IEEE journal of biomedical and health informatics
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IEEE J Biomed Health Inform · May 2020
Network Analysis and Visualisation of Opioid Prescribing Data.
In many countries around the world (including Australia), the prescribing of opioid analgesic drugs is an increasing trend associated with significant increases in drug-related patient harm such as abuse, overdose, and death. In Australia, the Medicines Regulation and Quality Unit within Queensland Health maintains a database recording opioid analgesic drug prescriptions dispensed across the State (population 4.703 million). In this work, we propose the use of network visualisation and analysis as a tool for improved understanding of these data. ⋯ Local analysis is also carried out to demonstrate the clinical utility of the technique, including the dynamics of the graph structure over time. A variety of network statistics that measure network structural and dynamic properties are presented to reveal the characteristics and trends of drug seeking and prescribing behaviours. This approach has been recognised by healthcare professionals at Queensland Health as leading to new and useful insights on the relationship between patients and prescribers and supporting their advisory role to reduce patient harm from inappropriate use of prescription drugs.
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IEEE J Biomed Health Inform · Feb 2020
Deep Interpretable Early Warning System for the Detection of Clinical Deterioration.
Assessment of physiological instability preceding adverse events on hospital wards has been previously investigated through clinical early warning score systems. Early warning scores are simple to use yet they consider data as independent and identically distributed random variables. Deep learning applications are able to learn from sequential data, however they lack interpretability and are thus difficult to deploy in clinical settings. ⋯ DEWS achieved superior accuracy than the state-of-the-art that is currently implemented in clinical settings, the National Early Warning Score, in terms of the overall area under the receiver operating characteristic curve (AUROC) (0.880 vs. 0.866) and when evaluated independently for each of the three outcomes. Our attention-based architecture was able to recognize 'historical' trends in the data that are most correlated with the predicted probability. With high sensitivity, improved clinical utility and increased interpretability, our model can be easily deployed in clinical settings to supplement existing EWS systems.
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IEEE J Biomed Health Inform · Nov 2019
Randomized Controlled TrialThe Effect of Mirroring Display of Virtual Reality Tour of the Operating Theatre on Preoperative Anxiety: A Randomized Controlled Trial.
A virtual reality (VR) tour of the operating theatre could reduce preoperative anxiety by providing a realistic experience for children. This randomized clinical trial was designed to determine whether parental co-experience of preoperative VR tour through a mirroring display could further reduce preoperative anxiety. Eighty children scheduled for elective surgery under general anesthesia were randomly allocated into either the control or mirroring group. ⋯ Preoperative anxiety of children (p = 0.025) and parents (p = 0.009) were lower in the mirroring group compared with the control group. Parents' satisfaction in the mirroring group was significantly higher than those in the control group (p = 0.008). Parental co-experience of the VR tour with children through mirroring the display was effective in reducing preoperative anxiety in both children and parents.
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IEEE J Biomed Health Inform · Jul 2019
Finite State Machine Framework for Instantaneous Heart Rate Validation Using Wearable Photoplethysmography During Intensive Exercise.
Accurate estimation of heart rate (HR) using reflectance-type photoplethysmographic (PPG) signals during intensive physical exercise is challenging because of very low signal-to-noise ratio and unpredictable motion artifacts (MA), which are frequently uncorrelated with reference signals, such as accelerometer signals. In this paper, we propose a finite state machine framework based novel algorithm for HR estimation and validation, which exploits the crest factor from the periodogram obtained after MA removal, and the estimated HR changes in consecutive windows as the estimation accuracy indicators. Our proposed algorithm automatically provides only accurate HR estimation results in real time by ignoring the estimation results when true HRs are not reflected in PPG signals or when the MAs uncorrelated with accelerometer signals are dominant. ⋯ Our algorithm exhibits an average absolute error of 0.99 beats per minute and an average relative error of 0.88%. The algorithm is simple; the computational time is [Formula: see text] for 8 s window. Also, the algorithm framework can be combined with existing methods to improve estimation accuracy.
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IEEE J Biomed Health Inform · May 2019
A Lightweight Multi-Section CNN for Lung Nodule Classification and Malignancy Estimation.
The size and shape of a nodule are the essential indicators of malignancy in lung cancer diagnosis. However, effectively capturing the nodule's structural information from CT scans in a computer-aided system is a challenging task. Unlike previous models that proposed computationally intensive deep ensemble models or three-dimensional CNN models, we propose a lightweight, multiple view sampling based multi-section CNN architecture. ⋯ It achieved the state-of-the-art performance with a mean 93.18% classification accuracy. The architecture could also be used to select the representative cross sections determining the nodule's malignancy that facilitates in the interpretation of results. Because of being lightweight, the model could be ported to mobile devices, which brings the power of artificial intelligence (AI) driven application directly into the practitioner's hand.