Asthma and COVID-19: Unveiling Outcome Disparities and Treatment Impact Based on Distinct Endotypes.

Rationale: Epidemiologic studies on asthmatics and data suggest a protective role of T2 inflammation in SARS-CoV-2 infection.

Objective: Using a large, multisite cohort, we studied clinical outcomes following SARS-CoV-2 infection in multiple asthma endotypes and examined the effects of T2-directed biologics in infected asthmatics.in Methods: The National COVID Cohort Collaborative (N3C) Data Enclave was used to identify and stratify asthmatic patients by endotype to include non-T2 and T2 asthmatics, as well as exposure to T2-directed biologic therapy.

AI and Social Determinants of Health in Health Care: A Personal Perspective.

As a biomedical data scientist, when I think of the future of artificial intelligence in health care, the potential fills me with both excitement and caution. A promising area of innovation, AI can be used to assess the impact of social determinants of health on health outcomes, though more standardization is needed.

Selective classification with machine learning uncertainty estimates improves ACS prediction: A retrospective study in the prehospital setting.

Accurate identification of acute coronary syndrome (ACS) in the prehospital sestting is important for timely treatments that reduce damage to the compromised myocardium. Current machine learning approaches lack sufficient performance to safely rule-in or rule-out ACS. Our goal is to identify a method that bridges this gap.

K-Track-Covid: interactive web-based dashboard for analyzing geographical and temporal spread of COVID-19 in South Korea.

The COVID-19 pandemic has necessitated the development of robust tools for tracking and modeling the spread of the virus. We present 'K-Track-Covid,' an interactive web-based dashboard developed using the R Shiny framework, to offer users an intuitive dashboard for analyzing the geographical and temporal spread of COVID-19 in South Korea. Our dashboard employs dynamic user interface elements, employs validated epidemiological models, and integrates regional data to offer tailored visual displays.

Evaluating robustness of a generalized linear model when applied to electronic health record data accessed using an Open API.

The Integrated Clinical and Environmental Exposures Service (ICEES) provides open regulatory-compliant access to clinical data, including electronic health record data, that have been integrated with environmental exposures data. While ICEES has been validated in the context of an asthma use case and several other use cases, the regulatory constraints on the ICEES open application programming interface (OpenAPI) result in data loss when using the service for multivariate analysis. In this study, we investigated the robustness of the ICEES OpenAPI through a comparative analysis, in which we applied a generalized linear model (GLM) to the OpenAPI data and the constraint-free source data to examine factors predictive of asthma exacerbations.

Assessing the impact of privacy-preserving record linkage on record overlap and patient demographic and clinical characteristics in PCORnet®, the National Patient-Centered Clinical Research Network.

Objective: This article describes the implementation of a privacy-preserving record linkage (PPRL) solution across PCORnet®, the National Patient-Centered Clinical Research Network.

Material And Methods: Using a PPRL solution from Datavant, we quantified the degree of patient overlap across the network and report a de-duplicated analysis of the demographic and clinical characteristics of the PCORnet population.

Results: There were ∼170M patient records across the responding Network Partners, with ∼138M (81%) of those corresponding to a unique patient.

Establishing a framework for privacy-preserving record linkage among electronic health record and administrative claims databases within PCORnet, the National Patient-Centered Clinical Research Network.

Objective: The aim of this study was to determine whether a secure, privacy-preserving record linkage (PPRL) methodology can be implemented in a scalable manner for use in a large national clinical research network.

Results: We established the governance and technical capacity to support the use of PPRL across the National Patient-Centered Clinical Research Network (PCORnet). As a pilot, four sites used the Datavant software to transform patient personally identifiable information (PII) into de-identified tokens.

Leveraging Open Electronic Health Record Data and Environmental Exposures Data to Derive Insights Into Rare Pulmonary Disease.

Research on rare diseases has received increasing attention, in part due to the realized profitability of orphan drugs. Biomedical informatics holds promise in accelerating translational research on rare disease, yet challenges remain, including the lack of diagnostic codes for rare diseases and privacy concerns that prevent research access to electronic health records when few patients exist. The Integrated Clinical and Environmental Exposures Service (ICEES) provides regulatory-compliant open access to electronic health record data that have been integrated with environmental exposures data, as well as analytic tools to explore the integrated data.

Dug: a semantic search engine leveraging peer-reviewed knowledge to query biomedical data repositories.

Motivation: As the number of public data resources continues to proliferate, identifying relevant datasets across heterogenous repositories is becoming critical to answering scientific questions. To help researchers navigate this data landscape, we developed Dug: a semantic search tool for biomedical datasets utilizing evidence-based relationships from curated knowledge graphs to find relevant datasets and explain why those results are returned.

Results: Developed through the National Heart, Lung and Blood Institute's (NHLBI) BioData Catalyst ecosystem, Dug has indexed more than 15 911 study variables from public datasets.

Authorship Correction: International Changes in COVID-19 Clinical Trajectories Across 315 Hospitals and 6 Countries: Retrospective Cohort Study.

[This corrects the article DOI: 10.2196/31400.].

Open Application of Statistical and Machine Learning Models to Explore the Impact of Environmental Exposures on Health and Disease: An Asthma Use Case.

ICEES (Integrated Clinical and Environmental Exposures Service) provides a disease-agnostic, regulatory-compliant approach for openly exposing and analyzing clinical data that have been integrated at the patient level with environmental exposures data. ICEES is equipped with basic features to support exploratory analysis using statistical approaches, such as bivariate chi-square tests. We recently developed a method for using ICEES to generate multivariate tables for subsequent application of machine learning and statistical models.

NuMorph: Tools for cortical cellular phenotyping in tissue-cleared whole-brain images.

Tissue-clearing methods allow every cell in the mouse brain to be imaged without physical sectioning. However, the computational tools currently available for cell quantification in cleared tissue images have been limited to counting sparse cell populations in stereotypical mice. Here, we introduce NuMorph, a group of analysis tools to quantify all nuclei and nuclear markers within the mouse cortex after clearing and imaging by light-sheet microscopy.

International Changes in COVID-19 Clinical Trajectories Across 315 Hospitals and 6 Countries: Retrospective Cohort Study.

Background: Many countries have experienced 2 predominant waves of COVID-19-related hospitalizations. Comparing the clinical trajectories of patients hospitalized in separate waves of the pandemic enables further understanding of the evolving epidemiology, pathophysiology, and health care dynamics of the COVID-19 pandemic.

Objective: In this retrospective cohort study, we analyzed electronic health record (EHR) data from patients with SARS-CoV-2 infections hospitalized in participating health care systems representing 315 hospitals across 6 countries.

Identification of social determinants of health using multi-label classification of electronic health record clinical notes.

Objectives: Social determinants of health (SDH), key contributors to health, are rarely systematically measured and collected in the electronic health record (EHR). We investigate how to leverage clinical notes using novel applications of multi-label learning (MLL) to classify SDH in mental health and substance use disorder patients who frequent the emergency department.

Methods And Materials: We labeled a gold-standard corpus of EHR clinical note sentences ( = 4063) with 6 identified SDH-related domains recommended by the Institute of Medicine for inclusion in the EHR.

COVID-19 Data Utilization in North Carolina: Qualitative Analysis of Stakeholder Experiences.

Background: As the world faced the pandemic caused by the novel coronavirus disease 2019 (COVID-19), medical professionals, technologists, community leaders, and policy makers sought to understand how best to leverage data for public health surveillance and community education. With this complex public health problem, North Carolinians relied on data from state, federal, and global health organizations to increase their understanding of the pandemic and guide decision-making.

Objective: We aimed to describe the role that stakeholders involved in COVID-19-related data played in managing the pandemic in North Carolina.

Sequence to Sequence ECG Cardiac Rhythm Classification Using Convolutional Recurrent Neural Networks.

This paper proposes a novel deep learning architecture involving combinations of Convolutional Neural Networks (CNN) layers and Recurrent neural networks (RNN) layers that can be used to perform segmentation and classification of 5 cardiac rhythms based on ECG recordings. The algorithm is developed in a sequence to sequence setting where the input is a sequence of five second ECG signal sliding windows and the output is a sequence of cardiac rhythm labels. The novel architecture processes as input both the spectrograms of the ECG signal as well as the heartbeats' signal waveform.

SAU-Net: A Unified Network for Cell Counting in 2D and 3D Microscopy Images.

Image-based cell counting is a fundamental yet challenging task with wide applications in biological research. In this paper, we propose a novel unified deep network framework designed to solve this problem for various cell types in both 2D and 3D images. Specifically, we first propose SAU-Net for cell counting by extending the segmentation network U-Net with a Self-Attention module.

An approach for open multivariate analysis of integrated clinical and environmental exposures data.

The Integrated Clinical and Environmental Exposures Service (ICEES) provides regulatory-compliant open access to sensitive patient data that have been integrated with public exposures data. ICEES was designed initially to support dynamic cohort creation and bivariate contingency tests. The objective of the present study was to develop an open approach to support multivariate analyses using existing ICEES functionalities and abiding by all regulatory constraints.

Cross Modality Microscopy Segmentation via Adversarial Adaptation.

Deep learning techniques have been successfully applied to automatically segment and quantify cell-types in images acquired from both confocal and light sheet fluorescence microscopy. However, the training of deep learning networks requires a massive amount of manually-labeled training data, which is a very time-consuming operation. In this paper, we demonstrate an adversarial adaptation method to transfer deep network knowledge for microscopy segmentation from one imaging modality (e.

Clinical Annotation Research Kit (CLARK): Computable Phenotyping Using Machine Learning.

Computable phenotypes are algorithms that translate clinical features into code that can be run against electronic health record (EHR) data to define patient cohorts. However, computable phenotypes that only make use of structured EHR data do not capture the full richness of a patient's medical record. While natural language processing (NLP) methods have shown success in extracting clinical features from text, the use of such tools has generally been limited to research groups with substantial NLP expertise.

Fast Healthcare Interoperability Resources (FHIR) as a Meta Model to Integrate Common Data Models: Development of a Tool and Quantitative Validation Study.

Background: In a multisite clinical research collaboration, institutions may or may not use the same common data model (CDM) to store clinical data. To overcome this challenge, we proposed to use Health Level 7's Fast Healthcare Interoperability Resources (FHIR) as a meta-CDM-a single standard to represent clinical data.

Objective: In this study, we aimed to create an open-source application termed the Clinical Asset Mapping Program for FHIR (CAMP FHIR) to efficiently transform clinical data to FHIR for supporting source-agnostic CDM-to-FHIR mapping.

SAU-Net: A Universal Deep Network for Cell Counting.

Image-based cell counting is a fundamental yet challenging task with wide applications in biological research. In this paper, we propose a novel Deep Network designed to universally solve this problem for various cell types. Specifically, we first extend the segmentation network, U-Net with a Self-Attention module, named SAU-Net, for cell counting.

A novel approach for exposing and sharing clinical data: the Translator Integrated Clinical and Environmental Exposures Service.

Objective: This study aimed to develop a novel, regulatory-compliant approach for openly exposing integrated clinical and environmental exposures data: the Integrated Clinical and Environmental Exposures Service (ICEES).

Materials And Methods: The driving clinical use case for research and development of ICEES was asthma, which is a common disease influenced by hundreds of genes and a plethora of environmental exposures, including exposures to airborne pollutants. We developed a pipeline for integrating clinical data on patients with asthma-like conditions with data on environmental exposures derived from multiple public data sources.

The Building Blocks of Interoperability. A Multisite Analysis of Patient Demographic Attributes Available for Matching.

Background: Patient matching is a key barrier to achieving interoperability. Patient demographic elements must be consistently collected over time and region to be valuable elements for patient matching.

Objectives: We sought to determine what patient demographic attributes are collected at multiple institutions in the United States and see how their availability changes over time and across clinical sites.