An Activation Likelihood Estimation Meta-Analysis of Voxel-Based Morphometry Studies of Chemotherapy-Related Brain Volume Changes in Breast Cancer.

Background/objectives: Breast cancer chemotherapy patients and survivors face cognitive side effects that are not fully understood. Neuroimaging can provide a unique way to study these effects; however, it can be difficult to recruit large numbers of subjects. Our meta-analysis aims to synthesize volumetric neuroimaging data to highlight consistent findings in regional brain volume changes to further advance our understanding of the chemotherapy-related cognitive impairments faced by breast cancer patients and survivors.

New implementation of data standards for AI in oncology: Experience from the EuCanImage project.

Background: An unprecedented amount of personal health data, with the potential to revolutionize precision medicine, is generated at health care institutions worldwide. The exploitation of such data using artificial intelligence (AI) relies on the ability to combine heterogeneous, multicentric, multimodal, and multiparametric data, as well as thoughtful representation of knowledge and data availability. Despite these possibilities, significant methodological challenges and ethicolegal constraints still impede the real-world implementation of data models.

Knowledge Representation and Management in the Age of Long Covid and Large Language Models: a 2022-2023 Survey.

Objectives: To select, present, and summarize cutting edge work in the field of Knowledge Representation and Management (KRM) published in 2022 and 2023.

Methods: A comprehensive set of KRM-relevant articles published in 2022 and 2023 was retrieved by querying PubMed. Topic modeling with Latent Dirichlet Allocation was used to further refine this query and suggest areas of focus.

Expanding the Ontology of Organizational Structures of Trauma Centers and Trauma Systems.

A knowledge gap exists regarding the impact of organizational parameters of trauma centers and patient outcomes. This is partially due to such organizational parameters being understudied. The Ontology of Organizational Structures of Trauma Centers and Trauma Systems (OOSTT) provides a controlled vocabulary to study that specific area.

ADVANCE: a biomedical informatics approach to investigate acute kidney injury in infants.

Background: Acute kidney injury (AKI) occurs in up to half of infants admitted to the neonatal intensive care unit (NICU) and is associated with increased risks of death and more days of mechanical ventilation, hospitalization, and vasopressor drug support. Our objective was to build a granular relational database to study the impact that AKI has on infants admitted to Level-IV NICUs.

Methods: A relational database was created by linking data from the Children's Hospitals Neonatal Database with AKI-focused data from electronic health records from 9 centers.

Documenting the de-identification process of clinical and imaging data for AI for health imaging projects.

Artificial intelligence (AI) is revolutionizing the field of medical imaging, holding the potential to shift medicine from a reactive "sick-care" approach to a proactive focus on healthcare and prevention. The successful development of AI in this domain relies on access to large, comprehensive, and standardized real-world datasets that accurately represent diverse populations and diseases. However, images and data are sensitive, and as such, before using them in any way the data needs to be modified to protect the privacy of the patients.

Analysis of Caregiver Burden Expressed in Social Media Discussions.

Almost 40% of US adults provide informal caregiving, yet research gaps remain around what burdens affect informal caregivers. This study uses a novel social media site, Reddit, to mine and better understand what online communities focus on as their caregiving burdens. These forums were accessed using an application programming interface, a machine learning classifier was developed to remove low information posts, and topic modeling was applied to the corpus.

The informed consent form navigator: a tool for producing readable and compliant consent documents.

Background/objective: Informed consent forms (ICFs) and practices vary widely across institutions. This project expands on previous work at the University of Arkansas for Medical Sciences (UAMS) Center for Health Literacy to develop a plain language ICF template. Our interdisciplinary team of researchers, comprised of biomedical informaticists, health literacy experts, and stakeholders in the Institutional Review Board (IRB) process, has developed the ICF Navigator, a novel tool to facilitate the creation of plain language ICFs that comply with all relevant regulatory requirements.

Evaluating the temporal association between the recency of prescribed controlled substance acquisition and fatal and non-fatal opioid overdose.

Background: Previous studies have explored psychosocial effects as possible triggers of opioid overdose (OOD). However, little is known about the temporal association between OOD and prescribed controlled substance (CS) acquisition.

Objective: The objective of this study was to evaluate the temporal relationship between OOD and acquiring prescribed CSs prior to OOD.

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.

Semantic Integration of Multi-Modal Data and Derived Neuroimaging Results Using the Platform for Imaging in Precision Medicine (PRISM) in the Arkansas Imaging Enterprise System (ARIES).

Neuroimaging is among the most active research domains for the creation and management of open-access data repositories. Notably lacking from most data repositories are integrated capabilities for semantic representation. The Arkansas Imaging Enterprise System (ARIES) is a research data management system which features integrated capabilities to support semantic representations of multi-modal data from disparate sources (imaging, behavioral, or cognitive assessments), across common image-processing stages (preprocessing steps, segmentation schemes, analytic pipelines), as well as derived results (publishable findings).

Characteristics and Network Influence of Providers Involved in the Treatment of Patients With Chronic Back, Neck or Joint Pain in Arkansas.

Increasing emphasis on guidelines and prescription drug monitoring programs highlight the role of healthcare providers in pain treatment. Objectives of this study were to identify characteristics of key players and influence of opioid prescribers through construction of a referral network of patients with chronic pain. A retrospective cohort study was performed and patients with commercial or Medicaid coverage with chronic back, neck, or joint pain were identified using the Arkansas All-Payer Claims-Database.

API Driven On-Demand Participant ID Pseudonymization in Heterogeneous Multi-Study Research.

Objectives: To facilitate clinical and translational research, imaging and non-imaging clinical data from multiple disparate systems must be aggregated for analysis. Study participant records from various sources are linked together and to patient records when possible to address research questions while ensuring patient privacy. This paper presents a novel tool that pseudonymizes participant identifiers (PIDs) using a researcher-driven automated process that takes advantage of application-programming interface (API) and the Perl Open-Source Digital Imaging and Communications in Medicine Archive (POSDA) to further de-identify PIDs.

DICOM re-encoding of volumetrically annotated Lung Imaging Database Consortium (LIDC) nodules.

Purpose: The dataset contains annotations for lung nodules collected by the Lung Imaging Data Consortium and Image Database Resource Initiative (LIDC) stored as standard DICOM objects. The annotations accompany a collection of computed tomography (CT) scans for over 1000 subjects annotated by multiple expert readers, and correspond to "nodules ≥ 3 mm", defined as any lesion considered to be a nodule with greatest in-plane dimension in the range 3-30 mm regardless of presumed histology. The present dataset aims to simplify reuse of the data with the readily available tools, and is targeted towards researchers interested in the analysis of lung CT images.

PRISM: A Platform for Imaging in Precision Medicine.

Purpose: Precision medicine requires an understanding of individual variability, which can only be acquired from large data collections such as those supported by the Cancer Imaging Archive (TCIA). We have undertaken a program to extend the types of data TCIA can support. This, in turn, will enable TCIA to play a key role in precision medicine research by collecting and disseminating high-quality, state-of-the-art, quantitative imaging data that meet the evolving needs of the cancer research community.

Enhancing the drug ontology with semantically-rich representations of National Drug Codes and RxNorm unique concept identifiers.

Background: The Drug Ontology (DrOn) is a modular, extensible ontology of drug products, their ingredients, and their biological activity created to enable comparative effectiveness and health services researchers to query National Drug Codes (NDCs) that represent products by ingredient, by molecular disposition, by therapeutic disposition, and by physiological effect (e.g., diuretic).

Enhancing Clinical Data and Clinical Research Data with Biomedical Ontologies - Insights from the Knowledge Representation Perspective.

Objectives: There exists a communication gap between the biomedical informatics community on one side and the computer science/artificial intelligence community on the other side regarding the meaning of the terms "semantic integration" and "knowledge representation". This gap leads to approaches that attempt to provide one-to-one mappings between data elements and biomedical ontologies. Our aim is to clarify the representational differences between traditional data management and semantic-web-based data management by providing use cases of clinical data and clinical research data re-representation.

Ontology-Enhanced Representations of Non-image Data in The Cancer Imaging Archive.

The Cancer Imaging Archive (TCIA) hosts over 11 million de-identified medical images related to cancer for research reuse. These are organized around DICOM-format radiological collections that are grouped by disease type, modality, or research focus. Many collections also include diverse non-image datasets in a variety of formats without a common approach to representing the entities that the data are about.

The Role of Axiomatically-Rich Ontologies in Transforming Medical Data to Knowledge.

In the biomedical domain, there exist a number of common data models (CDM) that have experienced wide uptake. However, none of these has emerged as the common model. Recently, the demand for integrating and analyzing increasingly large data sets in clinical and translational research has led to numerous efforts to harmonize existing CDMs and integrate data curated based on those models.

Mismatches between major subhierarchies and semantic tags in SNOMED CT.

The fully specified name of a concept in SNOMED CT is formed by a term to which in the typical case is added a semantic tag. The latter is meant to disambiguate homonymous terms and to indicate in which major subhierarchy of SNOMED CT that concept fits. We have developed a method to determine whether a concept's tag correctly identifies its place in the hierarchy, and applied this method to an analysis of all active concepts in every SNOMED CT release from January 2003 to January 2017.

Analyzing SNOMED CT's Historical Data: Pitfalls and Possibilities.

SNOMED CT's Release Format 2 (RF2) has been announced as an improvement over its predecessor, for instance because of its more consistent and almost formal approach towards describing changes in components over different versions, as well as changes in the structure of SNOMED CT itself. We explore two sorts of changes that are only partially formalized in RF2: the relationships between associative relations and reasons for inactivations as expressed in Association Reference Sets and Attribute Value Reference Sets on the one hand, and the various patterns according to which semantic tags appearing in fully specified names change over subsequent versions with or without being related to inactivations. We propose a data conversion methodology that combines assertions about SNOMED CT components into history profiles and use elements of these profiles to build Formal Concept Analysis contexts to discover valid implications that can render implicit assumptions hidden in SNOMED CT's structure explicit.

Protein Ontology (PRO): enhancing and scaling up the representation of protein entities.

The Protein Ontology (PRO; http://purl.obolibrary.org/obo/pr) formally defines and describes taxon-specific and taxon-neutral protein-related entities in three major areas: proteins related by evolution; proteins produced from a given gene; and protein-containing complexes.

Ontological Foundations for Tracking Data Quality through the Internet of Things.

Amongst the positive outcomes expected from the Internet of Things for Health are longitudinal patient records that are more complete and less erroneous by complementing manual data entry with automatic data feeds from sensors. Unfortunately, devices are fallible too. Quality control procedures such as inspection, testing and maintenance can prevent devices from producing errors.