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Found 14 papers, 6 papers with code
Interpreting Differentiable Latent States for Healthcare Time-series Data
Understanding the meaning of latent states is crucial for interpreting machine learning models, assuming they capture underlying patterns.
Lightweight Transformers for Clinical Natural Language Processing
To our knowledge, this is the first comprehensive study specifically focused on creating efficient and compact transformers for clinical NLP tasks.
G-CMP: Graph-enhanced Contextual Matrix Profile for unsupervised anomaly detection in sensor-based remote health monitoring
The Contextual Matrix Profile (CMP) is a configurable 2-dimensional version of the Matrix Profile (MP) that uses the distance matrix of all subsequences of a time series to discover patterns and anomalies.
MiniALBERT: Model Distillation via Parameter-Efficient Recursive Transformers
Different strategies have been proposed in the literature to alleviate these problems, with the aim to create effective compact models that nearly match the performance of their bloated counterparts with negligible performance losses.
On the Effectiveness of Compact Biomedical Transformers
Language models pre-trained on biomedical corpora, such as BioBERT, have recently shown promising results on downstream biomedical tasks.
Ranked #2 on
Named Entity Recognition (NER)
on BC2GM
Privacy-aware Early Detection of COVID-19 through Adversarial Training
One of our main contributions is that we specifically target the development of effective COVID-19 detection models with built-in mechanisms in order to selectively protect sensitive attributes against adversarial attacks.
Designing A Clinically Applicable Deep Recurrent Model to Identify Neuropsychiatric Symptoms in People Living with Dementia Using In-Home Monitoring Data
Agitation is one of the neuropsychiatric symptoms with high prevalence in dementia which can negatively impact the Activities of Daily Living (ADL) and the independence of individuals.
When Complexity Is Good: Do We Need Recurrent Deep Learning For Time Series Outlier Detection?
Recurrent deep learning methods have a larger capacity for learning complex representations in time series data.
Folded Hamiltonian Monte Carlo for Bayesian Generative Adversarial Networks
Generative Adversarial Networks (GANs) can learn complex distributions over images, audio, and data that are difficult to model.
Semi-supervised Learning for Identifying the Likelihood of Agitation in People with Dementia
We train and test the proposed model on a dataset from a clinical study.
A Hamiltonian Monte Carlo Model for Imputation and Augmentation of Healthcare Data
A Bayesian approach to impute missing values and creating augmented samples in high dimensional healthcare data is proposed in this work.
Deep Representation for Connected Health: Semi-supervised Learning for Analysing the Risk of Urinary Tract Infections in People with Dementia
Machine learning techniques combined with in-home monitoring technologies provide a unique opportunity to automate diagnosis and early detection of adverse health conditions in long-term conditions such as dementia.
Bridging the Gap: Attending to Discontinuity in Identification of Multiword Expressions
We introduce a new method to tag Multiword Expressions (MWEs) using a linguistically interpretable language-independent deep learning architecture.
Visualisation of Survey Responses using Self-Organising Maps: A Case Study on Diabetes Self-care Factors
Due to the chronic nature of diabetes, patient self-care factors play an important role in any treatment plan.
