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Computational frameworks integrating deep learning and statistical models in mining multimodal omics data
Computational frameworks integrating deep learning and statistical models in mining multimodal omics data
Recent widespread availability of health-related data (e.g., omics data) has accelerated the growth of big data analytics ...
Model Tuning or Prompt Tuning? A Study of Large Language Models for Clinical Concept and Relation Extraction
Model Tuning or Prompt Tuning? A Study of Large Language Models for Clinical Concept and Relation Extraction
In the recent decade, natural language processing (NLP) has experienced a change from fully supervised learning to pretrai...
Automatic categorization of self-acknowledged limitations in randomized controlled trial publications
Automatic categorization of self-acknowledged limitations in randomized controlled trial publications
Objective:Acknowledging study limitations in a scientific publication is a crucial element in scientific transparency and ...
Participant flow diagrams for health equity in AI
Participant flow diagrams for health equity in AI
An appropriate patient sample is essential to the integrity of any type of medical research. In observational studies, usi...
ParTRE: A relational triple extraction model of complicated entities and imbalanced relations in Parkinson’s disease
ParTRE: A relational triple extraction model of complicated entities and imbalanced relations in Parkinson’s disease
Parkinson’s disease (PD), characterized by static tremors and slow movements, is the world’s second most common progressiv...
Evaluation of ChatGPT-generated medical responses: A systematic review and meta-analysis
Evaluation of ChatGPT-generated medical responses: A systematic review and meta-analysis
In recent years, large language models (LLMs) have attracted attention for their potential to improve traditional approach...
FedFSA: Hybrid and federated framework for functional status ascertainment across institutions
FedFSA: Hybrid and federated framework for functional status ascertainment across institutions
Patients' functional status assesses their independence in performing activities of daily living (ADL), including basic AD...
Graph neural networks for clinical risk prediction based on electronic health records: A survey
Graph neural networks for clinical risk prediction based on electronic health records: A survey
Electronic health records (EHRs) are extensive, heterogeneous, and longitudinal repositories that document patients’ healt...
Soft phenotyping for sepsis via EHR time-aware soft clustering
Soft phenotyping for sepsis via EHR time-aware soft clustering
Sepsis is a life-threatening organ dysfunction syndrome secondary to a dysregulated host response to infection, and the pr...
Improving the interoperability of drugs terminologies: Infusing local standardization with an international perspective
Improving the interoperability of drugs terminologies: Infusing local standardization with an international perspective
Volume 151, March 2024, 104614Author links open overlay panel, , , , AbstractObjectives:The objective of this study is to ...
Temporal attention networks for biomedical hypothesis generation
Temporal attention networks for biomedical hypothesis generation
A large body of publicly accessible literature has become the driving force for research innovation. New, meaningful impli...
Multi-view representation learning for tabular data integration using inter-feature relationships
Multi-view representation learning for tabular data integration using inter-feature relationships
Objective:An applied problem facing all areas of data science is harmonizing data sources. Joining data from multiple orig...
Extracting adverse drug events from clinical Notes: A systematic review of approaches used
Extracting adverse drug events from clinical Notes: A systematic review of approaches used
Adverse drug events (ADEs), according to the World Health Organization (WHO), are defined as any injury or unfavorable con...
SPeC: A soft prompt-based calibration on performance variability of large language model in clinical notes summarization
SPeC: A soft prompt-based calibration on performance variability of large language model in clinical notes summarization
Electronic health records (EHRs) have brought about a revolutionary change in the accessibility and utilization of patient...
EHR-BERT: A BERT-based model for effective anomaly detection in electronic health records
EHR-BERT: A BERT-based model for effective anomaly detection in electronic health records
The evolving landscape of healthcare is increasingly leaning on health information technology (HIT) to enhance patient out...
CLARUS: An interactive explainable AI platform for manual counterfactuals in graph neural networks
CLARUS: An interactive explainable AI platform for manual counterfactuals in graph neural networks
Project Homepage: http://rshiny.gwdg.de/apps/clarus/ The datasets generated and analysed during the current study are avai...
Adjusting for false discoveries in constraint-based differential metabolic flux analysis
Adjusting for false discoveries in constraint-based differential metabolic flux analysis
One of the critical steps to characterize metabolic alterations in multifactorial diseases, as well as their heterogeneity...
CMBEE:A constraint-based multi-task learning framework for biomedical event extraction
CMBEE:A constraint-based multi-task learning framework for biomedical event extraction
Biomedical event extraction is a crucial task in the field of biomedical applications, as it helps to convert unstructured...
Clinical natural language processing for secondary uses
Clinical natural language processing for secondary uses
Electronic health records (EHRs) have the potential to significantly enhance quality improvement efforts and surveillance ...
Useful blunders: Can automated speech recognition errors improve downstream dementia classification?
Useful blunders: Can automated speech recognition errors improve downstream dementia classification?
Alzheimer’s disease (AD) is a neurodegenerative disorder that affects the use of speech and language and is difficult to d...
One-shot distributed algorithms for addressing heterogeneity in competing risks data across clinical sites
One-shot distributed algorithms for addressing heterogeneity in competing risks data across clinical sites
Real-world evidence (RWE) refers to data collected in environments outside of the controlled setting of randomized clinica...
Semantics-enabled Biomedical Literature Analytics
Semantics-enabled Biomedical Literature Analytics
Due to the large size and exponential growth of the number of scientific articles published in the biomedical domain, obta...
Development of a 3-Step theory of suicide ontology to facilitate 3ST factor extraction from clinical progress notes
Development of a 3-Step theory of suicide ontology to facilitate 3ST factor extraction from clinical progress notes
The Veterans Health Administration (VHA) uses suicide risk prediction algorithms to support clinical decision making for t...
Few-shot learning based oral cancer diagnosis using a dual feature extractor prototypical network
Few-shot learning based oral cancer diagnosis using a dual feature extractor prototypical network
Available online 8 January 2024, 104584Author links open overlay panel, , AbstractA large global health issue is cancer, w...
Retrieval augmentation of large language models for lay language generation
Retrieval augmentation of large language models for lay language generation
The COVID-19 pandemic underscored the difficulties the general public faces when attempting to use scientific information ...
Counterfactual formulation of patient-specific root causes of disease
Counterfactual formulation of patient-specific root causes of disease
Root causes of disease intuitively correspond to root vertices that increase the likelihood of a diagnosis. Clinicians ass...