Artificial intelligence in health care: enabling informed care

We read with interest the Lancet Editorial on artificial intelligence (AI) in health care (Dec 23, 2017, p 2739).1 Deep learning as a form of AI risks being overhyped. Deep neural networks contain multiple layers of nodes connected by adjustable weights. Learning occurs by adjusting these weights until the desired input-to-output function is achieved.2 With many millions of weights, huge amounts of data are required for learning, a process facilitated by recent increases in computational power. However, the learning algorithm, known as the error back-propagation algorithm, was invented in the 1980s and has been used to train neural networks ever since. Two decades ago, our neural network system scored sleep and diagnosed sleep disorders.3 Our machine learning algorithm,4, 5 which now provides early warning of deterioration in many hospitals, was commercialised a decade ago.6 A key change occurred in the early 2000s. Since then, error back-propagation learns features directly from the input data, rather than relying on expert-selected features (eg, microaneurysms for a neural network assessing diabetic retinopathy). The first layers become implicit feature detectors. The success of deep learning has been shown mainly in problems with inputs of image (or image-like) data, as shown in medical image analysis,7, 8 speech recognition, and board game playing. Deep learning also lacks explanatory power; deep neural networks cannot explain how a diagnosis is reached and the features enabling discrimination are not easily identifiable. Clinicians should be aware of the capabilities as well as current limitations of AI. Properly integrated AI will improve patient outcomes and health-care efficiency. Augmented intelligence at the point of care is likely to precede AI without human involvement. LT and PW are supported by the Biomedical Research Centre, Oxford. Both authors have received funding from the National Institute for Health Research. The authors have developed an electronic observations application for which Drayson Health has purchased a sole licence. Drayson Health has a research agreement with the University of Oxford and has paid LT personal fees for consultancy as a member of its Strategic Advisory Board. Drayson Health might pay PW consultancy fees in the future

Wearables for continuous patient monitoring on COVID-19 isolation wards

An ambulatory monitoring system for the continuous monitoring of heart rate, respiratory rate and oxygen saturation, using wearable devices was implemented at the start of the COVID-19 pandemic on selected isolation wards in a large UK hospital. We have retrospectively analysed the data and nurse observations from two groups of patients on these wards: those whose care was escalated so that they were admitted to the Intensive Care Unit (ICU); and those who were discharged home or to a non-isolation ward (stepping down). The computation of population averages for these two groups 24h prior to an ICU admission or prior to stepping down provides evidence for the value of wearable monitoring for the early identification of physiological deteriorations in COVID-19 patients. The continuous data from the finger-worn pulse oximeter reveals clinically significant changes between 2 and 3 hours ahead of the regular vital-sign observations by the nursing staff. We also show how a hybrid score based on six physiological parameters (calculated from a mixture of continuous and intermittent vital-sign data) can provide early warning of deterioration for high-risk patients

SleepVST: Sleep Staging from Near-Infrared Video Signals using Pre-Trained Transformers

Advances in camera-based physiological monitoring have enabled the robust, non-contact measurement of respiration and the cardiac pulse, which are known to be indicative of the sleep stage. This has led to research into camera-based sleep monitoring as a promising alternative to "gold-standard" polysomnography, which is cumbersome, expensive to administer, and hence unsuitable for longer-term clinical studies. In this paper, we introduce SleepVST, a transformer model which enables state-of-the-art performance in camera-based sleep stage classification (sleep staging). After pre-training on contact sensor data, SleepVST outperforms existing methods for cardio-respiratory sleep staging on the SHHS and MESA datasets, achieving total Cohen's kappa scores of 0.75 and 0.77 respectively. We then show that SleepVST can be successfully transferred to cardio-respiratory waveforms extracted from video, enabling fully contact-free sleep staging. Using a video dataset of 50 nights, we achieve a total accuracy of 78.8\% and a Cohen's $\kappa$ of 0.71 in four-class video-based sleep staging, setting a new state-of-the-art in the domain.Comment: CVPR 2024 Highlight Pape

Deep Learning-Enabled Sleep Staging From Vital Signs and Activity Measured Using a Near-Infrared Video Camera

Conventional sleep monitoring is time-consuming, expensive and uncomfortable, requiring a large number of contact sensors to be attached to the patient. Video data is commonly recorded as part of a sleep laboratory assessment. If accurate sleep staging could be achieved solely from video, this would overcome many of the problems of traditional methods. In this work we use heart rate, breathing rate and activity measures, all derived from a near-infrared video camera, to perform sleep stage classification. We use a deep transfer learning approach to overcome data scarcity, by using an existing contact-sensor dataset to learn effective representations from the heart and breathing rate time series. Using a dataset of 50 healthy volunteers, we achieve an accuracy of 73.4\% and a Cohen's kappa of 0.61 in four-class sleep stage classification, establishing a new state-of-the-art for video-based sleep staging.Comment: Accepted to the 6th International Workshop on Computer Vision for Physiological Measurement (CVPM) at CVPR 2023. 10 pages, 12 figures, 5 table

Continuous Physiological Monitoring of Ambulatory Patients

A poster originally presented at the "MEC Annual Meeting and Bioengineering14" conference (Imperial College London, 8th - 9th September 2014)

Modelling physiological deterioration in post-operative patient vital-sign data

Patients who undergo upper-gastrointestinal surgery have a high incidence of post-operative complications, often requiring admission to the intensive care unit several days after surgery. A dataset comprising observational vital-sign data from 171 post-operative patients taking part in a two-phase clinical trial at the Oxford Cancer Centre, was used to explore the trajectory of patients’ vital-sign changes during their stay in the post-operative ward using both univariate and multivariate analyses. A model of normality based vital-sign data from patients who had a “normal” recovery was constructed using a kernel density estimate, and tested with “abnormal” data from patients who deteriorated sufficiently to be re-admitted to the intensive care unit. The vital-sign distributions from “normal” patients were found to vary over time from admission to the post-operative ward to their discharge home, but no significant changes in their distributions were observed from halfway through their stay on the ward to the time of discharge. The model of normality identified patient deterioration when tested with unseen “abnormal” data, suggesting that such techniques may be used to provide early warning of adverse physiological events

The Influence of Recording Equipment on the Accuracy of Respiratory Rate Estimation from the Electrocardiogram and Photoplethysmogram

A poster originally presented at the "MEC Annual Meeting and Bioengineering14" conference (Imperial College London, 8th - 9th September 2014)

Estimation of respiratory rate from motion contaminated photoplethysmography signals incorporating accelerometry.

Estimation of respiratory rate (RR) from photoplethysmography (PPG) signals has important applications in the healthcare sector, from assisting doctors onwards to monitoring patients in their own homes. The problem is still very challenging, particularly during the motion for large segments of data, where results from different methods often do not agree. The authors aim to propose a new technique which performs motion reduction from PPG signals with the help of simultaneous acceleration signals where the PPG and accelerometer sensors need to be embedded in the same sensor unit. This method also reconstructs motion corrupted PPG signals in the Hilbert domain. An auto-regressive (AR) based technique has been used to estimate the RR from reconstructed PPGs. The proposed method has provided promising results for the estimation of RRs and their variations from PPG signals corrupted with motion artefact. The proposed platform is able to contribute to continuous in-hospital and home-based monitoring of patients using PPG signals under various conditions such as rest and motion states

Remote vital sign monitoring in admission avoidance hospital at home: a systematic review

Objectives: To examine randomized controlled trials (RCTs) of “hospital at home” (HAH) for admission avoidance in adults presenting with acute physical illness to identify the use of vital sign monitoring approaches and evidence for their effectiveness. Design: Systematic review. Setting and participants: This review compared strategies for vital sign monitoring in admission avoidance HAH for adults presenting with acute physical illness. Vital sign monitoring can support HAH acute multidisciplinary care by contributing to safety, determining requirement of further assessment, and guiding clinical decisions. There are a wide range of systems currently available, including reliable and automated continuous remote monitoring using wearable devices. Methods: Eligible studies were identified through updated database and trial registries searches (March 2, 2016, to February 15, 2023), and existing systematic reviews. Risk of bias was assessed using the Cochrane risk of bias 2 tool. Random effects meta-analyses were performed, and narrative summaries provided stratified by vital sign monitoring approach. Results: Twenty-one eligible RCTs (3459 participants) were identified. Two approaches to vital sign monitoring were characterized: manual and automated. Reporting was insufficient in the majority of studies for classification. For HAH compared to hospital care, 6-monthly mortality risk ratio (RR) was 0.94 (95% CI 0.78-1.12), 3-monthly readmission to hospital RR 1.02 (0.77-1.35), and length of stay mean difference 1.91 days (0.71-3.12). Readmission to hospital was reduced in the automated monitoring subgroup (RR 0.30 95% CI 0.11-0.86). Conclusions and Implications: This review highlights gaps in the reporting and evidence base informing remote vital sign monitoring in alternatives to admission for acute illness, despite expanding implementation in clinical practice. Although continuous vital sign monitoring using wearable devices may offer added benefit, its use in existing RCTs is limited. Recommendations for the implementation and evaluation of remote monitoring in future clinical trials are proposed