Search Results for author: Tingting Zhu

Found 33 papers, 8 papers with code

DTR-Bench: An in silico Environment and Benchmark Platform for Reinforcement Learning Based Dynamic Treatment Regime

1 code implementation28 May 2024 Zhiyao Luo, Mingcheng Zhu, Fenglin Liu, Jiali Li, Yangchen Pan, Jiandong Zhou, Tingting Zhu

Our experiments reveal varying degrees of performance degradation among RL algorithms in the presence of noise and patient variability, with some algorithms failing to converge.

Benchmarking Reinforcement Learning (RL)

Reinforcement Learning in Dynamic Treatment Regimes Needs Critical Reexamination

1 code implementation28 May 2024 Zhiyao Luo, Yangchen Pan, Peter Watkinson, Tingting Zhu

In the rapidly changing healthcare landscape, the implementation of offline reinforcement learning (RL) in dynamic treatment regimes (DTRs) presents a mix of unprecedented opportunities and challenges.

Offline RL reinforcement-learning +2

A Survey of Few-Shot Learning for Biomedical Time Series

no code implementations3 May 2024 Chenqi Li, Timothy Denison, Tingting Zhu

Advancements in wearable sensor technologies and the digitization of medical records have contributed to the unprecedented ubiquity of biomedical time series data.

Few-Shot Learning Time Series

Understanding Missingness in Time-series Electronic Health Records for Individualized Representation

no code implementations24 Feb 2024 Ghadeer O. Ghosheh, Jin Li, Tingting Zhu

The lack of focus on missingness representation in an individualized way limits the full utilization of machine learning applications towards true personalization.

Time Series

A Perspective on Individualized Treatment Effects Estimation from Time-series Health Data

no code implementations7 Feb 2024 Ghadeer O. Ghosheh, Moritz Gögl, Tingting Zhu

To this end, this work provides an overview of ITE works for time-series data and insights into future research.

Time Series

IGNITE: Individualized GeNeration of Imputations in Time-series Electronic health records

1 code implementation9 Jan 2024 Ghadeer O. Ghosheh, Jin Li, Tingting Zhu

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.

Missing Values Time Series

DySurv: Dynamic Deep Learning Model for Survival Prediction in the ICU

no code implementations28 Oct 2023 Munib Mesinovic, Peter Watkinson, Tingting Zhu

Survival analysis focuses on estimating time-to-event distributions which can help in dynamic risk prediction in healthcare.

Deep Learning Survival Analysis +3

How to Evaluate Semantic Communications for Images with ViTScore Metric?

no code implementations9 Sep 2023 Tingting Zhu, Bo Peng, Jifan Liang, Tingchen Han, Hai Wan, Jingqiao Fu, Junjie Chen

To evaluate the performance of ViTScore, we compare ViTScore with 3 typical metrics (PSNR, MS-SSIM, and LPIPS) through 4 classes of experiments: (i) correlation with BERTScore through evaluation of image caption downstream CV task, (ii) evaluation in classical image communications, (iii) evaluation in image semantic communication systems, and (iv) evaluation in image semantic communication systems with semantic attack.

MS-SSIM Semantic Communication +3

Explainable AI for clinical risk prediction: a survey of concepts, methods, and modalities

no code implementations16 Aug 2023 Munib Mesinovic, Peter Watkinson, Tingting Zhu

To ensure trust and reliability in AI systems, especially in clinical risk prediction models, explainability becomes crucial.

Decision Making Explainable Models +1

Medical records condensation: a roadmap towards healthcare data democratisation

no code implementations5 May 2023 Yujiang Wang, Anshul Thakur, Mingzhi Dong, Pingchuan Ma, Stavros Petridis, Li Shang, Tingting Zhu, David A. Clifton

The prevalence of artificial intelligence (AI) has envisioned an era of healthcare democratisation that promises every stakeholder a new and better way of life.

Clinical Knowledge Dataset Condensation +2

Data Encoding For Healthcare Data Democratisation and Information Leakage Prevention

1 code implementation5 May 2023 Anshul Thakur, Tingting Zhu, Vinayak Abrol, Jacob Armstrong, Yujiang Wang, David A. Clifton

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.

Deep Learning Time Series

Adversarial De-confounding in Individualised Treatment Effects Estimation

no code implementations19 Oct 2022 Vinod Kumar Chauhan, Soheila Molaei, Marzia Hoque Tania, Anshul Thakur, Tingting Zhu, David A. Clifton

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.

counterfactual Counterfactual Inference

A review of Generative Adversarial Networks for Electronic Health Records: applications, evaluation measures and data sources

no code implementations14 Mar 2022 Ghadeer Ghosheh, Jin Li, Tingting Zhu

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.

BIG-bench Machine Learning

Generating Synthetic Mixed-type Longitudinal Electronic Health Records for Artificial Intelligent Applications

1 code implementation22 Dec 2021 Jin Li, Benjamin J. Cairns, Jingsong Li, Tingting Zhu

Synthetic data, which benefits from the development and proliferation of generative models, has served as a promising substitute for real patient EHR data.

Decision Making Generative Adversarial Network

Cluster-based Feature Importance Learning for Electronic Health Record Time-series

no code implementations29 Sep 2021 Henrique Aguiar, Mauro Santos, Peter Watkinson, Tingting Zhu

The recent availability of Electronic Health Records (EHR) has allowed for the development of algorithms predicting inpatient risk of deterioration and trajectory evolution.

Feature Importance Respiratory Failure +2

Towards Scheduling Federated Deep Learning using Meta-Gradients for Inter-Hospital Learning

no code implementations4 Jul 2021 Rasheed el-Bouri, Tingting Zhu, David A. Clifton

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.

Federated Learning Scheduling +1

DeepMI: Deep Multi-lead ECG Fusion for Identifying Myocardial Infarction and its Occurrence-time

no code implementations31 Mar 2021 Girmaw Abebe Tadesse, Hamza Javed, Yong liu, Jin Liu, Jiyan Chen, Komminist Weldemariam, Tingting Zhu

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.

Transfer Learning

Let Your Heart Speak in its Mother Tongue: Multilingual Captioning of Cardiac Signals

1 code implementation19 Mar 2021 Dani Kiyasseh, Tingting Zhu, David Clifton

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.

CROCS: Clustering and Retrieval of Cardiac Signals Based on Patient Disease Class, Sex, and Age

no code implementations NeurIPS 2021 Dani Kiyasseh, Tingting Zhu, David A. Clifton

The process of manually searching for relevant instances in, and extracting information from, clinical databases underpin a multitude of clinical tasks.

Clustering Contrastive Learning +2

PCPs: Patient Cardiac Prototypes

no code implementations28 Nov 2020 Dani Kiyasseh, Tingting Zhu, David A. Clifton

Many clinical deep learning algorithms are population-based and difficult to interpret.

Contrastive Learning Dataset Distillation

Phenotyping Clusters of Patient Trajectories suffering from Chronic Complex Disease

no code implementations17 Nov 2020 Henrique Aguiar, Mauro Santos, Peter Watkinson, Tingting Zhu

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.

Clustering Time Series +1

DROPS: Deep Retrieval of Physiological Signals via Attribute-specific Clinical Prototypes

no code implementations28 Sep 2020 Dani Kiyasseh, Tingting Zhu, David A. Clifton

The ongoing digitization of health records within the healthcare industry results in large-scale datasets.

Attribute Clustering +3

Student-Teacher Curriculum Learning via Reinforcement Learning: Predicting Hospital Inpatient Admission Location

no code implementations ICML 2020 Rasheed el-Bouri, David Eyre, Peter Watkinson, Tingting Zhu, David Clifton

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.

reinforcement-learning Reinforcement Learning (RL)

CLOCS: Contrastive Learning of Cardiac Signals Across Space, Time, and Patients

1 code implementation27 May 2020 Dani Kiyasseh, Tingting Zhu, David A. Clifton

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.

Contrastive Learning Linear evaluation

SoQal: Selective Oracle Questioning in Active Learning

no code implementations22 Apr 2020 Dani Kiyasseh, Tingting Zhu, David A. Clifton

Large sets of unlabelled data within the healthcare domain remain underutilized.

Active Learning

SoQal: Selective Oracle Questioning for Consistency Based Active Learning of Cardiac Signals

2 code implementations20 Apr 2020 Dani Kiyasseh, Tingting Zhu, David A. Clifton

One way to mitigate this burden is via active learning (AL) which involves the (a) acquisition and (b) annotation of informative unlabelled instances.

Active Learning Pseudo Label +1

CLOPS: Continual Learning of Physiological Signals

no code implementations20 Apr 2020 Dani Kiyasseh, Tingting Zhu, David A. Clifton

Deep learning algorithms are known to experience destructive interference when instances violate the assumption of being independent and identically distributed (i. i. d).

Continual Learning Multi-Task Learning

Fusing Continuous-valued Medical Labels using a Bayesian Model

no code implementations23 Mar 2015 Tingting Zhu, Nic Dunkley, Joachim Behar, David A. Clifton, Gari. D. Clifford

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.

Time Series Time Series Analysis

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