Representing and utilizing clinical textual data for real world studies: An OHDSI approach
- Vipina K. Keloth,
- Juan M. Banda,
- Michael Gurley,
- Paul M. Heider,
- Georgina Kennedy,
- Hongfang Liu,
- Feifan Liu,
- Timothy Miller,
- Karthik Natarajan,
- Olga V Patterson,
- Yifan Peng,
- Kalpana Raja,
- Ruth M. Reeves,
- Masoud Rouhizadeh,
- Jianlin Shi,
- Xiaoyan Wang,
- Yanshan Wang,
- Wei-Qi Wei,
- Andrew E. Williams,
- Rui Zhang,
- Rimma Belenkaya,
- Christian Reich,
- Clair Blacketer,
- Patrick Ryan,
- George Hripcsak,
- Noémie Elhadad,
- Hua Xu
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AbstractClinical documentation in electronic health records contains crucial narratives and details about patients and their care. Natural language processing (NLP) can unlock the information conveyed in clinical notes and reports, and thus plays a ...
Novel evaluation approach for molecular signature-based deconvolution methods
- Agustín Nava,
- Daniela Alves da Quinta,
- Laura Prato,
- María Romina Girotti,
- Gabriel Moron,
- Andrea S. Llera,
- Elmer A. Fernández
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Highlights
- Current benchmarks for reference-based cell-type deconvolution methods are mainly based on metrics that focus on the linear relationship of their estimations with the ground truth or overall bias but not accounting for cell-type detection ...
The tumoral immune microenvironment (TIME) plays a key role in prognosis, therapeutic approach and pathophysiological understanding over oncological processes. Several computational immune cell-type deconvolution methods (DM), supported by ...
A community-of-practice-based evaluation methodology for knowledge intensive computational methods and its application to multimorbidity decision support
- William Van Woensel,
- Samson W. Tu,
- Wojtek Michalowski,
- Syed Sibte Raza Abidi,
- Samina Abidi,
- Jose-Ramon Alonso,
- Alessio Bottrighi,
- Marc Carrier,
- Ruth Edry,
- Irit Hochberg,
- Malvika Rao,
- Stephen Kingwell,
- Alexandra Kogan,
- Mar Marcos,
- Begoña Martínez Salvador,
- Martin Michalowski,
- Luca Piovesan,
- David Riaño,
- Paolo Terenziani,
- Szymon Wilk,
- Mor Peleg
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Abstract ObjectiveThe study has dual objectives. Our first objective (1) is to develop a community-of-practice-based evaluation methodology for knowledge-intensive computational methods. We target a whitebox analysis of the computational methods to gain ...
Development of a somatic variant registry in a National Cancer Center: towards Molecular Real World Data preparedness
- Martina Betti,
- Chiara Maria Salzano,
- Alice Massacci,
- Mattia D'Antonio,
- Isabella Grassucci,
- Benedetta Marcozzi,
- Marco Canfora,
- Elisa Melucci,
- Simonetta Buglioni,
- Beatrice Casini,
- Enzo Gallo,
- Edoardo Pescarmona,
- Gennaro Ciliberto,
- Matteo Pallocca
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AbstractThe Biomedical Research field is currently advancing to develop Clinical Trials and translational projects based on Real World Evidence. To make this transition feasible, clinical centers need to work toward Data Accessibility and ...
Progress Note Understanding — Assessment and Plan Reasoning: Overview of the 2022 N2C2 Track 3 shared task
Daily progress notes are a common note type in the electronic health record (EHR) where healthcare providers document the patient’s daily progress and treatment plans. The EHR is designed to document all the care provided to patients, but it also ...
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Contextualized medication information extraction using Transformer-based deep learning architectures
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Highlights
- Transformer-based NLP systems for medication extraction and event/context classification.
- Comparison of 6 pretrained state-of-the-art transformer models.
- Top performance from GatorTron model - trained using over 90 billion words of ...
To develop a natural language processing (NLP) system to extract medications and contextual information that help understand drug changes. This project is part of the 2022 n2c2 challenge.
Materials and methodsWe developed NLP systems ...
A method for extracting tumor events from clinical CT examination reports
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AbstractAccurate and efficient extraction of key information related to diseases from medical examination reports, such as X-ray and ultrasound images, CT scans, and others, is crucial for accurate diagnosis and treatment. These reports provide a ...
Lessons learned to enable question answering on knowledge graphs extracted from scientific publications: A case study on the coronavirus literature
The article presents a workflow to create a question-answering system whose knowledge base combines knowledge graphs and scientific publications on coronaviruses. It is based on the experience gained in modeling evidence from research articles to ...
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Highlights
- Systematize the processing of scientific corpora to build knowledge graphs.
- Identify and standardize entities mentioned in scientific texts about Coronavirus.
- Formally describe textual evidences based on co-occurrences in ...
Deep learning to refine the identification of high-quality clinical research articles from the biomedical literature: Performance evaluation
- Cynthia Lokker,
- Elham Bagheri,
- Wael Abdelkader,
- Rick Parrish,
- Muhammad Afzal,
- Tamara Navarro,
- Chris Cotoi,
- Federico Germini,
- Lori Linkins,
- R. Brian Haynes,
- Lingyang Chu,
- Alfonso Iorio
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Abstract BackgroundIdentifying practice-ready evidence-based journal articles in medicine is a challenge due to the sheer volume of biomedical research publications. Newer approaches to support evidence discovery apply deep learning techniques to improve ...
Learning to rank query expansion terms for COVID-19 scholarly search
With the onset of the Coronavirus Disease 2019 (COVID-19) pandemic, there has been a surge in the number of publicly available biomedical information sources, which makes it an increasingly challenging research goal to retrieve a ...
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Highlights
- CQED formalizes an effective search over PubMed for COVID-19 topics.
- Model uses a BERT and a UMLSBERT to expand queries posed to PubMed.
- CQED train a neural model to effectively re-rank expansion terms.
- In comparison to ...
From centralized to ad-hoc knowledge base construction for hypotheses generation
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Abstract ObjectiveTo demonstrate and develop an approach enabling individual researchers or small teams to create their own ad-hoc, lightweight knowledge bases tailored for specialized scientific interests, using text-mining over scientific literature, ...
Left ventricle segmentation combining deep learning and deformable models with anatomical constraints
Segmentation of the left ventricle is a key approach in Cardiac Magnetic Resonance Imaging for calculating biomarkers in diagnosis. Since there is substantial effort required from the expert, many automatic segmentation methods have been proposed,...
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Highlights
- A fully-automatic hybrid approach combining deep learning and level set.
- Anatomical and exam-specific adaptation based on deep learning segmentation.
- The method’s steps can be adapted for different segmentation contexts.
- ...
Meta-analysis informed machine learning: Supporting cytokine storm detection during CAR-T cell Therapy
- Alex Bogatu,
- Magdalena Wysocka,
- Oskar Wysocki,
- Holly Butterworth,
- Manon Pillai,
- Jennifer Allison,
- Dónal Landers,
- Elaine Kilgour,
- Fiona Thistlethwaite,
- André Freitas
Cytokine release syndrome (CRS), also known as cytokine storm, is one of the most consequential adverse effects of chimeric antigen receptor therapies that have shown otherwise promising results in cancer treatment. When emerging, CRS could be ...
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Highlights
- A metareview-based diagnosis process that maximizes the use of external information.
- A data augmentation method that addresses the data scarcity problem in healthcare.
- An ML-based diagnosis process that offer predictions with a ...
Causal feature selection using a knowledge graph combining structured knowledge from the biomedical literature and ontologies: A use case studying depression as a risk factor for Alzheimer’s disease
- Scott A. Malec,
- Sanya B. Taneja,
- Steven M. Albert,
- C. Elizabeth Shaaban,
- Helmet T. Karim,
- Arthur S. Levine,
- Paul Munro,
- Tiffany J. Callahan,
- Richard D. Boyce
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Highlights
- Knowledge of causal variables and their roles is essential for causal inference.
- We show how to search a knowledge graph (KG) for causal variables and their roles.
- The KG combines literature-derived knowledge with ontology-grounded ...
Causal feature selection is essential for estimating effects from observational data. Identifying confounders is a crucial step in this process. Traditionally, researchers employ content-matter expertise and literature review to ...
Conceptual framework and documentation standards of cystoscopic media content for artificial intelligence
- Okyaz Eminaga,
- Timothy Jiyong Lee,
- Jessie Ge,
- Eugene Shkolyar,
- Mark Laurie,
- Jin Long,
- Lukas Graham Hockman,
- Joseph C. Liao
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Highlights
- The secondary use of visual cystoscopic data for research and education is limited.
- We therefore propose a conceptual framework for visual cystoscopic data.
- Our framework facilitates the curation of FAIR data for cystoscopy.
- ...
The clinical documentation of cystoscopy includes visual and textual materials. However, the secondary use of visual cystoscopic data for educational and research purposes remains limited due to inefficient data management in routine ...
A robust phenotype-driven likelihood ratio analysis approach assisting interpretable clinical diagnosis of rare diseases
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Highlights
- Estimating the point prevalence value of rare diseases.
- Propagation protocol to overcome imprecise phenotype.
- Likelihood ratio was used to drive phenotype based rare disease diagnose.
- PheLR shows significant advantages over ...
Phenotype-based prioritization of candidate genes and diseases has become a well-established approach for multi-omics diagnostics of rare diseases. Most current algorithms exploit semantic analysis and probabilistic statistics based on Human ...
HLA amino acid Mismatch-Based risk stratification of kidney allograft failure using a novel Machine learning algorithm
- Satvik Dasariraju,
- Loren Gragert,
- Grace L. Wager,
- Keith McCullough,
- Nicholas K. Brown,
- Malek Kamoun,
- Ryan J. Urbanowicz
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Abstract ObjectiveWhile associations between HLA antigen-level mismatches (Ag-MM) and kidney allograft failure are well established, HLA amino acid-level mismatches (AA-MM) have been less explored. Ag-MM fails to consider the substantial variability in ...
A data-driven approach to optimizing clinical study eligibility criteria
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Abstract ObjectiveFeasible, safe, and inclusive eligibility criteria are crucial to successful clinical research recruitment. Existing expert-centered methods for eligibility criteria selection may not be representative of real-world populations. This ...
Offline reinforcement learning for safer blood glucose control in people with type 1 diabetes
The widespread adoption of effective hybrid closed loop systems would represent an important milestone of care for people living with type 1 diabetes (T1D). These devices typically utilise simple control algorithms to select the optimal insulin ...
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Sequential data mining of infection patterns as predictors for onset of type 1 diabetes in genetically at-risk individuals
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AbstractInfections are implicated in the etiology of type 1 diabetes mellitus (T1DM); however, conflicting epidemiologic evidence makes designing effective strategies for presymptomatic screening and disease prevention difficult. Considering the ...
ViPal: A framework for virulence prediction of influenza viruses with prior viral knowledge using genomic sequences
Influenza viruses pose great threats to public health and cause enormous economic losses every year. Previous work has revealed the viral factors associated with the virulence of influenza viruses in mammals. However, taking prior viral knowledge ...
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A systematic review of computational approaches to understand cancer biology for informed drug repurposing
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AbstractCancer is the second leading cause of death globally, trailing only heart disease. In the United States alone, 1.9 million new cancer cases and 609,360 deaths were recorded for 2022. Unfortunately, the success rate for new cancer drug development ...
The use of artificial intelligence for automating or semi-automating biomedical literature analyses: A scoping review
- Álisson Oliveira dos Santos,
- Eduardo Sergio da Silva,
- Letícia Machado Couto,
- Gustavo Valadares Labanca Reis,
- Vinícius Silva Belo
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Highlights
- Artificial intelligence (AI) is used widely in evidence-based medicine.
- AI is commonly applied to evidence assembly, literature mining & quality analysis.
- Automated preparation of systematic reviews is the most researched area.
Evidence-based medicine (EBM) is a decision-making process based on the conscious and judicious use of the best available scientific evidence. However, the exponential increase in the amount of information currently available likely ...