digital independence via tablet navigation, resources access, and fraud and scam prevention, to 20 low-income older adult participants (75% female, Mage = 70.85). Pre- and post-test phone interviews were conducted to measure any changes in digital independence, cognitive abilities, well-being, mental health, and mindset. RESULTS: Linear mixed effects models revealed no significant changes in outcome measures from pre- to post-test. However, we found effects of digital independence on several well-being measures, providing important information for the impact of technology access and training for low-income older adults. CONCLUSION: This pilot intervention offers limited but promising results, inspiring further investigations that may inform public health and policy services to address barriers to access and potentially improve psychological health.

Ambulatory Risk Models for the Long-Term Prevention of Sepsis: Retrospective Study.

BACKGROUND: Sepsis is a life-threatening condition that can rapidly lead to organ damage and death. Existing risk scores predict outcomes for patients who have already become acutely ill. OBJECTIVE: We aimed to develop a model for identifying patients at risk of getting sepsis within 2 years in order to support the reduction of sepsis morbidity and mortality. METHODS: Machine learning was applied to 2,683,049 electronic health records (EHRs) with over 64 million encounters across five states to develop models for predicting a patient's risk of getting sepsis within 2 years. Features were selected to be easily obtainable from a patient's chart in real time during ambulatory encounters. RESULTS: The models showed consistent prediction scores, with the highest area under the receiver operating characteristic curve of 0.82 and a positive likelihood ratio of 2.9 achieved with gradient boosting on all features combined. Predictive features included age, sex, ethnicity, average ambulatory heart rate, standard deviation of BMI, and the number of prior medical conditions and procedures. The findings identified both known and potential new risk factors for long-term sepsis. Model variations also illustrated trade-offs between incrementally higher accuracy, implementability, and interpretability. CONCLUSIONS: Accurate implementable models were developed to predict the 2-year risk of sepsis, using EHR data that is easy to obtain from ambulatory encounters. These results help advance the understanding of sepsis and provide a foundation for future trials of risk-informed preventive care.

Sex, obesity, diabetes, and exposure to particulate matter among patients with severe asthma: Scientific insights from a comparative analysis of open clinical data sources during a five-day hackathon.

This special communication describes activities, products, and lessons learned from a recent hackathon that was funded by the National Center for Advancing Translational Sciences via the Biomedical Data Translator program ('Translator'). Specifically, Translator team members self-organized and worked together to conceptualize and execute, over a five-day period, a multi-institutional clinical research study that aimed to examine, using open clinical data sources, relationships between sex, obesity, diabetes, and exposure to airborne fine particulate matter among patients with severe asthma. The goal was to develop a proof of concept that this new model of collaboration and data sharing could effectively produce meaningful scientific results and generate new scientific hypotheses. Three Translator Clinical Knowledge Sources, each of which provides open access (via Application Programming Interfaces) to data derived from the electronic health record systems of major academic institutions, served as the source of study data. Jupyter Python notebooks, shared in GitHub repositories, were used to call the knowledge sources and analyze and integrate the results. The results replicated established or suspected relationships between sex, obesity, diabetes, exposure to airborne fine particulate matter, and severe asthma. In addition, the results demonstrated specific differences across the three Translator Clinical Knowledge Sources, suggesting cohort- and/or environment-specific factors related to the services themselves or the catchment area from which each service derives patient data. Collectively, this special communication demonstrates the power and utility of intense, team-oriented hackathons and offers general technical, organizational, and scientific lessons learned.

Machine Learning of Spatiotemporal Bursting Behavior in Developing Neural Networks.

As with other modern sciences (and their computational counterparts), neuroscience experiments can now produce data that, in terms of both quantity and complexity, challenge our interpretative abilities. It is relatively common to be faced with datasets containing many millions of neural spikes collected from tens of thousands of neurons. Traditional data analysis methods can, in a relatively straightforward manner, identify large-scale features in such data (e.g., on the scale of entire networks). What these approaches often cannot do is to connect macroscopic activity to the relevant small-scale behaviors of individual cells, especially in the face of ongoing background activity that is not relevant. This communication presents an application of machine learning techniques to bridge the gap between microscopic and macroscopic behaviors and identify the small-scale activity that leads to large-scale behavior, reducing data complexity to a level that can be amenable to further analysis. A small number of spatiotemporal spikes (among many millions) were found to provide reliable information about if and where a burst will occur.