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Found 30 papers, 6 papers with code
Understanding Missingness in Time-series Electronic Health Records for Individualized Representation
The lack of focus on missingness representation in an individualized way limits the full utilization of machine learning applications towards true personalization.
A Perspective on Individualized Treatment Effects Estimation from Time-series Health Data
To this end, this work provides an overview of ITE works for time-series data and insights into future research.
IGNITE: Individualized GeNeration of Imputations in Time-series Electronic health records
In IGNITE, we further propose a novel individualized missingness mask (IMM), which helps our model generate values based on the individual's observed data and missingness patterns.
DySurv: Dynamic Deep Learning Model for Survival Prediction in the ICU
Survival analysis focuses on estimating time-to-event distributions which can help in dynamic risk prediction in healthcare.
How to Evaluate Semantic Communications for Images with ViTScore Metric?
Experimental results demonstrate that ViTScore can better evaluate the image semantic similarity than the other 3 typical metrics, which indicates that ViTScore is an effective performance metric when deployed in SC scenarios.
Explainable AI for clinical risk prediction: a survey of concepts, methods, and modalities
To ensure trust and reliability in AI systems, especially in clinical risk prediction models, explainability becomes crucial.
XMI-ICU: Explainable Machine Learning Model for Pseudo-Dynamic Prediction of Mortality in the ICU for Heart Attack Patients
Heart attack remain one of the greatest contributors to mortality in the United States and globally.
All models are local: time to replace external validation with recurrent local validation
We submit that external validation is insufficient to establish ML models' safety or utility.
Data Encoding For Healthcare Data Democratisation and Information Leakage Prevention
Experimental evaluation highlights that models trained on encoded time-series data effectively uphold the information bottleneck principle and hence, exhibit lesser information leakage from trained models.
Medical records condensation: a roadmap towards healthcare data democratisation
The prevalence of artificial intelligence (AI) has envisioned an era of healthcare democratisation that promises every stakeholder a new and better way of life.
Synthesizing Mixed-type Electronic Health Records using Diffusion Models
Electronic Health Records (EHRs) contain sensitive patient information, which presents privacy concerns when sharing such data.
Adversarial De-confounding in Individualised Treatment Effects Estimation
Observational studies have recently received significant attention from the machine learning community due to the increasingly available non-experimental observational data and the limitations of the experimental studies, such as considerable cost, impracticality, small and less representative sample sizes, etc.
A review of Generative Adversarial Networks for Electronic Health Records: applications, evaluation measures and data sources
Electronic Health Records (EHRs) are a valuable asset to facilitate clinical research and point of care applications; however, many challenges such as data privacy concerns impede its optimal utilization.
Generating Synthetic Mixed-type Longitudinal Electronic Health Records for Artificial Intelligent Applications
Synthetic data, which benefits from the development and proliferation of generative models, has served as a promising substitute for real patient EHR data.
Cluster-based Feature Importance Learning for Electronic Health Record Time-series
The recent availability of Electronic Health Records (EHR) has allowed for the development of algorithms predicting inpatient risk of deterioration and trajectory evolution.
Towards Scheduling Federated Deep Learning using Meta-Gradients for Inter-Hospital Learning
In this work, we aim to utilise patient data extracted from multiple hospital data centres to train a machine learning model without sacrificing patient privacy.
DeepMI: Deep Multi-lead ECG Fusion for Identifying Myocardial Infarction and its Occurrence-time
We propose an end-to-end deep learning approach, DeepMI, to classify MI from normal cases as well as identifying the time-occurrence of MI (defined as acute, recent and old), using a collection of fusion strategies on 12 ECG leads at data-, feature-, and decision-level.
Let Your Heart Speak in its Mother Tongue: Multilingual Captioning of Cardiac Signals
Cardiac signals, such as the electrocardiogram, convey a significant amount of information about the health status of a patient which is typically summarized by a clinician in the form of a clinical report, a cumbersome process that is prone to errors.
SoCal: Selective Oracle Questioning for Consistency-based Active Learning of Physiological Signals
The ubiquity and rate of collection of physiological signals produce large, unlabelled datasets.
PCPs: Patient Cardiac Prototypes
Many clinical deep learning algorithms are population-based and difficult to interpret.
CROCS: Clustering and Retrieval of Cardiac Signals Based on Patient Disease Class, Sex, and Age
The process of manually searching for relevant instances in, and extracting information from, clinical databases underpin a multitude of clinical tasks.
Phenotyping Clusters of Patient Trajectories suffering from Chronic Complex Disease
Recent years have seen an increased focus into the tasks of predicting hospital inpatient risk of deterioration and trajectory evolution due to the availability of electronic patient data.
DROPS: Deep Retrieval of Physiological Signals via Attribute-specific Clinical Prototypes
The ongoing digitization of health records within the healthcare industry results in large-scale datasets.
Student-Teacher Curriculum Learning via Reinforcement Learning: Predicting Hospital Inpatient Admission Location
Accurate and reliable prediction of hospital admission location is important due to resource-constraints and space availability in a clinical setting, particularly when dealing with patients who come from the emergency department.
CLOCS: Contrastive Learning of Cardiac Signals Across Space, Time, and Patients
This data can be exploited via contrastive learning, a self-supervised pre-training method that encourages representations of instances to be similar to one another.
SoQal: Selective Oracle Questioning in Active Learning
Large sets of unlabelled data within the healthcare domain remain underutilized.
SoQal: Selective Oracle Questioning for Consistency Based Active Learning of Cardiac Signals
One way to mitigate this burden is via active learning (AL) which involves the (a) acquisition and (b) annotation of informative unlabelled instances.
CLOPS: Continual Learning of Physiological Signals
Deep learning algorithms are known to experience destructive interference when instances violate the assumption of being independent and identically distributed (i. i. d).
Severity Detection Tool for Patients with Infectious Disease
A support vector machine is employed to classify the ANSD levels.
Fusing Continuous-valued Medical Labels using a Bayesian Model
To address these problems, a Bayesian Continuous-valued Label Aggregator(BCLA) is proposed to provide a reliable estimation of label aggregation while accurately infer the precision and bias of each algorithm.
