Evaluating Artificial Intelligence-Driven Responses to Acute Liver Failure Queries: A Comparative Analysis Across Accuracy, Clarity, and Relevance.

Introduction: Recent advancements in Artificial Intelligence (AI), particularly through the deployment of Large Language Models (LLMs), have profoundly impacted healthcare. This study assesses five LLMs-ChatGPT 3.5, ChatGPT 4, BARD, CLAUDE, and COPILOT-on their response accuracy, clarity, and relevance to queries concerning acute liver failure (ALF).

Supporting the Capture of Social Needs Through Natural Language Processing.

Social factors impact morbidity and mortality among patients. Documenting social needs in the clinical notes is currently widely done by family physicians. The unstructured format of information on social factors in electronic health records limits the ability of providers to address these issues.

Racial Distribution of Neighborhood-Level Social Deprivation in a Retrospective Cohort of Prostate Cancer Survivors.

Background: A better understanding of neighborhood-level factors’ contribution is needed in order to increase the precision of cancer control interventions that target geographic determinants of cancer health disparities. This study characterized the distribution of neighborhood deprivation in a racially diverse cohort of prostate cancer survivors. Methods: A retrospective cohort of 253 prostate cancer patients who were treated with radical prostatectomy from 2011 to 2019 was established at the Medical University of South Carolina.

Comparison of a Cancer Family History Collection and Risk Assessment Tool - ItRunsInMyFamily - with Risk Assessment by Health-Care Professionals.

Introduction: Primary care providers (PCPs) and oncologists lack time and training to appropriately identify patients at increased risk for hereditary cancer using family health history (FHx) and clinical practice guideline (CPG) criteria. We built a tool, "ItRunsInMyFamily" (ItRuns) that automates FHx collection and risk assessment using CPGs. The purpose of this study was to evaluate ItRuns by measuring the level of concordance in referral patterns for genetic counseling/testing (GC/GT) between the CPGs as applied by the tool and genetic counselors (GCs), in comparison to oncologists and PCPs.

A Systems Approach to Interrogate Gene Expression Patterns in African American Men Presenting with Clinically Localized Prostate Cancer.

An emerging theory about racial differences in cancer risk and outcomes is that psychological and social stressors influence cellular stress responses; however, limited empirical data are available on racial differences in cellular stress responses among men who are at risk for adverse prostate cancer outcomes. In this study, we undertook a systems approach to examine molecular profiles and cellular stress responses in an important segment of African American (AA) and European American (EA) men: men undergoing prostate biopsy. We assessed the prostate transcriptome with a single biopsy core via high throughput RNA sequencing (RNA-Seq).

Informatics Ecosystems to Advance the Biology of Glycans.

Glycomics researchers have identified the need for integrated database systems for collecting glycomics information in a consistent format. The goal is to create a resource for knowledge discovery and dissemination to wider research communities. This has the potential and has exhibited initial success, to extend the research community to include biologists, clinicians, chemists, and computer scientists.

Racial Differences in Patient Portal Activation and Research Enrollment Among Patients With Prostate Cancer.

Purpose: The purpose of this study was to examine racial differences in patient portal activation and research participation among patients with prostate cancer.

Materials And Methods: Participants were African American and White patients with prostate cancer who were treated with radical prostatectomy (n = 218). Patient portal activation was determined using electronic health records, and research participation was measured based on completion of a social determinants survey.

Evaluation and comparison of hereditary Cancer guidelines in the population.

Background: Family health history (FHx) is an effective tool for identifying patients at risk of hereditary cancer. Hereditary cancer clinical practice guidelines (CPG) contain criteria used to evaluate FHx and to make recommendations for genetic consultation. Comparing different CPGs used to evaluate a common set of FHx provides insight into how well the CPGs perform, the extent of agreement across guidelines, and how well they identify patients who should consider a cancer genetic consultation.

Artificial Intelligence Pipeline to Bridge the Gap between Bench Researchers and Clinical Researchers in Precision Medicine.

Precision medicine informatics is a field of research that incorporates learning systems that generate new knowledge to improve individualized treatments using integrated data sets and models. Given the ever-increasing volumes of data that are relevant to patient care, artificial intelligence (AI) pipelines need to be a central component of such research to speed discovery. Applying AI methodology to complex multidisciplinary information retrieval can support efforts to discover bridging concepts within collaborating communities.

Automated detection of altered mental status in emergency department clinical notes: a deep learning approach.

Background: Machine learning has been used extensively in clinical text classification tasks. Deep learning approaches using word embeddings have been recently gaining momentum in biomedical applications. In an effort to automate the identification of altered mental status (AMS) in emergency department provider notes for the purpose of decision support, we compare the performance of classic bag-of-words-based machine learning classifiers and novel deep learning approaches.

Artificial Intelligence and Integrated Genotype⁻Phenotype Identification.

The integration of phenotypes and genotypes is at an unprecedented level and offers new opportunities to establish deep phenotypes. There are a number of challenges to overcome, specifically, accelerated growth of data, data silos, incompleteness, inaccuracies, and heterogeneity within and across data sources. This perspective report discusses artificial intelligence (AI) approaches that hold promise in addressing these challenges by automating computable phenotypes and integrating them with genotypes.

Data integration strategies for predictive analytics in precision medicine.

With the rapid growth of health-related data including genomic, proteomic, imaging and clinical, the arduous task of data integration can be overwhelmed by the complexity of the environment including data size and diversity. This report examines the role of data integration strategies for big data predictive analytics in precision medicine research. Infrastructure-as-code methodologies will be discussed as a means of integrating and managing data.

Prostate-specific IL-6 transgene autonomously induce prostate neoplasm through amplifying inflammation in the prostate and peri-prostatic adipose tissue.

Background: The causative role of the pro-inflammatory cytokine IL-6 in prostate cancer progression has been well established at molecular level. However, whether and how IL-6 may play a role in prostate cancer risk and development is not well defined. One limitation factor to acquiring this knowledge is the lack of appropriate animal models.

Virtualization of open-source secure web services to support data exchange in a pediatric critical care research network.

Objectives: To examine the feasibility of deploying a virtual web service for sharing data within a research network, and to evaluate the impact on data consistency and quality.

Material And Methods: Virtual machines (VMs) encapsulated an open-source, semantically and syntactically interoperable secure web service infrastructure along with a shadow database. The VMs were deployed to 8 Collaborative Pediatric Critical Care Research Network Clinical Centers.

Efficient Execution Methods of Pivoting for Bulk Extraction of Entity-Attribute-Value-Modeled Data.

Entity-attribute-value (EAV) tables are widely used to store data in electronic medical records and clinical study data management systems. Before they can be used by various analytical (e.g.

Informatics tools to advance the biology of glycosaminoglycans and proteoglycans.

Glycomics researchers have identified the need for integrated database systems for collecting glycomics information in a consistent format. The goal is to create a resource for knowledge discovery and dissemination to wider research communities. This has the potential to extend the research community to include biologists, clinicians, chemists, and computer scientists.

Using a content analysis to identify study eligibility criteria concepts in cancer nursing research.

The aims of this study were to (1) identify and categorize study eligibility criteria concepts used in cancer nursing randomized controlled trials and (2) determine the extent to which a previously identified set of study eligibility criteria, based primarily on medical randomized controlled trials, were represented in cancer nursing randomized controlled trials. A total of 145 articles of cancer nursing randomized controlled trials indexed in PubMed or Cumulative Index to Nursing and Allied Health Literature and published in English from 1986 to 2010 were screened, and 114 were eligible. Directed content analysis was conducted until data saturation was achieved.

Biomedical Terminology Mapper for UML projects.

As the biomedical community collects and generates more and more data, the need to describe these datasets for exchange and interoperability becomes crucial. This paper presents a mapping algorithm that can help developers expose local implementations described with UML through standard terminologies. The input UML class or attribute name is first normalized and tokenized, then lookups in a UMLS-based dictionary are performed.

Clinical and molecular models of glioblastoma multiforme survival.

Glioblastoma multiforme (GBM), a highly aggressive form of brain cancer, results in a median survival of 12-15 months. For decades, researchers have explored the effects of clinical and molecular factors on this disease and have identified several candidate prognostic markers. In this study, we evaluated the use of multivariate classification models for differentiating between subsets of patients who survive a relatively long or short time.

Multiplicity: an organizing principle for cancers and somatic mutations.

Background: With the advent of whole-genome analysis for profiling tumor tissue, a pressing need has emerged for principled methods of organizing the large amounts of resulting genomic information. We propose the concept of multiplicity measures on cancer and gene networks to organize the information in a clinically meaningful manner. Multiplicity applied in this context extends Fearon and Vogelstein's multi-hit genetic model of colorectal carcinoma across multiple cancers.

Metadata mapping and reuse in caBIG.

Background: This paper proposes that interoperability across biomedical databases can be improved by utilizing a repository of Common Data Elements (CDEs), UML model class-attributes and simple lexical algorithms to facilitate the building domain models. This is examined in the context of an existing system, the National Cancer Institute (NCI)'s cancer Biomedical Informatics Grid (caBIG). The goal is to demonstrate the deployment of open source tools that can be used to effectively map models and enable the reuse of existing information objects and CDEs in the development of new models for translational research applications.

Agent-based modeling supporting the migration of registry systems to grid based architectures.

With the increasing age and cost of operation of the existing NCI SEER platform core technologies, such essential resources in the fight against cancer as these will eventually have to be migrated to Grid based systems. In order to model this migration, a simulation is proposed based upon an agent modeling technology. This modeling technique allows for simulation of complex and distributed services provided by a large scale Grid computing platform such as the caBIG(™) project's caGRID.