Implementing Cancer Registry Data With the PCORnet Common Data Model: The Greater Plains Collaborative Experience.

Purpose: Electronic health records (EHRs) comprise a rich source of real-world data for cancer studies, but they often lack critical structured data elements such as diagnosis date and disease stage. Fortunately, such concepts are available from hospital cancer registries. We describe experiences from integrating cancer registry data with EHR and billing data in an interoperable data model across a multisite clinical research network.

Feasibility of regional center telehealth visits utilizing a rural research network in people with Parkinson's disease.

Background: Impaired motor and cognitive function can make travel cumbersome for People with Parkinson's disease (PwPD). Over 50% of PwPD cared for at the University of Arkansas for Medical Sciences (UAMS) Movement Disorders Clinic reside over 30 miles from Little Rock. Improving access to clinical care for PwPD is needed.

Feasibility of telemedicine research visits in people with Parkinson's disease residing in medically underserved areas.

Introduction: Gait, balance, and cognitive impairment make travel cumbersome for People with Parkinson's disease (PwPD). About 75% of PwPD cared for at the University of Arkansas for Medical Sciences' Movement Disorders Clinic reside in medically underserved areas (MUAs). Validated remote evaluations could help improve their access to care.

DeIDNER Model: A Neural Network Named Entity Recognition Model for Use in the De-identification of Clinical Notes.

Clinical named entity recognition (NER) is an essential building block for many downstream natural language processing (NLP) applications such as information extraction and de-identification. Recently, deep learning (DL) methods that utilize word embeddings have become popular in clinical NLP tasks. However, there has been little work on evaluating and combining the word embeddings trained from different domains.

The h-ANN Model: Comprehensive Colonoscopy Concept Compilation Using Combined Contextual Embeddings.

Colonoscopy is a screening and diagnostic procedure for detection of colorectal carcinomas with specific quality metrics that monitor and improve adenoma detection rates. These quality metrics are stored in disparate documents i.e.

TAX-Corpus: Taxonomy based Annotations for Colonoscopy Evaluation.

Colonoscopy plays a critical role in screening of colorectal carcinomas (CC). Unfortunately, the data related to this procedure are stored in disparate documents, colonoscopy, pathology, and radiology reports respectively. The lack of integrated standardized documentation is impeding accurate reporting of quality metrics and clinical and translational research.

Breast and thyroid cancer: A multicenter study with Accrual to Clinical Trials Network.

Objective: To investigate a possible link between breast and thyroid cancer.

Methods: A multicenter retrospective review of patients in the electronic medical records of six Accrual to Clinical Trial (ACT) institutions with both breast cancer and thyroid carcinoma. Each center queried its data using a predefined data dictionary.

Patient characteristics and procedural outcomes of moderate sedation for endoscopic procedures in patients with obesity: A retrospective, propensity score-matched study.

Endoscopic procedures are performed commonly with moderate sedation. Obesity can pose a challenge in its safe administration. This study was aimed at assessing outcomes of endoscopy procedures performed with moderate sedation in obese patients.

DeIDNER Corpus: Annotation of Clinical Discharge Summary Notes for Named Entity Recognition Using BRAT Tool.

Named Entity Recognition (NER) aims to identify and classify entities into predefined categories is a critical pre-processing task in Natural Language Processing (NLP) pipeline. Readily available off-the-shelf NER algorithms or programs are trained on a general corpus and often need to be retrained when applied on a different domain. The end model's performance depends on the quality of named entities generated by these NER models used in the NLP task.

Consolidated EHR Workflow for Endoscopy Quality Reporting.

Although colonoscopy is the most frequently performed endoscopic procedure, the lack of standardized reporting is impeding clinical and translational research. Inadequacies in data extraction from the raw, unstructured text in electronic health records (EHR) pose an additional challenge to procedure quality metric reporting, as vital details related to the procedure are stored in disparate documents. Currently, there is no EHR workflow that links these documents to the specific colonoscopy procedure, making the process of data extraction error prone.

Machine Learning Approach to Optimize Sedation Use in Endoscopic Procedures.

Endoscopy procedures are often performed with either moderate or deep sedation. While deep sedation is costly, procedures with moderate sedation are not always well tolerated resulting in patient discomfort, and are often aborted. Due to lack of clear guidelines, the decision to utilize moderate sedation or anesthesia for a procedure is made by the providers, leading to high variability in clinical practice.

Deep Learning Methods to Predict Mortality in COVID-19 Patients: A Rapid Scoping Review.

The ongoing COVID-19 pandemic has become the most impactful pandemic of the past century. The SARS-CoV-2 virus has spread rapidly across the globe affecting and straining global health systems. More than 2 million people have died from COVID-19 (as of 30 January 2021).

Application of Machine Learning in Intensive Care Unit (ICU) Settings Using MIMIC Dataset: Systematic Review.

Modern Intensive Care Units (ICUs) provide continuous monitoring of critically ill patients susceptible to many complications affecting morbidity and mortality. ICU settings require a high staff-to-patient ratio and generates a sheer volume of data. For clinicians, the real-time interpretation of data and decision-making is a challenging task.

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.

Role of Machine Learning Techniques to Tackle the COVID-19 Crisis: Systematic Review.

Background: SARS-CoV-2, the novel coronavirus responsible for COVID-19, has caused havoc worldwide, with patients presenting a spectrum of complications that have pushed health care experts to explore new technological solutions and treatment plans. Artificial Intelligence (AI)-based technologies have played a substantial role in solving complex problems, and several organizations have been swift to adopt and customize these technologies in response to the challenges posed by the COVID-19 pandemic.

Objective: The objective of this study was to conduct a systematic review of the literature on the role of AI as a comprehensive and decisive technology to fight the COVID-19 crisis in the fields of epidemiology, diagnosis, and disease progression.

Chest imaging representing a COVID-19 positive rural U.S. population.

As the COVID-19 pandemic unfolds, radiology imaging is playing an increasingly vital role in determining therapeutic options, patient management, and research directions. Publicly available data are essential to drive new research into disease etiology, early detection, and response to therapy. In response to the COVID-19 crisis, the National Cancer Institute (NCI) has extended the Cancer Imaging Archive (TCIA) to include COVID-19 related images.

Toolkit to Compute Time-Based Elixhauser Comorbidity Indices and Extension to Common Data Models.

Objective: The time-dependent study of comorbidities provides insight into disease progression and trajectory. We hypothesize that understanding longitudinal disease characteristics can lead to more timely intervention and improve clinical outcomes. As a first step, we developed an efficient and easy-to-install toolkit, the Time-based Elixhauser Comorbidity Index (TECI), which pre-calculates time-based Elixhauser comorbidities and can be extended to common data models (CDMs).

Near Real Time EHR Data Utilization in a Clinical Study.

Extraction and use of Electronic Health Record (EHR) data is common in retrospective observational studies. However, electronic extraction and use of EHR data is rare during longitudinal prospective studies. One of the reasons is the amount of processing needed to assess data quality and assure consistency in meaning and format across multiple investigational sites.

Document Oriented Graphical Analysis and Prediction.

In general, small-mid size research laboratories struggle with managing clinical and secondary datasets. In addition, faster dissemination, correlation and prediction of information from available datasets is always a bottleneck. To address these challenges, we have developed a novel approach, Document Oriented Graphical Analysis and Prediction (DO-GAP), a hybrid tool, merging strengths of Not only SQL (NoSQL) document oriented and graph databases.

Prescription Patterns and Outcomes of Patients With Atrial Fibrillation Treated With Direct Oral Anticoagulants and Warfarin: A Real-World Analysis.

Background: Direct oral anticoagulants (DOACs) have been found to be similar or superior to warfarin in reducing ischemic stroke and intracranial hemorrhage (ICH) in patients with atrial fibrillation (AF). We sought to examine the anticoagulation prescription patterns in community since the advent of DOACs and also evaluate the outcomes in terms of gastrointestinal (GI) bleeding, ischemic stroke, and ICH in real-world patients with AF receiving anticoagulation.

Methods: This is a retrospective study comprising patients who were newly diagnosed with nonvalvular AF and were prescribed anticoagulants for stroke prevention.