Automatic assessment of patient eligibility by utilizing NLP and rule-based analysis.

Assessment of patient eligibility is an essential process in the clinical trial but there are a lot of manual processes involved. Natural Language Processing (NLP) is a promising technique to automate analysing of the massive volume of Electronic Medical Records (EMRs) hence it can assist in the assessment of patient eligibility, especially in clinical trials that require complex inclusion/exclusion criteria. In this paper, we proposed a hybrid model which utilized both rule-based and NLP technologies to automate the assessment of patient eligibility. The result showed that the hybrid model had a better trade-off between sensitivity and precision compared to the rule-based model and NLP similarity model. Moreover, the accuracy of the hybrid model was validated on the larger dataset and it reached an accuracy of 87.3%. Therefore, this technique potentially can improve the efficiency of patient recruitment by eliminating the manual processes that involve in the assessment of patient eligibility.

Use of Wearable Sensor Device and Mobile Application for Objective Assessment of Pain in Post-surgical Patients: A Preliminary Study.

Effective post-operative pain management requires an accurate and frequent assessment of the pain experienced by the patients. The current gold-standard of pain assessment is through patient self-evaluation (e.g., numeric rating scale, NRS) which is subjective, prone to recall-bias, and does not provide comprehensive information of the pain intensity and its trends. We conducted a study to explore the potential of wearable biosensors and machine learning-based analysis of physiological parameters to estimate the pain intensity. The results from our study of post-operative knee surgery patients monitored over a period of 30 days demonstrate the feasibility of the system in ambulatory setting, with a substantial agreement (Cohen's Kappa = 0.70, 95% CI 0.68-0.72) between the pain intensity estimation and the patient reported numerical rating scale. Therefore, the wearable biosensors coupled with the machine learning-derived pain estimation are capable of remotely assessing the pain intensity.

Accurate Ventricular Tachyarrhytmia Beats Detection on Low Sample Rate ECG Patch Signals Using 1D U-Net.

ECG signals quality from mobile cardiac telemetry (MCT) wearable is much noisier than Holter or standard twelve leads ECG. Although, there are beats detection algorithms that has been shown to be accurate for MIT-BIH data, their performances degrade when applying to patches data and non sinus rhythms, especially when detecting ventricular beats on ventricular tachyarrhythmia. This paper presents a deep learning approach using convolutional neural network 1D U-net architecture as a core model, accompanied with miniature pre-processing and post-processing. The model consists of contracting path and expanding path. The contracting path is a sequence of multiple convolution layers and max pooling layers while the expanding path is a sequence of multiple convolution layers and up-convolution layers. There are internal connections between the contracting path and the expanding path to avoid gradient vanishing. The output of the model predicts beat probability map which can be converted to beat locations. Performance of the model on patch data gives 98.86% recall and 97.46% F1-score which is better than Pan-Tompkins by 2.48% and 0.33% respectively. For only ventricular beats, the recall is 95.21% which outperform Pan-Tompkins by 3.68%.

Daily ambulatory remote monitoring system for drug escalation in chronic heart failure with reduced ejection fraction: pilot phase of DAVID-HF study.

Aims: Underutilization of guideline-directed heart failure with reduced ejection fraction (HFrEF) medications contributes to poor outcomes. Methods and results: A pilot study to evaluate the safety and efficacy of a home-based remote monitoring system for HFrEF management was performed. The system included wearable armband monitors paired with the smartphone application. An HFrEF medication titration algorithm was used to adjust medication daily. The primary endpoint was HFrEF medication utilization at 120 days. Twenty patients (60.5 ± 8.2 years, men: 85%) with HFrEF were recruited. All received angiotensin-converting enzyme inhibitor (ACEI)/angiotensin receptor blocker (ARB)/angiotensin receptor-neprilysin inhibitor (ARNI) at recruitment; 45% received ≥50% maximal targeted dose (MTD) with % MTD of 44.4 ± 31.7%. At baseline, 90 and 70% received beta-adrenergic blocker and mineralocorticoid receptor antagonist (MRA), 35% received ≥50% MTD beta-adrenergic blocker with % MTD of 34.1 ± 29.6%, and 25% received ≥50% MTD MRA with % MTD of 25.0 ± 19.9%. At 120 days, 70% received ≥50% MTD ACEI/ARB/ARNI (P = 0.110) with % MTD increased to 64.4 ± 33.5% (P = 0.060). The proportion receiving ≥50% MTD ARNI increased from 15 to 55% (P = 0.089) with % MTD ARNI increased from 20.6 ± 30.9 to 53.1 ± 39.5% (P = 0.006*). More patients received ≥50% MTD MRA (65 vs. 25%, P = 0.011*) with % MTD MRA increased from 25.0 ± 19.9 to 46.2 ± 28.8% (P = 0.009*). Ninety-five per cent of patients had reduced NT-proBNP with the percentage reduction of 26.7 ± 19.7%. Conclusion: Heart failure with reduced ejection fraction medication escalation with remote monitoring appeared feasible.

Observational study on wearable biosensors and machine learning-based remote monitoring of COVID-19 patients.

Patients infected with SARS-CoV-2 may deteriorate rapidly and therefore continuous monitoring is necessary. We conducted an observational study involving patients with mild COVID-19 to explore the potentials of wearable biosensors and machine learning-based analysis of physiology parameters to detect clinical deterioration. Thirty-four patients (median age: 32 years; male: 52.9%) with mild COVID-19 from Queen Mary Hospital were recruited. The mean National Early Warning Score 2 (NEWS2) were 0.59 ± 0.7. 1231 manual measurement of physiology parameters were performed during hospital stay (median 15 days). Physiology parameters obtained from wearable biosensors correlated well with manual measurement including pulse rate (r = 0.96, p < 0.0001) and oxygen saturation (r = 0.87, p < 0.0001). A machine learning-derived index reflecting overall health status, Biovitals Index (BI), was generated by autonomous analysis of physiology parameters, symptoms, and other medical data. Daily BI was linearly associated with respiratory tract viral load (p < 0.0001) and NEWS2 (r = 0.75, p < 0.001). BI was superior to NEWS2 in predicting clinical worsening events (sensitivity 94.1% and specificity 88.9%) and prolonged hospitalization (sensitivity 66.7% and specificity 72.7%). Wearable biosensors coupled with machine learning-derived health index allowed automated detection of clinical deterioration.

Compressive Sampling Based Multi-Spectrum Deep Learning for Sub-Nyquist Pacemaker ECG Analysis.

Automatic electrocardiogram (ECG) analysis for pacemaker patients is crucial for monitoring cardiac conditions and the effectiveness of cardiac resynchronization treatment. However, under the condition of energy-saving remote monitoring, the low-sampling-rate issue of an ECG device can lead to the miss detection of pacemaker spikes as well as incorrect analysis on paced rhythm and non-paced arrhythmias. To solve the issue, this paper proposed a novel system that applies the compressive sampling (CS) framework to sub-Nyquist acquire and reconstruct ECG, and then uses multi-dimensional feature-based deep learning to identify paced rhythm and non-paced arrhythmias. Simulation testing results on ECG databases and comparison with existing approaches demonstrate its effectiveness and outstanding performance for pacemaker ECG analysis.

Spectro-Temporal Feature Based Multi-Channel Convolutional Neural Network for ECG Beat Classification.

Automatic classification of abnormal beats in ECG signals is crucial for monitoring cardiac conditions and the performance of the classification will improve the success rate of the treatment. However, under certain circumstances, traditional classifiers cannot be adapted well to the variation of ECG morphologies or variation of different patients due to fixed hand-crafted features selection. Additionally, existing deep learning related solutions reach their limitation because they fail to use the beat-to-beat information together with single-beat morphologies. This paper applies a novel solution which converts one-dimensional ECG signal into spectro-temporal images and use multiple dense convolutional neural network to capture both beat-to-beat and single-beat information for analysis. The results of simulation on the MIT-BIH arrhythmias database demonstrate the effectiveness of the proposed methodology by showing an outstanding detection performance compared to other existing methods.