594 results match your criteria AMIA Jt Summits Transl Sci Proc[Journal]


Developing a FHIR-based Framework for Phenome Wide Association Studies: A Case Study with A Pan-Cancer Cohort.

AMIA Jt Summits Transl Sci Proc 2020 30;2020:750-759. Epub 2020 May 30.

Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA.

Phenome Wide Association Studies (PheWAS) enables phenome-wide scans to discover novel associations between genotype and clinical phenotypes via linking available genomic reports and large-scale Electronic Health Record (EHR). Data heterogeneity from different EHR systems and genetic reports has been a critical challenge that hinders meaningful validation. To address this, we propose an FHIR-based framework to model the PheWAS study in a standard manner. Read More

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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7233075PMC

Web-based Interactive Visualization of Non-Lattice Subgraphs (WINS) in SNOMED CT.

AMIA Jt Summits Transl Sci Proc 2020 30;2020:740-749. Epub 2020 May 30.

The University of Texas Health Science Center at Houston, Houston, Texas, USA.

Non-lattice subgraphs are often indicative of structural anomalies in ontological systems. Visualization of SNOMED CT's non-lattice subgraphs can help make sense of what has been asserted in the hierarchical ("is-a") relation. More importantly, it can demonstrate what has not been asserted, or "is-not-a," using Closed-World Assumption for such subgraphs. Read More

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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7233097PMC

Mining Twitter to Assess the Determinants of Health Behavior towards Palliative Care in the United States.

AMIA Jt Summits Transl Sci Proc 2020 30;2020:730-739. Epub 2020 May 30.

University of Florida, Gainesville, Florida, USA.

Palliative care is a specialized service with proven efficacy in improving patients' quality-of-life. Nevertheless, lack of awareness and misunderstanding limits its adoption. Research is urgently needed to understand the determinants (e. Read More

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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7233059PMC

Data-driven Sublanguage Analysis for Cancer Genomics Knowledge Modeling: Applications in Mining Oncological Genetics Information from Patients' Genetic Reports.

AMIA Jt Summits Transl Sci Proc 2020 30;2020:720-729. Epub 2020 May 30.

Division of Digital Health Sciences, Mayo Clinic, Rochester, MN.

Despite an abundance of information in clinical genetic testing reports, information is oftentimes not well documented/utilized for decision making. Unstructured information in genetic reports can contribute to long-term patient management and future translational research. Thus, we proposed a knowledge model that could manage unstructured information in medical genetic reports and facilitate knowledge extraction, curation and updating. Read More

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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7233104PMC

Integrating Electronic Health Record Data into the ADEpedia-on-OHDSI Platform for Improved Signal Detection: A Case Study of Immune-related Adverse Events.

AMIA Jt Summits Transl Sci Proc 2020 30;2020:710-719. Epub 2020 May 30.

Department of Health Sciences Research, Mayo Clinic, Rochester, MN.

With widespread adoption of electronic health records (EHRs), Real World Data and Real World Evidence (RWE) have been increasingly used by FDA for evaluating drug safety and effectiveness. However, integration of heterogeneous drug safety data sources and systems remains an impediment for effective pharmacovigilance studies. In an ongoing project, we have developed a next generation pharmacovigilance signal detection framework known as ADEpedia-on-OHDSI using the OMOP common data model (CDM). Read More

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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7233056PMC

A deep-learning based system for accurate extraction of blood pressure data in clinical narratives.

AMIA Jt Summits Transl Sci Proc 2020 30;2020:703-709. Epub 2020 May 30.

University of North Carolina at Charlotte, Charlotte, NC, USA.

This study presents a novel workflow for identifying and analyzing blood pressure readings in clinical narratives using a Convolution Neural Network. The network performs three tasks: identifying blood pressure readings, determining the exactness of the readings, and then classifying the readings into three classes: general, treatment, and suggestion. The system can be easily set up and deployed by people who are not experts in clinical Natural Language Processing. Read More

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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7233038PMC

Adapting a System-Theoretic Hazard Analysis Method for the Analysis of an eHealth Interoperability Conformance Profile.

AMIA Jt Summits Transl Sci Proc 2020 30;2020:693-702. Epub 2020 May 30.

University of Victoria, Victoria, BC, Canada.

Interoperability between heterogenous (health) IT systems relies on standards, which are communicated to system vendors in the form of so-called conformance profiles. Clinical information systems are often subjected to mandatory conformance testing and certification prior to being admitted into the health information exchange (HIE). The requirements specified in conformance profiles are therefore instrumental for ensuring the correctness and safety of the emerging HIE network. Read More

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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7233088PMC

FHIR Lab Reports: using SMART on FHIR and CDS Hooks to increase the clinical utility of pharmacogenomic laboratory test results.

AMIA Jt Summits Transl Sci Proc 2020 30;2020:683-692. Epub 2020 May 30.

The Department of Biomedical Informatics, 421 Wakara Way, University of Utah, Salt Lake City, Utah 84108.

Laboratory tests are a common aspect of clinical care and are the primary source of clinical genomic data. However, most laboratories use PDF documents to store and exchange the results of these tests. This locks the data into a static format and leaves the results only human-readable. Read More

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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7233102PMC

A Word Graph-based Method for Disease Topic Identification in Biomedical Literature.

AMIA Jt Summits Transl Sci Proc 2020 30;2020:674-682. Epub 2020 May 30.

IBM Research - China, Beijing, China.

An important task in biomedical literature precise search is to identify paper describing a certain disease. The tradi- tional topic identification approaches based on neural network can be used to recognize the disease topic of literature. To achieve better performance, we propose a novel word graph-based method for disease topic identification in this paper. Read More

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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7233094PMC

A Population-Based Study of Simvastatin Drug-Drug Interactions in Cardiovascular Disease Patients.

AMIA Jt Summits Transl Sci Proc 2020 30;2020:664-673. Epub 2020 May 30.

Institute for Health Informatics, University of Minnesota, Minneapolis, MN, USA.

Simvastatin is a commonly used medication for lipid management and cardiovascular disease, however, the risk of adverse events (AEs) with its use increases via drug-drug interaction (DDI) exposures. Patients were extracted if initially diagnosed with cardiovascular disease and newly initiated simvastatin therapy. The cohort was divided into a DDI-exposed group and a non-DDI exposed group. Read More

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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7233072PMC

MultiFusionNet: Atrial Fibrillation Detection With Deep Neural Networks.

AMIA Jt Summits Transl Sci Proc 2020 30;2020:654-663. Epub 2020 May 30.

Emory University, Atlanta, GA, USA.

Atrial fibrillation (AF) is the most common cardiac arrhythmia as well as a significant risk factor in heart failure and coronary artery disease. AF can be detected by using a short ECG recording. However, discriminating atrial fibrillation from normal sinus rhythm, other arrhythmia and strong noise, given a short ECG recording, is challenging. Read More

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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7233068PMC

Can Neo4j Replace PostgreSQL in Healthcare?

AMIA Jt Summits Transl Sci Proc 2020 30;2020:646-653. Epub 2020 May 30.

University of San Francisco, San Francisco, CA.

Our current big data landscape prompts us to develop new analytical skills in order to make the best use of the abundance of datasets at hand. Traditionally, SQL databases such as PostgreSQL have been the databases of choice, and newer graph databases such as Neo4j have been relegated to the analysis of social network and transportation datasets. In this paper, we conduct a side by side comparison of PostgreSQL (using SQL) and Neo4j (using Cypher) using the MIMIC-III patient database as a case study. Read More

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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7233060PMC

Interpretable Batch IRL to Extract Clinician Goals in ICU Hypotension Management.

AMIA Jt Summits Transl Sci Proc 2020 30;2020:636-645. Epub 2020 May 30.

Harvard University, Paulson School of Engineering and Applied Sciences, Cambridge, MA.

Exposing and understanding the motivations of clinicians is an important step for building robust assistive agents as well as improving care. In this work, we focus on understanding the motivations for clinicians managing hypotension in the ICU. We model the ICU interventions as a batch, sequential decision making problem and develop a novel interpretable batch variant of Adversarial Inverse Reinforcement Learning algorithm that not only learns rewards which induce treatment policies similar to clinical treatments, but also ensure that the learned functional form of rewards is consistent with the decision mechanisms of clinicians in the ICU. Read More

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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7233064PMC

Paraphrasing to improve the performance of Electronic Health Records Question Answering.

AMIA Jt Summits Transl Sci Proc 2020 30;2020:626-635. Epub 2020 May 30.

School of Biomedical Informatics, University of Texas Health Science Center at Houston, Houston TX, USA.

This paper describes a paraphrasing approach to improve the performance of question answering (QA) for electronic health records (EHRs). QA systems for structured EHR data usually rely on semantic parsing, which aims to generate machine-understandable logical forms from free-text questions. Training semantic parsers requires large datasets of question-logical form (QL) pairs, which are labor-intensive to create. Read More

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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7233085PMC

The Impact of Medical Big Data Anonymization on Early Acute Kidney Injury Risk Prediction.

AMIA Jt Summits Transl Sci Proc 2020 30;2020:617-625. Epub 2020 May 30.

University of Kansas Medical Center, Department of Internal Medicine, Division of Medical Informatics, Kansas City, KS, USA.

Artificial intelligence enabled medical big data analysis has the potential to revolutionize medical practice from diagnosis and prediction of complex diseases to making recommendations and resource allocation decisions in an evidence-based manner. However, big data comes with big disclosure risks. To preserve privacy, excessive data anonymization is often necessary, leading to significant loss of data utility. Read More

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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7233037PMC

A Machine Learning Approach for Long-Term Prognosis of Bladder Cancer based on Clinical and Molecular Features.

AMIA Jt Summits Transl Sci Proc 2020 30;2020:607-616. Epub 2020 May 30.

Department of Biomedical Data Science, Dartmouth College, Hanover, NH.

Improving the consistency and reproducibility of bladder cancer prognoses necessitates the development of accurate, predictive prognostic models. Current methods of determining the prognosis of bladder cancer patients rely on manual decision-making, including factors with high intra- and inter-observer variability, such as tumor grade. To advance the long-term prediction of bladder cancer prognoses, we developed and tested a computational model to predict the 10-year overall survival outcome using population-based bladder cancer data, without considering tumor grade classification. Read More

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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7233061PMC

Patient Representation Transfer Learning from Clinical Notes based on Hierarchical Attention Network.

AMIA Jt Summits Transl Sci Proc 2020 30;2020:597-606. Epub 2020 May 30.

School of Biomedical Informatics, The University of Texas Health Science Center at Houston Houston, TX, USA.

To explicitly learn patient representations from longitudinal clinical notes, we propose a hierarchical attention-based recurrent neural network (RNN) with greedy segmentation to distinguish between shorter and longer, more meaningful gaps between notes. The proposed model is evaluated for both a direct clinical prediction task (mortality) and as a transfer learning pre-training model to downstream evaluation (phenotype prediction of obesity and its comorbidities). Experimental results first show the proposed model with appropriate segmentation achieved the best performance on mortality prediction, indicating the effectiveness of hierarchical RNNs in dealing with longitudinal clinical text. Read More

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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7233035PMC

Characterizing Basic and Complex Usage of i2b2 at an Academic Medical Center.

AMIA Jt Summits Transl Sci Proc 2020 30;2020:589-596. Epub 2020 May 30.

Information Technologies and Services Department, Weill Cornell Medicine, New York, NY.

Developed to enable basic queries for cohort discovery, i2b2 has evolved to support complex queries. Little is known whether query sophistication - and the informatics resources required to support it - addresses researcher needs. In three years at our institution, 609 researchers ran 6,662 queries and requested re-identification of 80 patient cohorts to support specific studies. Read More

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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7233105PMC

Developing a Search Engine for Precision Medicine.

AMIA Jt Summits Transl Sci Proc 2020 30;2020:579-588. Epub 2020 May 30.

School of Biomedical Informatics UTHealth, Houston, Texas.

Precision medicine focuses on developing new treatments based on an individual's genetic, environmental, and lifestyle profile. While this data-driven approach has led to significant advances, retrieving information specific to a patient's condition has proved challenging for oncologists due to the large volume of data. In this paper, we propose the PRecIsion Medicine Robust Oncology Search Engine (PRIMROSE) for cancer patients that retrieves scientific articles and clinical trials based on a patient's condition, genetic profile, age, and gender. Read More

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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7233032PMC

A Series Registration Framework to Recover Resting-State Functional Magnetic Resonance Data Degraded By Motion.

AMIA Jt Summits Transl Sci Proc 2020 30;2020:569-578. Epub 2020 May 30.

Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, Pennsylvania.

Data retention is a significant problem in the medical imaging domain. For example, resting-state functional magnetic resonance images (rs-fMRIs) are invaluable for studying neurodevelopment but are highly susceptible to corruption due to patient motion. The effects of patient motion can be reduced through post-acquisition techniques such as volume registration. Read More

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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7233096PMC

Chemical Entity Recognition for MEDLINE Indexing.

AMIA Jt Summits Transl Sci Proc 2020 30;2020:561-568. Epub 2020 May 30.

Lister Hill National Center for Biomedical Communications, U.S. National Library of Medicine, National Institutes of Health, Bethesda, MD.

Chemical entity recognition is essential for indexing scientific literature in the MEDLINE database at the National Library of Medicine. However, the tool currently used to suggest terms for indexing, the Medical Text Indexer, was not originally conceived as a chemical recognition tool. It has instead been adapted to the task via its use of MetaMap and the addition of in-house patterns and rules. Read More

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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7233078PMC

An Automated Two-step Pipeline for Aggressive Prostate Lesion Detection from Multi-parametric MR Sequence.

AMIA Jt Summits Transl Sci Proc 2020 30;2020:552-560. Epub 2020 May 30.

Department of Biomedical Data Science, Stanford University, Stanford, CA.

A substantial percentage of prostate cancer cases are overdiagnosed and overtreated due to the challenge in deter- mining aggressiveness. Multi-parametric MR is a powerful imaging technique to capture distinct characteristics of prostate lesions that are informative for aggressiveness assessment. However, manual interpretation requires a high level of expertise, is time-consuming, and significant inter-observer variation exists for radiologists. Read More

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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7233091PMC

The Evaluation of Clinical Classifications Software Using the National Inpatient Sample Database.

AMIA Jt Summits Transl Sci Proc 2020 30;2020:542-551. Epub 2020 May 30.

University of Minnesota College of Pharmacy, Minneapolis, MN, US.

The Clinical Classifications Software (CCS), by grouping International Classification of Diseases (ICD), provides the capacity to better account for clinical conditions for payers, policy makers, and researchers to analyze outcomes, costs, and utilization. There is a critical need for additional research on application of CCS categories to validate the clinical condition representation and to prevent gaps in research. This study compared the event frequency and ICD codes of CCS categories with significant changes from the first three quarters of 2015 to 2016 using National Inpatient Sample data. Read More

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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7233079PMC

JSONize: A Scalable Machine Learning Pipeline to Model Medical Notes as Semi-structured Documents.

AMIA Jt Summits Transl Sci Proc 2020 30;2020:533-541. Epub 2020 May 30.

Oak Ridge National Laboratory, Oak Ridge, TN.

The Department of Veteran's Affairs (VA) archives one of the largest corpora of clinical notes in their corporate data warehouse as unstructured text data. Unstructured text easily supports keyword searches and regular expressions. Often these simple searches do not adequately support the complex searches that need to be performed on notes. Read More

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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7233081PMC

Utilizing User-Centered EHR Design for Systematic Deep Brain Stimulation Data Collection.

AMIA Jt Summits Transl Sci Proc 2020 30;2020:527-532. Epub 2020 May 30.

Icahn School of Medicine at Mount Sinai, New York, New York.

This project aims to assess usability and acceptance of a customized Epic-based flowsheet designed to streamline the complex workflows associated with care of patients with implanted Deep Brain Stimulators (DBS). DBS patient care workflows are markedly fragmented, requiring providers to switch between multiple disparate systems. This is the first attempt to systematically evaluate usability of a unified solution built as a flowsheet in Epic. Read More

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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7233084PMC

Challenges in Using a Graph Database to Represent and Analyze Mappings of Cancer Study Data Standards.

AMIA Jt Summits Transl Sci Proc 2020 30;2020:517-526. Epub 2020 May 30.

Mayo Clinic, Rochester, MN, USA.

While using data standards can facilitate research by making it easier to share data, manually mapping to data standards creates an obstacle to their adoption. Semi-automated mapping strategies can reduce the manual mapping burden. Machine learning approaches, such as artificial neural networks, can predict mappings between clinical data standards but are limited by the need for training data. Read More

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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7233100PMC

Extracting Smoking Status from Electronic Health Records Using NLP and Deep Learning.

AMIA Jt Summits Transl Sci Proc 2020 30;2020:507-516. Epub 2020 May 30.

Wake Forest University School of Medicine, Winston Salem, NC.

Half a million people die every year from smoking-related issues across the United States. It is essential to identify individuals who are tobacco-dependent in order to implement preventive measures. In this study, we investigate the effectiveness of deep learning models to extract smoking status of patients from clinical progress notes. Read More

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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7233082PMC

Automating the Transformation of Free-Text Clinical Problems into SNOMED CT Expressions.

AMIA Jt Summits Transl Sci Proc 2020 30;2020:497-506. Epub 2020 May 30.

Department of Health Sciences Research, Mayo Clinic, Rochester, MN.

An important function of the patient record is to effectively and concisely communicate patient problems. In many cases, these problems are represented as short textual summarizations and appear in various sections of the record including problem lists, diagnoses, and chief complaints. While free-text problem descriptions effectively capture the clinicians' intent, these unstructured representations are problematic for downstream analytics. Read More

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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7233039PMC

Mental Health Severity Detection from Psychological Forum Data using Domain-Specific Unlabelled Data.

AMIA Jt Summits Transl Sci Proc 2020 30;2020:487-496. Epub 2020 May 30.

Department of Biostatistics and Data Science, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX.

Mental health has become a growing concern in the medical field, yet remains difficult to study due to both privacy concerns and the lack of objectively quantifiable measurements (e.g., lab tests, physical exams). Read More

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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7233051PMC

A Secondary Analysis of Panoramic Radiographs Reveals Hotspots in the Maxillofacial Region Associated with Diabetes.

AMIA Jt Summits Transl Sci Proc 2020 30;2020:477-486. Epub 2020 May 30.

Center for Oral and Systemic Health, Marshfield Clinic, Marshfield, WI.

Diabetes mellitus is the putative cause of a number of pathologies occurring in the bony and soft tissues of the maxillo-facial region and is known to exacerbate other oral diseases such as periodontitis.We present the first use of clinical panoramic radiographs for a secondary analysis of disease, with a focus on identifying hotspots in the maxillofacial region that are associated with diabetes. We developed a curated data set using Consensus Landmark Points (CLPs) and used that data to develop an analysis pipeline. Read More

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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7233101PMC

Feature Engineering and Process Mining to Enable Hazard Detection in Health Information Technology.

AMIA Jt Summits Transl Sci Proc 2020 30;2020:469-476. Epub 2020 May 30.

US Department of Veterans Affairs, Washington, DC, USA.

In this work, we aim to enhance the reliability of health information technology (HIT) systems by detection of plausible HIT hazards in clinical order transactions. In the absence of well-defined event logs in corporate data warehouses, our proposed approach identifies relevant timestamped data fields that could indicate transactions in the clinical order life cycle generating raw event sequences. Subsequently, we adopt state transitions of the OASIS Human Task standard to map the raw event sequences and simplify the complex process that clinical radiology orders go through. Read More

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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7233031PMC

Local Topic Mining for Reflective Medical Writing.

AMIA Jt Summits Transl Sci Proc 2020 30;2020:459-468. Epub 2020 May 30.

Virginia Commonwealth University Health System, Richmond, VA, USA.

Reflective writing is used by medical educators to identify challenges and promote inter-professional skills. These non-medical skills are central to leadership and career development, and are clinically relevant and vital to a trainees success as a practicing physician. However, identification of actionable feedback from reflective writings can be chal- lenging. Read More

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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7233034PMC

Predicting Polypharmacy Side-effects Using Knowledge Graph Embeddings.

AMIA Jt Summits Transl Sci Proc 2020 30;2020:449-458. Epub 2020 May 30.

Data Science Institute, NUI Galway.

Polypharmacy is the use of drug combinations and is commonly used for treating complex and terminal diseases. Despite its effectiveness in many cases, it poses high risks of adverse side effects. Polypharmacy side-effects occur due to unwanted interactions of combined drugs, and they can cause severe complications to patients which results in increasing the risks of morbidity and leading to new mortalities. Read More

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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7233093PMC

Building Cancer Diagnosis Text to OncoTree Mapping Pipelines for Clinical Sequencing Data Integration and Curation.

AMIA Jt Summits Transl Sci Proc 2020 30;2020:440-448. Epub 2020 May 30.

Wake Forest Baptist Medical Center, Winston Salem, NC.

Precision oncology research seeks to derive knowledge from existing data. Current work seeks to integrate clinical and genomic data across cancer centers to enable impactful secondary use. However, integrated data reliability depends on the data curation method used and its systematicity. Read More

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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7233083PMC

Predicting The Effects of Chemical-Protein Interactions On Proteins Using Tensor Factorisation.

AMIA Jt Summits Transl Sci Proc 2020 30;2020:430-439. Epub 2020 May 30.

Insight Centre for Data Analytics, NUI Galway, Galway, Ireland.

Understanding the different effects of chemical substances on human proteins is fundamental for designing new drugs. It is also important for elucidating the different mechanisms of action of drugs that can cause side-effects. In this context, computational methods for predicting chemical-protein interactions can provide valuable insights on the relation between therapeutic chemical substances and proteins. Read More

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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7233103PMC

Mapping Local Biospecimen Records to the OMOP Common Data Model.

AMIA Jt Summits Transl Sci Proc 2020 30;2020:422-429. Epub 2020 May 30.

Department of Healthcare Policy & Research, Weill Cornell Medicine, New York, NY.

Research to support precision medicine for leukemia patients requires integration of biospecimen and clinical data. The Observational Medical Outcomes Partnership common data model (OMOP CDM) and its Specimen table presents a potential solution. Although researchers have described progress and challenges in mapping electronic health record (EHR) data to populate the OMOP CDM, to our knowledge no studies have described populating the OMOP CDM with biospecimen data. Read More

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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7233045PMC

Self-Supervised Contextual Language Representation of Radiology Reports to Improve the Identification of Communication Urgency.

AMIA Jt Summits Transl Sci Proc 2020 30;2020:413-421. Epub 2020 May 30.

Computer Science Department, Dartmouth College, Hanover, NH 03755, USA.

Machine learning methods have recently achieved high-performance in biomedical text analysis. However, a major bottleneck in the widespread application of these methods is obtaining the required large amounts of annotated training data, which is resource intensive and time consuming. Recent progress in self-supervised learning has shown promise in leveraging large text corpora without explicit annotations. Read More

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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7233055PMC

Exploring Novel Computable Knowledge in Structured Drug Product Labels.

AMIA Jt Summits Transl Sci Proc 2020 30;2020:403-412. Epub 2020 May 30.

University of Pittsburgh Department of Biomedical Informatics, Pittsburgh, PA.

This paper introduces a database derived from Structured Product Labels (SPLs). SPLs are legally mandated snapshots containing information on all drugs released to market in the United States. Since publication is not required for pre-trial findings, we hypothesize that SPLs may contain knowledge absent in the literature, and hence "novel. Read More

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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7233092PMC

Automated Analysis of Public Health Laboratory Test Results.

AMIA Jt Summits Transl Sci Proc 2020 30;2020:393-402. Epub 2020 May 30.

University of British Columbia, Vancouver, British Columbia, Canada.

This study investigates the use of machine learning methods for classifying and extracting structured information from laboratory reports stored as semi-structured point-form English text. This is a novel data format that has not been evaluated in conjunction with machine learning classifiers in previous literature. Our classifiers achieve human-level predictive accuracy on the binary Test Performed and 4-class Test Outcome labels. Read More

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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7233052PMC

Sharing is Caring: Exploring machine learning-enabled methods for regional medical imaging exchange using procedure metadata.

AMIA Jt Summits Transl Sci Proc 2020 30;2020:383-392. Epub 2020 May 30.

University of Toronto, Toronto, Ontario Canada.

Seamless sharing between imaging facilities of medical images obtained on the same patient is crucial in providing accurate and efficient care to patients. However, the terminology used to describe semantically similar examinations can vary widely between facilities. Current practice is manual table-based mapping to a standard terminology, which has substantial potential for mislabelled and missing examinations. Read More

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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7233030PMC

Using Entity Metrics to Understand Drug Repurposing.

AMIA Jt Summits Transl Sci Proc 2020 30;2020:377-382. Epub 2020 May 30.

School of Information Management, Wuhan University, Wuhan, Hubei, China.

Understanding the process of drug repurposing is critically significant for drug development. In this paper, we employ extracted bio-entities to detect the features of different phases in drug repurposing. We proposed a transparent and easy entitymetric indicator for bio-entities, i. Read More

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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7233087PMC

Predicting Clinical Outcomes with Patient Stratification via Deep Mixture Neural Networks.

AMIA Jt Summits Transl Sci Proc 2020 30;2020:367-376. Epub 2020 May 30.

Department of Computer Science.

The increasing availability of electronic health record data offers unprecedented opportunities for predictive modeling in healthcare informatics including outcomes such as mortality and disease diagnosis as well as risk factor identification. Recently, deep neural networks (DNNs) have been successfully applied in healthcare informatics and achieved state-of-art predictive performance. However, existing DNN models either rely on the pre-defined patient subgroups or take the "one-size-fits-all" approach and are built without considering patient stratification. Read More

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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7233047PMC

Mining Misdiagnosis Patterns from Biomedical Literature.

AMIA Jt Summits Transl Sci Proc 2020 30;2020:360-366. Epub 2020 May 30.

Center for Biomedical Informatics, Brown University, Providence, RI, United States.

Diagnostic errors can pose a serious threat to patient safety, leading to serious harm and even death. Efforts are being made to develop interventions that allow physicians to reassess for errors and improve diagnostic accuracy. Our study presents an exploration of misdiagnosis patterns mined from PubMed abstracts. Read More

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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7233073PMC

JITA: A Platform for Enabling Real Time Point-of-Care Patient Recruitment.

AMIA Jt Summits Transl Sci Proc 2020 30;2020:355-359. Epub 2020 May 30.

University of Massachusetts Medical School, Worcester, MA 01655, USA.

Timely accrual continues to be a challenge in clinical trials. The evolution of Electronic Health Record systems and cohort selection tools like i2b2 have improved identification of potential candidate participants. However, delays in receiving relevant patient information and lack of real time patient identification cause difficulty in meeting recruitment targets. Read More

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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7233033PMC

Normalizing Adverse Events using Recurrent Neural Networks with Attention.

AMIA Jt Summits Transl Sci Proc 2020 30;2020:345-354. Epub 2020 May 30.

George Mason University, Fairfax, VA, USA.

Adverse events (AEs) are undesirable outcomes of medication administration and cause many hospitalizations as well as even deaths per year. Information about AEs can enable their prevention. Natural language processing (NLP) techniques can identify AEs from narratives and match them to a structured terminology. Read More

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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7233057PMC

Extraction and Analysis of Clinically Important Follow-up Recommendations in a Large Radiology Dataset.

AMIA Jt Summits Transl Sci Proc 2020 30;2020:335-344. Epub 2020 May 30.

Department of Biomedical and Health Informatics, University of Washington, Seattle, WA.

Communication of follow-up recommendations when abnormalities are identified on imaging studies is prone to error. In this paper, we present a natural language processing approach based on deep learning to automatically identify clinically important recommendations in radiology reports. Our approach first identifies the recommendation sentences and then extracts reason, test, and time frame of the identified recommendations. Read More

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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7233090PMC

Standardized Architecture for a Mega-Biobank Phenomic Library: The Million Veteran Program (MVP).

AMIA Jt Summits Transl Sci Proc 2020 30;2020:326-334. Epub 2020 May 30.

Division of Population Health and Data Science, Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, MA.

Electronic health records (EHRs) provide a wealth of data for phenotype development in population health studies, and researchers invest considerable time to curate data elements and validate disease definitions. The ability to reproduce well-defined phenotypes increases data quality, comparability of results and expedites research. In this paper, we present a standardized approach to organize and capture phenotype definitions, resulting in the creation of an open, online repository of phenotypes. Read More

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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7233040PMC

Automatically Identifying Comparator Groups on Twitter for Digital Epidemiology of Pregnancy Outcomes.

AMIA Jt Summits Transl Sci Proc 2020 30;2020:317-325. Epub 2020 May 30.

Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.

Despite the prevalence of adverse pregnancy outcomes such as miscarriage, stillbirth, birth defects, and preterm birth, their causes are largely unknown. We seek to advance the use of social media for observational studies of pregnancy outcomes by developing a natural language processing pipeline for automatically identifying users from which to select comparator groups on Twitter. We annotated 2361 tweets by users who have announced their pregnancy on Twitter, which were used to train and evaluate supervised machine learning algorithms as a basis for automatically detecting women who have reported that their pregnancy had reached term and their baby was born at a normal weight. Read More

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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7233041PMC

Developing a real-time EHR-integrated SDoH clinical tool.

AMIA Jt Summits Transl Sci Proc 2020 30;2020:308-316. Epub 2020 May 30.

Dell Medical School at UT-Austin, Austin, TX, USA.

We describe an implementation of a pilot integration to embed SDoH-based data visualizations into the EHR in real time for clinical staff treating children with asthma. Read More

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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7233042PMC

The accuracy vs. coverage trade-off in patient-facing diagnosis models.

AMIA Jt Summits Transl Sci Proc 2020 30;2020:298-307. Epub 2020 May 30.

Curai, Palo Alto, CA, USA.

A third of adults in America use the Internet to diagnose medical concerns, and online symptom checkers are increasingly part of this process. These tools are powered by diagnosis models similar to clinical decision support systems, with the primary difference being the coverage of symptoms and diagnoses. To be useful to patients and physicians, these models must have high accuracy while covering a meaningful space of symptoms and diagnoses. Read More

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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7233065PMC