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Found 15 papers, 8 papers with code
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.
TSI-Bench: Benchmarking Time Series Imputation
Despite the development of numerous deep learning algorithms for time series imputation, the community lacks standardized and comprehensive benchmark platforms to effectively evaluate imputation performance across different settings.
Unveiling the Secrets: How Masking Strategies Shape Time Series Imputation
In this study, we explore the impact of different masking strategies on time series imputation models.
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.
Knowledge Enhanced Conditional Imputation for Healthcare Time-series
This study presents a novel approach to addressing the challenge of missing data in multivariate time series, with a particular focus on the complexities of healthcare data.
Exploring Multimodal Large Language Models for Radiology Report Error-checking
This paper proposes one of the first clinical applications of multimodal large language models (LLMs) as an assistant for radiologists to check errors in their reports.
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.
Estimating Redundancy in Clinical Text
This work is a quantitative examination of information redundancy in EHR notes.
Multi-domain Clinical Natural Language Processing with MedCAT: the Medical Concept Annotation Toolkit
Electronic health records (EHR) contain large volumes of unstructured text, requiring the application of Information Extraction (IE) technologies to enable clinical analysis.
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.
Experimental Evaluation and Development of a Silver-Standard for the MIMIC-III Clinical Coding Dataset
Clinical coding is currently a labour-intensive, error-prone, but critical administrative process whereby hospital patient episodes are manually assigned codes by qualified staff from large, standardised taxonomic hierarchies of codes.
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.
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.
