COPER: Continuous Patient State Perceiver

Chauhan, Vinod Kumar; Thakur, Anshul; O'Donoghue, Odhran; Clifton, David A.

doi:10.1109/bhi56158.2022.9926807

Cited by 5 publications

(7 citation statements)

References 11 publications

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“…IHM prediction is very important for resource management, triage, initial risk assessment and designing effective treatment plans [12]. For preprocessing of MIMIC dataset, we have followed [25] to get a dataset with 76 features and 14,681, 3,236 and 3,222 samples in train, validation and test datasets 1 , respectively. For Physionet dataset, we follow the preprocessing as used in [8].…”

Section: A Datasets and Baselinesmentioning

confidence: 99%

“…The contributions of the paper are summarised below. The preliminary idea and the results of the present work were published in a four page paper [1]. This paper revises the idea and the empirical evaluation of [1] in a number of ways: (i) methodology is discussed in details, with the addition of algorithmic details, (ii) the architecture/idea is revised to have continuity only in the patient health state as this is sufficient to address the irregularity in EHR, which also improves the results than having continuity in the input as well as output spaces.…”

Section: Introductionmentioning

confidence: 99%

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Continuous Patient State Attention Models

Chauhan

Thakur

O'Donoghue

et al. 2022

Preprint

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Irregular time-series (ITS) are prevalent in the electronic health records (EHR) as the data is recorded in EHR system as per the clinical guidelines/requirements but not for research and also depends on the patient health status. ITS present challenges in training of machine learning algorithms, which are mostly built on assumption of coherent fixed dimensional feature space. In this paper, we propose a computationally efficient variant of the transformer based on the idea of cross-attention, called Perceiver, for time-series in healthcare. We further develop continuous patient state attention models, using the Perceiver and the transformer to deal with ITS in EHR. The continuous patient state models utilise neural ordinary differential equations to learn the patient health dynamics, i.e., patient health trajectory from the observed irregular time-steps, which enables them to sample any number of time-steps at any time. The performance of the proposed models is evaluated on in-hospital-mortality prediction task on Physionet-2012 challenge and MIMIC-III datasets. The Perceiver model significantly outperforms the baselines and reduces the computational complexity, as compared with the transformer model, without significant loss of performance. The carefully designed experiments to study irregularity in healthcare also show that the continuous patient state models outperform the baselines. The code is publicly released and verified at https://codeocean.com/capsule/4587224

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Section: A Datasets and Baselinesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Continuous Patient State Attention Models

Chauhan

Thakur

O'Donoghue

et al. 2022

Preprint

Self Cite

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show abstract

“…The adoption of EHR in healthcare has resulted in big data that has presented great opportunities for the development of machine learning algorithms and artificial intelligence technologies to reduce the burden on the healthcare system, support clinical decision making and efficient management of healthcare resources [6][7][8][9][10][11][12][13][14][15][16]. Machine learning algorithms, however, are primarily predicated on an assumption of coherent fixed dimensional feature vectors, and presence of ITS in EHR invalidates that assumption.…”

Section: Introductionmentioning

confidence: 99%

“…The contributions of the paper are summarised below. The preliminary idea of this work was published in [39]. This paper revises the idea and the empirical evaluation of [39] in several ways: (i) the architecture/idea is revised to have continuity only in patient health state as this suffices to address irregularity in EHR, which also improves results than having continuity in input as well as output spaces.…”

Section: Introductionmentioning

confidence: 99%

Continuous patient state attention model for addressing irregularity in electronic health records

Chauhan,

Thakur,

O’Donoghue

et al. 2024

BMC Med Inform Decis Mak

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Background Irregular time series (ITS) are common in healthcare as patient data is recorded in an electronic health record (EHR) system as per clinical guidelines/requirements but not for research and depends on a patient’s health status. Due to irregularity, it is challenging to develop machine learning techniques to uncover vast intelligence hidden in EHR big data, without losing performance on downstream patient outcome prediction tasks. Methods In this paper, we propose Perceiver, a cross-attention-based transformer variant that is computationally efficient and can handle long sequences of time series in healthcare. We further develop continuous patient state attention models, using Perceiver and transformer to deal with ITS in EHR. The continuous patient state models utilise neural ordinary differential equations to learn patient health dynamics, i.e., patient health trajectory from observed irregular time steps, which enables them to sample patient state at any time. Results The proposed models’ performance on in-hospital mortality prediction task on PhysioNet-2012 challenge and MIMIC-III datasets is examined. Perceiver model either outperforms or performs at par with baselines, and reduces computations by about nine times when compared to the transformer model, with no significant loss of performance. Experiments to examine irregularity in healthcare reveal that continuous patient state models outperform baselines. Moreover, the predictive uncertainty of the model is used to refer extremely uncertain cases to clinicians, which enhances the model’s performance. Code is publicly available and verified at https://codeocean.com/capsule/4587224. Conclusions Perceiver presents a computationally efficient potential alternative for processing long sequences of time series in healthcare, and the continuous patient state attention models outperform the traditional and advanced techniques to handle irregularity in the time series. Moreover, the predictive uncertainty of the model helps in the development of transparent and trustworthy systems, which can be utilised as per the availability of clinicians.

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“…EHRs offer numerous benefits, including improved patient care, increased efficiency, and reduced healthcare costs [ 18 ]. Regardless of the potential for EHRs in various applications, their effective usage is hindered by data-specific restrictions [ 6 ], such as high missingness and irregular sampling [ 19 , 20 , 21 ], as well as imbalanced classes due to uneven prevalence of illnesses [ 22 ]. Therefore, it is important to address these limitations in order to fully realise the potential of EHRs.…”

Section: Introductionmentioning

confidence: 99%

Improving Diagnostics with Deep Forest Applied to Electronic Health Records

Khodadadi

Bousejin²,

Molaei

et al. 2023

Sensors

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An electronic health record (EHR) is a vital high-dimensional part of medical concepts. Discovering implicit correlations in the information of this data set and the research and informative aspects can improve the treatment and management process. The challenge of concern is the data sources’ limitations in finding a stable model to relate medical concepts and use these existing connections. This paper presents Patient Forest, a novel end-to-end approach for learning patient representations from tree-structured data for readmission and mortality prediction tasks. By leveraging statistical features, the proposed model is able to provide an accurate and reliable classifier for predicting readmission and mortality. Experiments on MIMIC-III and eICU datasets demonstrate Patient Forest outperforms existing machine learning models, especially when the training data are limited. Additionally, a qualitative evaluation of Patient Forest is conducted by visualising the learnt representations in 2D space using the t-SNE, which further confirms the effectiveness of the proposed model in learning EHR representations.

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COPER: Continuous Patient State Perceiver

Cited by 5 publications

References 11 publications

Continuous Patient State Attention Models

Continuous Patient State Attention Models

Continuous patient state attention model for addressing irregularity in electronic health records

Improving Diagnostics with Deep Forest Applied to Electronic Health Records

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