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Found 16 papers, 4 papers with code
Improving Extraction of Clinical Event Contextual Properties from Electronic Health Records: A Comparative Study
Electronic Health Records are large repositories of valuable clinical data, with a significant portion stored in unstructured text format.
How Deep is your Guess? A Fresh Perspective on Deep Learning for Medical Time-Series Imputation
By identifying conceptual gaps in the literature and existing reviews, we devise a taxonomy grounded on the inductive bias of neural imputation frameworks, resulting in a classification of existing deep imputation strategies based on their suitability for specific imputation scenarios and data-specific properties.
Uncertainty-Aware Deep Attention Recurrent Neural Network for Heterogeneous Time Series Imputation
We propose DEep Attention Recurrent Imputation (DEARI), which jointly estimates missing values and their associated uncertainty in heterogeneous multivariate time series.
Validating transformers for redaction of text from electronic health records in real-world healthcare
Protecting patient privacy in healthcare records is a top priority, and redaction is a commonly used method for obscuring directly identifiable information in text.
Discharge Summary Hospital Course Summarisation of In Patient Electronic Health Record Text with Clinical Concept Guided Deep Pre-Trained Transformer Models
Brief Hospital Course (BHC) summaries are succinct summaries of an entire hospital encounter, embedded within discharge summaries, written by senior clinicians responsible for the overall care of a patient.
MedGPT: Medical Concept Prediction from Clinical Narratives
The data available in Electronic Health Records (EHRs) provides the opportunity to transform care, and the best way to provide better care for one patient is through learning from the data available on all other patients.
Comparing Natural Language Processing Techniques for Alzheimer's Dementia Prediction in Spontaneous Speech
We exclusively analyse the supplied textual transcripts of the spontaneous speech dataset, building and comparing performance across numerous models for the classification of AD vs controls and the prediction of Mental Mini State Exam scores.
Comparative Analysis of Text Classification Approaches in Electronic Health Records
Text classification tasks which aim at harvesting and/or organizing information from electronic health records are pivotal to support clinical and translational research.
Modeling Rare Interactions in Time Series Data Through Qualitative Change: Application to Outcome Prediction in Intensive Care Units
Many areas of research are characterised by the deluge of large-scale highly-dimensional time-series data.
Identifying physical health comorbidities in a cohort of individuals with severe mental illness: An application of SemEHR
Multimorbidity research in mental health services requires data from physical health conditions which is traditionally limited in mental health care electronic health records.
The side effect profile of Clozapine in real world data of three large mental hospitals
Further, the data was combined from three trusts, and chi-square tests were applied to estimate the average effect of ADRs in each monthly interval.
MedCAT -- Medical Concept Annotation Tool
To uncover the potential of biomedical documents we need to extract and structure the information they contain.
On Classifying Sepsis Heterogeneity in the ICU: Insight Using Machine Learning
Current machine learning models aiming to predict sepsis from Electronic Health Records (EHR) do not account for the heterogeneity of the condition, despite its emerging importance in prognosis and treatment.
MedCATTrainer: A Biomedical Free Text Annotation Interface with Active Learning and Research Use Case Specific Customisation
Secondary use of data for clinical research often has task and context specific criteria.
Application of Clinical Concept Embeddings for Heart Failure Prediction in UK EHR data
Electronic health records (EHR) are increasingly being used for constructing disease risk prediction models.
