Developing an automated algorithm for identification of children and adolescents with diabetes using electronic health records from the OneFlorida+ clinical research network.

Aim: To develop an automated computable phenotype (CP) algorithm for identifying diabetes cases in children and adolescents using electronic health records (EHRs) from the UF Health System.

Materials And Methods: The CP algorithm was iteratively derived based on structured data from EHRs (UF Health System 2012-2020). We randomly selected 536 presumed cases among individuals aged <18 years who had (1) glycated haemoglobin levels ≥ 6.

Develop and validate a computable phenotype for the identification of Alzheimer's disease patients using electronic health record data.

Introduction: Alzheimer's disease (AD) is often misclassified in electronic health records (EHRs) when relying solely on diagnosis codes. This study aimed to develop a more accurate, computable phenotype (CP) for identifying AD patients using structured and unstructured EHR data.

Methods: We used EHRs from the University of Florida Health (UFHealth) system and created rule-based CPs iteratively through manual chart reviews.

Develop and Validate a Computable Phenotype for the Identification of Alzheimer's Disease Patients Using Electronic Health Record Data.

Introduction: Alzheimer's Disease (AD) are often misclassified in electronic health records (EHRs) when relying solely on diagnostic codes. This study aims to develop a more accurate, computable phenotype (CP) for identifying AD patients by using both structured and unstructured EHR data.

Methods: We used EHRs from the University of Florida Health (UF Health) system and created rule-based CPs iteratively through manual chart reviews.

The role of health system penetration rate in estimating the prevalence of type 1 diabetes in children and adolescents using electronic health records.

Objective: Having sufficient population coverage from the electronic health records (EHRs)-connected health system is essential for building a comprehensive EHR-based diabetes surveillance system. This study aimed to establish an EHR-based type 1 diabetes (T1D) surveillance system for children and adolescents across racial and ethnic groups by identifying the minimum population coverage from EHR-connected health systems to accurately estimate T1D prevalence.

Materials And Methods: We conducted a retrospective, cross-sectional analysis involving children and adolescents <20 years old identified from the OneFlorida+ Clinical Research Network (2018-2020).

The PRISM semantic cohort builder: a novel tool to search and access clinical data in TCIA imaging collections.

The cancer imaging archive (TICA) receives and manages an ever-increasing quantity of clinical (non-image) data containing valuable information about subjects in imaging collections. To harmonize and integrate these data, we have first cataloged the types of information occurring across public TCIA collections. We then produced mappings for these diverse instance data using ontology-based representation patterns and transformed the data into a knowledge graph in a semantic database.

A scoping review of semantic integration of health data and information.

Objective: We summarized a decade of new research focusing on semantic data integration (SDI) since 2009, and we aim to: (1) summarize the state-of-art approaches on integrating health data and information; and (2) identify the main gaps and challenges of integrating health data and information from multiple levels and domains.

Materials And Methods: We used PubMed as our focus is applications of SDI in biomedical domains and followed the Preferred Reporting Items for Systematic Review and Meta-Analyses (PRISMA) to search and report for relevant studies published between January 1, 2009 and December 31, 2021. We used Covidence-a systematic review management system-to carry out this scoping review.

Biases in using social media data for public health surveillance: A scoping review.

Objectives: A landscape scan of the methods that are used to either assess or mitigate biases when using social media data for public health surveillance, through a scoping review.

Materials And Methods: Following best practices, we searched two literature databases (i.e.

Assessing the Need for Semantic Data Integration for Surgical Biobanks-A Knowledge Representation Perspective.

To improve patient outcomes after trauma, the need to decrypt the post-traumatic immune response has been identified. One prerequisite to drive advancement in understanding that domain is the implementation of surgical biobanks. This paper focuses on the outcomes of patients with one of two diagnoses: post-traumatic arthritis and osteomyelitis.

Syndromic Surveillance Systems for Mass Gatherings: A Scoping Review.

Syndromic surveillance involves the near-real-time collection of data from a potential multitude of sources to detect outbreaks of disease or adverse health events earlier than traditional forms of public health surveillance. The purpose of the present study is to elucidate the role of syndromic surveillance during mass gathering scenarios. In the present review, the use of syndromic surveillance for mass gathering scenarios is described, including characteristics such as methodologies of data collection and analysis, degree of preparation and collaboration, and the degree to which prior surveillance infrastructure is utilized.

A Prospective Observational Comparison of Two Approaches to Anesthetizing the Trachea for Awake Intubation.

Background: Multiple techniques have been described for anesthetizing the lower glottis and trachea prior to awake fiberoptic intubation. The primary aim of this study is to evaluate whether direct application of local anesthetic to the lower airway via an epidural catheter under direct vision is equally efficacious when compared to use of a transtracheal block in adult patients with an anticipated difficult airway.

Methods: Patients age >18 years requiring awake fiberoptic intubation who underwent upper and lower airway topicalization were observed prospectively.

Understanding the Gap Between Information Models and Realism-Based Ontologies Using the Generic Component Model.

The wide-spread use of Common Data Models and information models in biomedical informatics encourages assumptions that those models could provide the entirety of what is needed for knowledge representation purposes. Based on the lack of computable semantics in frequently used Common Data Models, there appears to be a gap between knowledge representation requirements and these models. In this use-case oriented approach, we explore how a system-theoretic, architecture-centric, ontology-based methodology can help to better understand this gap.

Collecting data on organizational structures of trauma centers: the CAFE web service.

Background: During the past several decades, the American College of Surgeons has led efforts to standardize trauma care through their trauma center verification process and Trauma Quality Improvement Program. Despite these endeavors, great variability remains among trauma centers functioning at the same level. Little research has been conducted on the correlation between trauma center organizational structure and patient outcomes.