Electronic Health Record Data Quality and Performance Assessments: Scoping Review.

Background: Electronic health records (EHRs) have an enormous potential to advance medical research and practice through easily accessible and interpretable EHR-derived databases. Attainability of this potential is limited by issues with data quality (DQ) and performance assessment.

Objective: This review aims to streamline the current best practices on EHR DQ and performance assessments as a replicable standard for researchers in the field.

Intraoperative hypotension and postoperative acute kidney injury: A systematic review.

Background: There is no consensus regarding safe intraoperative blood pressure thresholds that protect against postoperative acute kidney injury (AKI). This review aims to examine the existing literature to delineate safe intraoperative hypotension (IOH) parameters to prevent postoperative AKI.

Methods: PubMed, Cochrane Central, and Web of Science were systematically searched for articles published between 2015 and 2022 relating the effects of IOH on postoperative AKI.

Machine learning models using mobile game play accurately classify children with autism.

Digitally-delivered healthcare is well suited to address current inequities in the delivery of care due to barriers of access to healthcare facilities. As the COVID-19 pandemic phases out, we have a unique opportunity to capitalize on the current familiarity with telemedicine approaches and continue to advocate for mainstream adoption of remote care delivery. In this paper, we specifically focus on the ability of GuessWhat? a smartphone-based charades-style gamified therapeutic intervention for autism spectrum disorder (ASD) to generate a signal that distinguishes children with ASD from neurotypical (NT) children.

Training Affective Computer Vision Models by Crowdsourcing Soft-Target Labels.

Background/introduction: Emotion detection classifiers traditionally predict discrete emotions. However, emotion expressions are often subjective, thus requiring a method to handle compound and ambiguous labels. We explore the feasibility of using crowdsourcing to acquire reliable soft-target labels and evaluate an emotion detection classifier trained with these labels.

Improved Digital Therapy for Developmental Pediatrics Using Domain-Specific Artificial Intelligence: Machine Learning Study.

Background: Automated emotion classification could aid those who struggle to recognize emotions, including children with developmental behavioral conditions such as autism. However, most computer vision emotion recognition models are trained on adult emotion and therefore underperform when applied to child faces.

Objective: We designed a strategy to gamify the collection and labeling of child emotion-enriched images to boost the performance of automatic child emotion recognition models to a level closer to what will be needed for digital health care approaches.

A Mobile Game Platform for Improving Social Communication in Children with Autism: A Feasibility Study.

Background: Many children with autism cannot receive timely in-person diagnosis and therapy, especially in situations where access is limited by geography, socioeconomics, or global health concerns such as the current COVD-19 pandemic. Mobile solutions that work outside of traditional clinical environments can safeguard against gaps in access to quality care.

Objective: The aim of the study is to examine the engagement level and therapeutic feasibility of a mobile game platform for children with autism.

Crowdsourced privacy-preserved feature tagging of short home videos for machine learning ASD detection.

Standard medical diagnosis of mental health conditions requires licensed experts who are increasingly outnumbered by those at risk, limiting reach. We test the hypothesis that a trustworthy crowd of non-experts can efficiently annotate behavioral features needed for accurate machine learning detection of the common childhood developmental disorder Autism Spectrum Disorder (ASD) for children under 8 years old. We implement a novel process for identifying and certifying a trustworthy distributed workforce for video feature extraction, selecting a workforce of 102 workers from a pool of 1,107.

Comparing Computed Tomography-Derived Augmented Reality Holograms to a Standard Picture Archiving and Communication Systems Viewer for Presurgical Planning: Feasibility Study.

Background: Picture archiving and communication systems (PACS) are ubiquitously used to store, share, and view radiological information for preoperative planning across surgical specialties. Although traditional PACS software has proven reliable in terms of display accuracy and ease of use, it remains limited by its inherent representation of medical imaging in 2 dimensions. Augmented reality (AR) systems present an exciting opportunity to complement traditional PACS capabilities.

Feature replacement methods enable reliable home video analysis for machine learning detection of autism.

Autism Spectrum Disorder is a neuropsychiatric condition affecting 53 million children worldwide and for which early diagnosis is critical to the outcome of behavior therapies. Machine learning applied to features manually extracted from readily accessible videos (e.g.

Precision Telemedicine through Crowdsourced Machine Learning: Testing Variability of Crowd Workers for Video-Based Autism Feature Recognition.

Mobilized telemedicine is becoming a key, and even necessary, facet of both precision health and precision medicine. In this study, we evaluate the capability and potential of a crowd of virtual workers-defined as vetted members of popular crowdsourcing platforms-to aid in the task of diagnosing autism. We evaluate workers when crowdsourcing the task of providing categorical ordinal behavioral ratings to unstructured public YouTube videos of children with autism and neurotypical controls.