Data Science Curriculum in the iField.

Many disciplines, including the broad Field of Information (iField), have been offering Data Science (DS) programs. There have been significant efforts exploring an individual discipline's identity and unique contributions to the broader DS education landscape. To advance DS education in the iField, the iSchool Data Science Curriculum Committee (iDSCC) was formed and charged with building and recommending a DS education framework for iSchools.

Cohort Identification from Free-Text Clinical Notes Using SNOMED CT's Hierarchical Semantic Relations.

In this paper, a new cohort identification system that exploits the semantic hierarchy of SNOMED CT is proposed to overcome the limitations of supervised machine learning-based approaches. Eligibility criteria descriptions and free-text clinical notes from the 2018 National NLP Clinical Challenge (n2c2) were processed to map to relevant SNOMED CT concepts and to measure semantic similarity between the eligibility criteria and patients. The eligibility of a patient was determined if the patient had a similarity score higher than a threshold cut-off value.

Health Recommender Systems Development, Usage, and Evaluation from 2010 to 2022: A Scoping Review.

A health recommender system (HRS) provides a user with personalized medical information based on the user's health profile. This scoping review aims to identify and summarize the HRS development in the most recent decade by focusing on five key aspects: health domain, user, recommended item, recommendation technology, and system evaluation. We searched PubMed, ACM Digital Library, IEEE Xplore, Web of Science, and Scopus databases for English literature published between 2010 and 2022.

Machine Learning and Medication Adherence: Scoping Review.

Background: This is the first scoping review to focus broadly on the topics of machine learning and medication adherence.

Objective: This review aims to categorize, summarize, and analyze literature focused on using machine learning for actions related to medication adherence.

Methods: PubMed, Scopus, ACM Digital Library, IEEE, and Web of Science were searched to find works that meet the inclusion criteria.

A systematic review of automatic text summarization for biomedical literature and EHRs.

Objective: Biomedical text summarization helps biomedical information seekers avoid information overload by reducing the length of a document while preserving the contents' essence. Our systematic review investigates the most recent biomedical text summarization researches on biomedical literature and electronic health records by analyzing their techniques, areas of application, and evaluation methods. We identify gaps and propose potential directions for future research.

The use of SNOMED CT, 2013-2020: a literature review.

Objective: This article reviews recent literature on the use of SNOMED CT as an extension of Lee et al's 2014 review on the same topic. The Lee et al's article covered literature published from 2001-2012, and the scope of this review was 2013-2020.

Materials And Methods: In line with Lee et al's methods, we searched the PubMed and Embase databases and identified 1002 articles for review, including studies from January 2013 to September 2020.

Research evidence on strategies enabling integration of electronic health records in the health care systems of low- and middle-income countries: A literature review.

Integration of electronic health records (EHRs) in the national health care systems of low- and middle-income countries (LMICs) is vital for achieving the United Nations Sustainable Development Goal of ensuring healthy lives and promoting well-being for all people of all ages. National EHR systems are increasing, but mostly in developed countries. Besides, there is limited research evidence on successful strategies for ensuring integration of national EHRs in the health care systems of LMICs.

Federated health information architecture: Enabling healthcare providers and policymakers to use data for decision-making.

Health information systems (HIS) in India, as in most other developing countries, support public health management but fail to enable healthcare providers to use data for delivering quality services. Such a failure is surprising, given that the population healthcare data that the system collects are aggregated from patient records. An important reason for this failure is that the health information architecture (HIA) of the HIS is designed primarily to serve the information needs of policymakers and program managers.

Feasibility of Extracting Key Elements from ClinicalTrials.gov to Support Clinicians' Patient Care Decisions.

Clinicians need up-to-date evidence from high quality clinical trials to support clinical decisions. However, applying evidence from the primary literature requires significant effort. To examine the feasibility of automatically extracting key clinical trial information from ClinicalTrials.

Physicians' perception of alternative displays of clinical research evidence for clinical decision support - A study with case vignettes.

Objective: To design alternate information displays that present summaries of clinical trial results to clinicians to support decision-making; and to compare the displays according to efficacy and acceptability.

Methods: A 6-between (information display presentation order) by 3-within (display type) factorial design. Two alternate displays were designed based on Information Foraging theory: a narrative summary that reduces the content to a few sentences; and a table format that structures the display according to the PICO (Population, Intervention, Comparison, Outcome) framework.

Formative evaluation of a patient-specific clinical knowledge summarization tool.

Objective: To iteratively design a prototype of a computerized clinical knowledge summarization (CKS) tool aimed at helping clinicians finding answers to their clinical questions; and to conduct a formative assessment of the usability, usefulness, efficiency, and impact of the CKS prototype on physicians' perceived decision quality compared with standard search of UpToDate and PubMed.

Materials And Methods: Mixed-methods observations of the interactions of 10 physicians with the CKS prototype vs. standard search in an effort to solve clinical problems posed as case vignettes.

Emergency Medical Text Classifier: New system improves processing and classification of triage notes.

Objective: Automated syndrome classification aims to aid near real-time syndromic surveillance to serve as an early warning system for disease outbreaks, using Emergency Department (ED) data. We present a system that improves the automatic classification of an ED record with triage note into one or more syndrome categories using the vector space model coupled with a 'learning' module that employs a pseudo-relevance feedback mechanism.

Materials And Methods: Terms from standard syndrome definitions are used to construct an initial reference dictionary for generating the syndrome and triage note vectors.

Text summarization in the biomedical domain: a systematic review of recent research.

Objective: The amount of information for clinicians and clinical researchers is growing exponentially. Text summarization reduces information as an attempt to enable users to find and understand relevant source texts more quickly and effortlessly. In recent years, substantial research has been conducted to develop and evaluate various summarization techniques in the biomedical domain.

Implementation of Emergency Medical Text Classifier for syndromic surveillance.

Public health officials use syndromic surveillance systems to facilitate early detection and response to infectious disease outbreaks. Emergency department clinical notes are becoming more available for surveillance but present the challenge of accurately extracting concepts from these text data. The purpose of this study was to implement a new system, Emergency Medical Text Classifier (EMT-C), into daily production for syndromic surveillance and evaluate system performance and user satisfaction.

Anonymous Record Linkage Between EPR and CDW-H: Toward Development of a Federated Genotype-Phenotype System.

Environmental Polymorphisms Registry (EPR) is a large-scale phenotype-by-genotype registry developed by National Institute of Environmental Health Sciences to facilitate translational research. The link between personal identity and collected genomic data was preserved in EPR which creates opportunities for EPR to be linked to phenotype-rich databases, such as the Carolina Data Warehouse for Health (CDW-H) located at the University of North Carolina hospital system. CDW-H contains clinically-relevant data for patients who have been admitted to UNC healthcare system.

Automatic identification of comparative effectiveness research from medline citations to support clinicians' treatment information needs.

Unlabelled: Online knowledge resources such as Medline can address most clinicians' patient care information needs. Yet, significant barriers, notably lack of time, limit the use of these sources at the point of care. The most common information needs raised by clinicians are treatment-related.

The secure medical research workspace: an IT infrastructure to enable secure research on clinical data.

Clinical data have tremendous value for translational research, but only if security and privacy concerns can be addressed satisfactorily. A collaboration of clinical and informatics teams, including RENCI, NC TraCS, UNC's School of Information and Library Science, Information Technology Service's Research Computing and other partners at the University of North Carolina at Chapel Hill have developed a system called the Secure Medical Research Workspace (SMRW) that enables researchers to use clinical data securely for research. SMRW significantly minimizes the risk presented when using identified clinical data, thereby protecting patients, researchers, and institutions associated with the data.

Automatically extracting sentences from Medline citations to support clinicians' information needs.

Objective: Online health knowledge resources contain answers to most of the information needs raised by clinicians in the course of care. However, significant barriers limit the use of these resources for decision-making, especially clinicians' lack of time. In this study we assessed the feasibility of automatically generating knowledge summaries for a particular clinical topic composed of relevant sentences extracted from Medline citations.

Patient electronic health data-driven approach to clinical decision support.

This article presents a novel visual analytics (VA)-based clinical decision support (CDS) tool prototype that was designed as a collaborative work between Renaissance Computing Institute and Duke University. Using Major Depressive Disorder data from MindLinc electronic health record system at Duke, the CDS tool shows an approach to leverage data from comparative population (patients with similar medical profile) to enhance a clinicians' decision making process at the point of care. The initial work is being extended in collaboration with the University of North Carolina CTSA to address the key challenges of CDS, as well as to show the use of VA to derive insight from large volumes of Electronic Health Record patient data.

Discovering implicit associations among critical biological entities.

We propose an approach to predicting implicit gene-disease associations based on the inference network, whereby genes and diseases are represented as nodes and are connected via two types of intermediate nodes: gene functions and phenotypes. To estimate the probabilities involved in the model, two learning schemes are compared; one baseline using co-annotations of keywords and the other taking advantage of free text. Additionally, we explore the use of domain ontologies to complement data sparseness and examine the impact of full text documents.

Discovering implicit associations between genes and hereditary diseases.

We propose an approach to predicting implicit gene-disease associations based on the inference network, whereby genes and diseases are represented as nodes and are connected via two types of intermediate nodes: gene functions and phenotypes. To estimate the probabilities involved in the model, two learning schemes are compared; one baseline using co-annotations of keywords and the other taking advantage of free text. Additionally, we explore the use of domain ontologies to complement data sparseness and examine the impact of full text documents.

A probabilistic model for identifying protein names and their name boundaries.

This paper proposes a method for identifying protein names in biomedical texts with an emphasis on detecting protein name boundaries. We use a probabilistic model which exploits several surface clues characterizing protein names and incorporates word classes for generalization. In contrast to previously proposed methods, our approach does not rely on natural language processing tools such as part-of-speech taggers and syntactic parsers, so as to reduce processing overhead and the potential number of probabilistic parameters to be estimated.