Physiotherapy - Research Publications
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ItemUncertainty-aware non-invasive patient–ventilator asynchrony detection using latent Gaussian mixture generative classifier with noisy label correction(Springer Science and Business Media LLC, 2024-01-01)Abstract Patient–ventilator asynchrony (PVA) refers to instances where a mechanical ventilator’s cycles are desynchronised from the patient’s breathing efforts, and may result in patient discomfort and potential ineffective ventilation. Typically, they are identified with constant monitoring by trained clinicians. Such expertise is often limited; therefore, it is desirable to automate PVA detection with machine learning methods. However, there are three major challenges to applying machine learning to the problem: data collected from non-invasive ventilation are often noisy, there exists high variability between patients or between setting changes, and manual annotations of PVA events are not always consistent. To produce meaningful inference from such noisy data, a model needs to not only provide a measure of uncertainty, but also take into account potential inconsistencies in the training signal it is based on. In this work, we propose a conditional latent Gaussian mixture generative classifier with noisy label correction, which is capable of capturing variations within and between classes, providing well-calibrated class probabilities, detecting unlikely input instances that deviates from training data, while also taking into account possible mislabelling of event classes. We show that our model is able to match the performance of a well-tuned gradient boosting classifier, but also produce better calibrated predictions and smaller performance variability between patients.
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ItemIdentification of Patient Ventilator Asynchrony in Physiological Data Through Integrating Machine-Learning(SCITEPRESS - Science and Technology Publications, 2024)Patient Ventilator Asynchrony (PVA) occurs where a mechanical ventilator aiding a patient's breathing falls out of synchronisation with their breathing pattern. This de-synchronisation may cause patient distress and can lead to long-term negative clinical outcomes. Research into the causes and possible mitigations of PVA is currently conducted by clinical domain experts using manual methods, such as parsing entire sleep hypnograms visually, and identifying and tagging instances of PVA that they find. This process is very labour-intensive and can be error prone. This project aims to make this analysis more efficient, by using machine-learning approaches to automatically parse, classify, and suggest instances of PVA for ultimate confirmation by domain experts. The solution has been developed based on a retrospective dataset of intervention and control patients that were recruited to a non-invasive ventilation study. This achieves a specificity metric of over 90%. This paper describes the process of integrating the output of the machine learning into the bedside clinical monitoring system for production use in anticipation of a future clinical trial.
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ItemEarly Detection and Classification of Patient-Ventilator Asynchrony Using Machine Learning(SPRINGER INTERNATIONAL PUBLISHING AG, 2022)