Quadrupedal robots trot into the wild.

Deep reinforcement learning enables quadruped robots to traverse challenging natural environments using only proprioception.

Real-time, interactive website for US-county level Covid-19 event risk assessment.

Large events and gatherings, particularly those taking place indoors, have been linked to multi-transmission events that have accelerated the pandemic spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). To provide real-time, geo-localized risk information, we developed an interactive online dashboard that estimates the risk that at least one individual with SARS-CoV-2 is present in gatherings of different sizes in the United States. The website combines documented case reports at the county level with ascertainment bias information obtained via population-wide serological surveys to estimate real time circulating, per-capita infection rates.

Education and Training on Electronic Medical Records (EMRs) for health care professionals and students: A Scoping Review.

Background And Objectives: The ability of health care providers and students to use EMRs efficiently can lead to achieving improved clinical outcomes. Training policies and strategies play a major role in successful technology implementation and ongoing use of the EMR systems. To provide evidence-based guidance for developing and implementing educational interventions and training, we reviewed and summarized the current literature on EMR training targeting both healthcare professionals (HCP) and students.

Designing a Clinician-Facing Tool for Using Insights From Patients' Social Media Activity: Iterative Co-Design Approach.

Background: Recent research has emphasized the need for accessing information about patients to augment mental health patients' verbal reports in clinical settings. Although it has not been introduced in clinical settings, computational linguistic analysis on social media has proved it can infer mental health attributes, implying a potential use as collateral information at the point of care. To realize this potential and make social media insights actionable to clinical decision making, the gaps between computational linguistic analysis on social media and the current work practices of mental health clinicians must be bridged.

Computational discrimination between natural images based on gaze during mental imagery.

When retrieving image from memory, humans usually move their eyes spontaneously as if the image were in front of them. Such eye movements correlate strongly with the spatial layout of the recalled image content and function as memory cues facilitating the retrieval procedure. However, how close the correlation is between imagery eye movements and the eye movements while looking at the original image is unclear so far.

Noninvasive three-state sleep-wake staging in mice using electric field sensors.

Study Objective: Validate a novel method for sleep-wake staging in mice using noninvasive electric field (EF) sensors.

Methods: Mice were implanted with electroencephalogram (EEG) and electromyogram (EMG) electrodes and housed individually. Noninvasive EF sensors were attached to the exterior of each chamber to record respiration and other movement simultaneously with EEG, EMG, and video.

Digital Innovations for Global Mental Health: Opportunities for Data Science, Task Sharing, and Early Intervention.

Purpose: Globally, individuals living with mental disorders are more likely to have access to a mobile phone than mental health care. In this commentary, we highlight opportunities for expanding access to and use of digital technologies to advance research and intervention in mental health, with emphasis on the potential impact in lower resource settings.

Recent Findings: Drawing from empirical evidence, largely from higher income settings, we considered three emerging areas where digital technology will potentially play a prominent role: supporting methods in data science to further our understanding of mental health and inform interventions, task sharing for building workforce capacity by training and supervising non-specialist health workers, and facilitating new opportunities for early intervention for young people in lower resource settings.

The development and electronic delivery of case-based learning using a fast healthcare interoperability resource system.

HL7 International's Fast Healthcare Interoperability Resources (FHIR) standard provides a common format for sharing health data (eg, FHIR resources) and a RESTful Application Programming Interface (eg, FHIR API) for accessing those resources via a FHIR server connected to an electronic health record system or any other system storing clinical data. Substitutable Medical Applications and Reusable Technologies (SMART) leverages FHIR to create an electronic health record (EHR) agnostic app platform. It utilizes the OAuth standard to provide for authorization and authentication.

Deep learning-based automated speech detection as a marker of social functioning in late-life depression.

Background: Late-life depression (LLD) is associated with poor social functioning. However, previous research uses bias-prone self-report scales to measure social functioning and a more objective measure is lacking. We tested a novel wearable device to measure speech that participants encounter as an indicator of social interaction.

Rumor Propagation is Amplified by Echo Chambers in Social Media.

Spreading rumors on the Internet has become increasingly pervasive due to the proliferation of online social media. This paper investigates how rumors are amplified by a group of users who share similar interests or views, dubbed as an echo chamber. To this end, we identify and analyze 'rumor' echo chambers, each of which is a group of users who have participated in propagating common rumors.

A Cognitive Reformation.

Although cognitive science started in the 1970s as a multidisciplinary field with the goal of becoming an interdisciplinary one over time, it is now dominated by cognitive psychology. The question becomes whether this matters, and if it does, what should cognitive scientists do about it? I propose that the multidisciplinarity and interdisciplinarity of cognitive science does matter because it leads to potential generation of new ideas, models, and methods. I offer a few recommendations for reforming cognitive science based, in part, on the recent 41st annual meeting of the Cognitive Science Society that sought to reopen cognitive science to minority disciplines comprising cognitive science.

Intermittent high-flux albumin dialysis with continuous venovenous hemodialysis for acute-on-chronic liver failure and acute kidney injury.

Acute-on-chronic liver failure (ACLF) requiring intensive medical care and associated with acute kidney injury (AKI) has a mortality rate as high as 90% due to the lack of effective therapies. In this study, we assessed the effects of intermittent high-flux single-pass albumin dialysis (SPAD) coupled with continuous venovenous hemodialysis (CVVHD) on 28-day and 90-day survival and an array of clinical and laboratory parameters in patients with severe ACLF and renal insufficiency. Sixteen patients were studied.

Identifying and Planning for Individualized Change: Patient-Provider Collaboration Using Lightweight Food Diaries in Healthy Eating and Irritable Bowel Syndrome.

Identifying and planning strategies that support a healthy lifestyle or manage a chronic disease often require patient-provider collaboration. For example, people with healthy eating goals often share everyday food, exercise, or sleep data with health coaches or nutritionists to find opportunities for change, and patients with irritable bowel syndrome (IBS) often gather food and symptom data as part of working with providers to diagnose and manage symptoms. However, a lack of effective support often prevents health experts from reviewing large amounts of data in time-constrained visits, prevents focusing on individual goals, and prevents generating correct, individualized, and actionable recommendations.

A computational study of mental health awareness campaigns on social media.

As public discourse continues to progress online, it is important for mental health advocates, public health officials, and other curious parties and stakeholders, ranging from researchers, to those affected by the issue, to be aware of the advancing new mediums in which the public can share content ranging from useful resources and self-help tips to personal struggles with respect to both illness and its stigmatization. A better understanding of this new public discourse on mental health, often framed as social media campaigns, can help perpetuate the allocation of sparse mental health resources, the need for educational awareness, and the usefulness of community, with an opportunity to reach those seeking help at the right moment. The objective of this study was to understand the nature of and engagement around mental health content shared on mental health campaigns, specifically #MyTipsForMentalHealth on Twitter around World Mental Health Awareness Day in 2017.

Seizure detection devices for use in antiseizure medication clinical trials: A systematic review.

Objective: This study characterizes the current capabilities of seizure detection device (SDD) technology and evaluates the fitness of these devices for use in anti-seizure medication (ASM) clinical trials.

Methods: Through a systematic literature review, 36 wireless SDDs featured in published device validation studies were identified. Each device's seizure detection capabilities that addressed ASM clinical trial primary endpoint measurement needs were cataloged.

Dermoscopy diagnosis of cancerous lesions utilizing dual deep learning algorithms via visual and audio (sonification) outputs: Laboratory and prospective observational studies.

Background: Early diagnosis of skin cancer lesions by dermoscopy, the gold standard in dermatological imaging, calls for a diagnostic upscale. The aim of the study was to improve the accuracy of dermoscopic skin cancer diagnosis through use of novel deep learning (DL) algorithms. An additional sonification-derived diagnostic layer was added to the visual classification to increase sensitivity.

Trajectory-Based Skill Learning Using Generalized Cylinders.

In this article, we introduce Trajectory Learning using Generalized Cylinders (TLGC), a novel trajectory-based skill learning approach from human demonstrations. To model a demonstrated skill, TLGC uses a Generalized Cylinder-a geometric representation composed of an arbitrary space curve called the spine and a surface with smoothly varying cross-sections. Our approach is the first application of Generalized Cylinders to manipulation, and its geometric representation offers several key features: it identifies and extracts the implicit characteristics and boundaries of the skill by encoding the , it supports for generation of multiple skill reproductions maintaining those characteristics, the constructed model can generalize the skill to unforeseen situations through trajectory editing techniques, our approach also allows for obstacle avoidance and interactive human refinement of the resulting model through kinesthetic correction.

Multi-part segmentation for porcine offal inspection with auto-context and adaptive atlases.

Extensions to auto-context segmentation are proposed and applied to segmentation of multiple organs in porcine offal as a component of an envisaged system for post-mortem inspection at abbatoir. In common with multi-part segmentation of many biological objects, challenges include variations in configuration, orientation, shape, and appearance, as well as inter-part occlusion and missing parts. Auto-context uses context information about inferred class labels and can be effective in such settings.

EDoP Distance Between Sets of Incomplete Permutations: Application to Bacteria Classification Based on Gene Order.

In this work, we extend measures of distance between permutations to support incomplete permutations. Modeling and comparing incomplete permutations are a challenging computational problem of practical importance in many applications in bioinformatics and social science. We show that the proposed distance measure admits a closed-form expression and can be efficiently computed on sets of permutations involving several missing elements.

Variability in adolescent portal privacy features: how the unique privacy needs of the adolescent patient create a complex decision-making process.

Objective: Medical privacy policies, which are clear-cut for adults and young children, become ambiguous during adolescence. Yet medical organizations must establish unambiguous rules about patient and parental access to electronic patient portals. We conducted a national interview study to characterize the diversity in adolescent portal policies across a range of institutions and determine the factors influencing decisions about these policies.

A review of mobile apps for epilepsy self-management.

Mobile health app developers increasingly are interested in supporting the daily self-care of people with chronic conditions. The purpose of this study was to review mobile applications (apps) to promote epilepsy self-management. It investigates the following: 1) the available mobile apps for epilepsy, 2) how these apps support patient education and self-management (SM), and 3) their usefulness in supporting management of epilepsy.

Association Between Labor and Delivery Unit Census and Delays in Patient Management: Findings From a Computer Simulation Module.

Objective: To demonstrate the association between increases in labor and delivery unit census and delays in patient care decisions using a computer simulation module.

Methods: This was an observational cohort study of labor and delivery unit nurse managers. We developed a computer module that simulates the physical layout and clinical activity of the labor and delivery unit at our tertiary care academic medical center, in which players act as clinical managers in dynamically allocating nursing staff and beds as patients arrive, progress in labor, and undergo procedures.

Fontan Surgical Planning: Previous Accomplishments, Current Challenges, and Future Directions.

The ultimate goal of Fontan surgical planning is to provide additional insights into the clinical decision-making process. In its current state, surgical planning offers an accurate hemodynamic assessment of the pre-operative condition, provides anatomical constraints for potential surgical options, and produces decent post-operative predictions if boundary conditions are similar enough between the pre-operative and post-operative states. Moving forward, validation with post-operative data is a necessary step in order to assess the accuracy of surgical planning and determine which methodological improvements are needed.