Digital Health Innovations for Screening and Mitigating Mental Health Impacts of Adverse Childhood Experiences: Narrative Review.

Background: Exposures to both negative and positive experiences in childhood have proven to influence cardiovascular, immune, metabolic, and neurologic function throughout an individual's life. As such, adverse childhood experiences (ACEs) could have severe consequences on health and well-being into adulthood.

Objective: This study presents a narrative review of the use of digital health technologies (DHTs) and artificial intelligence to screen and mitigate risks and mental health consequences associated with ACEs among children and youth.

Evaluating Racial Disparities in Implementation and Monitoring of a Remote Blood Pressure Program in a Pregnant Population-A Retrospective Cohort Study.

Whether remote blood pressure (BP) monitoring can decrease racial disparities in BP measurement during pregnancy and the postpartum period remains unclear. This study evaluated whether Black and White patients enrolled in the Connected Maternity Online Monitoring (CMOM) program showed improvements in BP ascertainment and interval. A retrospective cohort of 3,976 pregnant patients enrolled in CMOM were compared to matched usual care patients between January 2016 and September 2022 using electronic health record data.

Towards an Explainable AI Platform to Study Interruptions in Cancer Radiation Therapy.

Radiation therapy interruptions drive cancer treatment failures; they represent an untapped opportunity for improving outcomes and narrowing treatment disparities. This research reports on the early development of the X-CART platform, which uses explainable AI to model cancer treatment outcome metrics based on high-dimensional associations with our local social determinants of health dataset to identify and explain causal pathways linking social disadvantage with increased radiation therapy interruptions.

Digital Personal Health Coaching Platform for Promoting Human Papillomavirus Infection Vaccinations and Cancer Prevention: Knowledge Graph-Based Recommendation System.

Background: Health promotion can empower populations to gain more control over their well-being by using digital interventions that focus on preventing the root causes of diseases. Digital platforms for personalized health coaching can improve health literacy and information-seeking behavior, leading to better health outcomes. Personal health records have been designed to enhance patients' self-management of a disease or condition.

Risk factors associated with post-acute sequelae of SARS-CoV-2: an N3C and NIH RECOVER study.

Background: More than one-third of individuals experience post-acute sequelae of SARS-CoV-2 infection (PASC, which includes long-COVID). The objective is to identify risk factors associated with PASC/long-COVID diagnosis.

Methods: This was a retrospective case-control study including 31 health systems in the United States from the National COVID Cohort Collaborative (N3C).

A Digital Personal Health Library for the Management of Abortion-Related Care via Telemedicine.

Abortion remains a highly controversial topic in many countries, particularly in the United States. As the COVID-19 pandemic introduced new challenges and restrictions, society saw a marked increase in demand for self-managed care. Likewise, the utilization of abortion care via telemedicine sparked interest, especially in communities with high infection rates.

Ochsner Emergency Department Overcrowding Scale (OEDOCS): predicting Emergency Department crowding among diverse academic hospital settings.

We developed the Ochsner Emergency Department Overcrowding Scale (OEDOCS) to help us measure and respond to crowding among diverse-sized Emergency Departments (ED) within our network. Not satisfied with our current Emergency Department (ED) crowding score, we first surveyed our ED staff to report perceived crowding and then developed models to predict perceived crowding from our Electronic Health Record (EHR) data. Staff at two ED locations, one large and one small, were asked to report a perceived crowding level between 0-200 every four hours for over 3 months.

Generalisable long COVID subtypes: findings from the NIH N3C and RECOVER programmes.

Background: Stratification of patients with post-acute sequelae of SARS-CoV-2 infection (PASC, or long COVID) would allow precision clinical management strategies. However, long COVID is incompletely understood and characterised by a wide range of manifestations that are difficult to analyse computationally. Additionally, the generalisability of machine learning classification of COVID-19 clinical outcomes has rarely been tested.

Defining Radiation Treatment Interruption Rates During the COVID-19 Pandemic: Findings From an Academic Center in an Underserved Urban Setting.

Purpose: Our purpose was to characterize radiation treatment interruption (RTI) rates and their potential association with sociodemographic variables in an urban population before and during the COVID-19 pandemic.

Methods And Materials: Electronic health records were retrospectively reviewed for patients treated between January 1, 2015, and February 28, 2021. Major and minor RTI were defined as ≥5 and 2 to 4 unplanned cancellations, respectively.

Geospatial data in pediatric asthma in the United States: a scoping review protocol.

Objective: The objective of this scoping review is to identify and describe the literature on the use of geospatial data in pediatric asthma research.

Introduction: Asthma is one of the most common pediatric chronic diseases in the United States, disproportionately affecting low-income patients. Asthma exacerbations may be triggered by local environmental factors, such as air pollution or exposure to indoor allergens.

Risk Factors Associated with Post-Acute Sequelae of SARS-CoV-2 in an EHR Cohort: A National COVID Cohort Collaborative (N3C) Analysis as part of the NIH RECOVER program.

Background: More than one-third of individuals experience post-acute sequelae of SARS-CoV-2 infection (PASC, which includes long-COVID).

Objective: To identify risk factors associated with PASC/long-COVID.

Design: Retrospective case-control study.

An Urban Population Health Observatory for Disease Causal Pathway Analysis and Decision Support: Underlying Explainable Artificial Intelligence Model.

Background: Many researchers have aimed to develop chronic health surveillance systems to assist in public health decision-making. Several digital health solutions created lack the ability to explain their decisions and actions to human users.

Objective: This study sought to (1) expand our existing Urban Population Health Observatory (UPHO) system by incorporating a semantics layer; (2) cohesively employ machine learning and semantic/logical inference to provide measurable evidence and detect pathways leading to undesirable health outcomes; (3) provide clinical use case scenarios and design case studies to identify socioenvironmental determinants of health associated with the prevalence of obesity, and (4) design a dashboard that demonstrates the use of UPHO in the context of obesity surveillance using the provided scenarios.

Sentiment Analysis of the Covid-19 Vaccines on Social Media.

The COVID-19 pandemic fueled one of the quickest vaccine developments in history. Misinformation on online social media often leads to negative vaccine sentiment. We conducted a sentiment analysis and Latent Dirichlet Allocation topic modeling from Reddit communities focusing on the COVID-19 vaccine.

Generalizable Long COVID Subtypes: Findings from the NIH N3C and RECOVER Programs.

Accurate stratification of patients with post-acute sequelae of SARS-CoV-2 infection (PASC, or long COVID) would allow precision clinical management strategies. However, the natural history of long COVID is incompletely understood and characterized by an extremely wide range of manifestations that are difficult to analyze computationally. In addition, the generalizability of machine learning classification of COVID-19 clinical outcomes has rarely been tested.

Analyzing Relationships Between Economic and Neighborhood-Related Social Determinants of Health and Intensive Care Unit Length of Stay for Critically Ill Children With Medical Complexity Presenting With Severe Sepsis.

Objectives: Of the Social Determinants of Health (SDoH), we evaluated socioeconomic and neighborhood-related factors which may affect children with medical complexity (CMC) admitted to a Pediatric Intensive Care Unit (PICU) in Shelby County, Tennessee with severe sepsis and their association with PICU length of stay (LOS). We hypothesized that census tract-level socioeconomic and neighborhood factors were associated with prolonged PICU LOS in CMC admitted with severe sepsis in the underserved community.

Methods: This single-center retrospective observational study included CMC living in Shelby County, Tennessee admitted to the ICU with severe sepsis over an 18-month period.

Public sentiment analysis and topic modeling regarding COVID-19 vaccines on the Reddit social media platform: A call to action for strengthening vaccine confidence.

Background: The COVID-19 pandemic fueled one of the most rapid vaccine developments in history. However, misinformation spread through online social media often leads to negative vaccine sentiment and hesitancy.

Methods: To investigate COVID-19 vaccine-related discussion in social media, we conducted a sentiment analysis and Latent Dirichlet Allocation topic modeling on textual data collected from 13 Reddit communities focusing on the COVID-19 vaccine from Dec 1, 2020, to May 15, 2021.

An Urban Population Health Observatory System to Support COVID-19 Pandemic Preparedness, Response, and Management: Design and Development Study.

Background: COVID-19 is impacting people worldwide and is currently a leading cause of death in many countries. Underlying factors, including Social Determinants of Health (SDoH), could contribute to these statistics. Our prior work has explored associations between SDoH and several adverse health outcomes (eg, asthma and obesity).

HemPHL: A Personal Health Library and mHealth Recommender to Promote Self-Management of Hemophilia.

Hemophilia is a rare inherited bleeding disorder characterized by the blood's inability to clot and could result in potentially life-threatening spontaneous bleeding into joints, organs, and tissues. Moreover, long-term management of this chronic disease is complex and costly. Current scientific evidence demonstrates that personalized digital health technologies could promote and facilitate the self-management of chronic diseases.

Predicting Intensive Care Unit Length of Stay and Mortality Using Patient Vital Signs: Machine Learning Model Development and Validation.

Background: Patient monitoring is vital in all stages of care. In particular, intensive care unit (ICU) patient monitoring has the potential to reduce complications and morbidity, and to increase the quality of care by enabling hospitals to deliver higher-quality, cost-effective patient care, and improve the quality of medical services in the ICU.

Objective: We here report the development and validation of ICU length of stay and mortality prediction models.

Using a Personal Health Library-Enabled mHealth Recommender System for Self-Management of Diabetes Among Underserved Populations: Use Case for Knowledge Graphs and Linked Data.

Background: Traditionally, digital health data management has been based on electronic health record (EHR) systems and has been handled primarily by centralized health providers. New mechanisms are needed to give patients more control over their digital health data. Personal health libraries (PHLs) provide a single point of secure access to patients' digital health data and enable the integration of knowledge stored in their digital health profiles with other sources of global knowledge.

Implementing an Urban Public Health Observatory for (Near) Real-Time Surveillance for the COVID-19 Pandemic.

The COVID-19 pandemic is broadly undercutting global health and economies, while disproportionally impacting socially disadvantaged populations. An impactful pandemic surveillance solution must draw from multi-dimensional integration of social determinants of health (SDoH) to contextually inform traditional epidemiological factors. In this article, we describe an Urban Public Health Observatory (UPHO) model which we have put into action in a mid-sized U.

Explainable Artificial Intelligence Recommendation System by Leveraging the Semantics of Adverse Childhood Experiences: Proof-of-Concept Prototype Development.

Background: The study of adverse childhood experiences and their consequences has emerged over the past 20 years. Although the conclusions from these studies are available, the same is not true of the data. Accordingly, it is a complex problem to build a training set and develop machine-learning models from these studies.

The Personal Health Library: A Single Point of Secure Access to Patient Digital Health Information.

Traditionally, health data management has been EMR-based and mostly handled by health care providers. Mechanisms are needed to give patients more control over their health conditions. Personal Health Libraries (PHLs) provide a single point of secure access to patients' digital health information that can help empower patients to make better-informed decisions about their health care.