1 code implementation • 28 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.
1 code implementation • 28 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.
no code implementations • 3 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.
no code implementations • 24 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.
no code implementations • 7 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.
1 code implementation • 9 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.
no code implementations • 28 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.
no code implementations • 9 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.
no code implementations • 16 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.
no code implementations • 10 May 2023 • Munib Mesinovic, Peter Watkinson, Tingting Zhu
Heart attack remain one of the greatest contributors to mortality in the United States and globally.
no code implementations • 5 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.
1 code implementation • 5 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.
no code implementations • 5 May 2023 • Alex Youssef, Michael Pencina, Anshul Thakur, Tingting Zhu, David Clifton, Nigam H. Shah
We submit that external validation is insufficient to establish ML models' safety or utility.
no code implementations • 28 Feb 2023 • Taha Ceritli, Ghadeer O. Ghosheh, Vinod Kumar Chauhan, Tingting Zhu, Andrew P. Creagh, David A. Clifton
Electronic Health Records (EHRs) contain sensitive patient information, which presents privacy concerns when sharing such data.
no code implementations • 19 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.
no code implementations • 14 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.
1 code implementation • 22 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.
no code implementations • 29 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.
no code implementations • 4 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.
no code implementations • 31 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.
1 code implementation • 19 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.
no code implementations • 1 Jan 2021 • Dani Kiyasseh, Tingting Zhu, David A. Clifton
The ubiquity and rate of collection of physiological signals produce large, unlabelled datasets.
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.
no code implementations • 28 Nov 2020 • Dani Kiyasseh, Tingting Zhu, David A. Clifton
Many clinical deep learning algorithms are population-based and difficult to interpret.
no code implementations • 17 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.
no code implementations • 28 Sep 2020 • Dani Kiyasseh, Tingting Zhu, David A. Clifton
The ongoing digitization of health records within the healthcare industry results in large-scale datasets.
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.
1 code implementation • 27 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.
no code implementations • 22 Apr 2020 • Dani Kiyasseh, Tingting Zhu, David A. Clifton
Large sets of unlabelled data within the healthcare domain remain underutilized.
2 code implementations • 20 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.
no code implementations • 20 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).
no code implementations • 10 Dec 2019 • Girmaw Abebe Tadesse, Tingting Zhu, Nhan Le Nguyen Thanh, Nguyen Thanh Hung, Ha Thi Hai Duong, Truong Huu Khanh, Pham Van Quang, Duc Duong Tran, LamMinh Yen, H Rogier Van Doorn, Nguyen Van Hao, John Prince, Hamza Javed, DaniKiyasseh, Le Van Tan, Louise Thwaites, David A. Clifton
A support vector machine is employed to classify the ANSD levels.
no code implementations • 23 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.