Sensor-Based Multifaceted Feature Extraction and Ensemble Elastic Net Approach for Assessing Fall Risk in Community-Dwelling Older Adults.

Accurate identification of community-dwelling older adults at high fall risk can facilitate timely intervention and significantly reduce fall incidents. Analyzing gait and balance capabilities via feature extraction and modeling through sensor-based motion data has emerged as a viable approach for fall risk assessment. However, the existing approaches for extracting key features related to fall risk lack inclusiveness, with limited consideration of the non-linear characteristics of sensor signals, such as signal complexity, self-similarity, and local stability.

Clinical prediction rule of termination of resuscitation for out-of-hospital cardiac arrest patient with pre-hospital defibrillation given.

Objective: To derive a clinical prediction rule of termination of resuscitation (TOR) for out-of-hospital cardiac arrest (OHCA) with pre-hospital defibrillation given.

Method: This was a retrospective multicenter cohort study performed in three emergency departments (EDs) of three regional hospitals from 1/1/2012 to 31/12/2018. Patients of OHCA aged ≥18 years old were included.

Influencing factors of Barthel index scores among the community-dwelling elderly in Hong Kong: a random intercept model.

Background: Barthel Index (BI) is one of the most widely utilized tools for assessing functional independence in activities of daily living. Most existing BI studies used populations with specific diseases (e.g.

Prognostics for lithium-ion batteries using a two-phase gamma degradation process model.

To address the degradation of rechargeable batteries, this paper presents a two-phase gamma process model with a fixed change-point for modeling the voltage-discharge curves of battery cycle aging under a constant current. The model can be applied to estimate the state of charge (SOC) and the remaining useful discharge time (RUT) in a cycle with consideration of the effect of cycle aging, and can also be applied to estimate the state of life (SOL) and the remaining useful life (RUL) across cycles. The applications of the proposed model are demonstrated using the experimental cycle aging data of a lithium iron phosphate battery.

Evaluating resampling methods and structured features to improve fall incident report identification by the severity level.

Objective: This study aims to improve the classification of the fall incident severity level by considering data imbalance issues and structured features through machine learning.

Materials And Methods: We present an incident report classification (IRC) framework to classify the in-hospital fall incident severity level by addressing the imbalanced class problem and incorporating structured attributes. After text preprocessing, bag-of-words features, structured text features, and structured clinical features were extracted from the reports.

An Adaptive Weight Learning-Based Multitask Deep Network for Continuous Blood Pressure Estimation Using Electrocardiogram Signals.

Estimating blood pressure via combination analysis with electrocardiogram and photoplethysmography signals has attracted growing interest in continuous monitoring patients' health conditions. However, most wearable/portal monitoring devices generally acquire only one kind of physiological signals due to the consideration of energy cost, device weight and size, etc. In this study, a novel adaptive weight learning-based multitask deep learning framework based on single lead electrocardiogram signals is proposed for continuous blood pressure estimation.

Wearable Device Heart Rate and Activity Data in an Unsupervised Approach to Personalized Sleep Monitoring: Algorithm Validation.

Background: The proliferation of wearable devices that collect activity and heart rate data has facilitated new ways to measure sleeping and waking durations unobtrusively and longitudinally. Most existing sleep/wake identification algorithms are based on activity only and are trained on expensive and laboriously annotated polysomnography (PSG). Heart rate can also be reflective of sleep/wake transitions, which has motivated its investigation herein in an unsupervised algorithm.

Forecasting one-day-forward wellness conditions for community-dwelling elderly with single lead short electrocardiogram signals.

Background: The accelerated growth of elderly population is creating a heavy burden to the healthcare system in many developed countries and regions. Electrocardiogram (ECG) analysis has been recognized as effective approach to cardiovascular disease diagnosis and widely utilized for monitoring personalized health conditions.

Method: In this study, we present a novel approach to forecasting one-day-forward wellness conditions for community-dwelling elderly by analyzing single lead short ECG signals acquired from a station-based monitoring device.

Monitoring data quality for telehealth systems in the presence of missing data.

Background: All-in-one station-based health monitoring devices are implemented in elder homes in Hong Kong to support the monitoring of vital signs of the elderly. During a pilot study, it was discovered that the systolic blood pressure was incorrectly measured during multiple weeks. A real-time solution was needed to identify future data quality issues as soon as possible.

Using Baidu index to nowcast hand-foot-mouth disease in China: a meta learning approach.

Background: Hand, foot, and mouth disease (HFMD) has been recognized as one of the leading infectious diseases among children in China, which causes hundreds of annual deaths since 2008. In China, the reports of monthly HFMD cases usually have a delay of 1-2 months due to the time needed for collecting and processing clinical information. This time lag is far from optimal for policymakers making decisions.

Regularized Gaussian Mixture Model for High-Dimensional Clustering.

Finding low-dimensional representation of high-dimensional data sets is an important task in various applications. The fact that data sets often contain clusters embedded in different subspaces poses barrier to this task. Driven by the need in methods that enable clustering and finding each cluster's intrinsic subspace simultaneously, in this paper, we propose a regularized Gaussian mixture model (GMM) for clustering.

A Framework of Rebalancing Imbalanced Healthcare Data for Rare Events' Classification: A Case of Look-Alike Sound-Alike Mix-Up Incident Detection.

Identifying rare but significant healthcare events in massive unstructured datasets has become a common task in healthcare data analytics. However, imbalanced class distribution in many practical datasets greatly hampers the detection of rare events, as most classification methods implicitly assume an equal occurrence of classes and are designed to maximize the overall classification accuracy. In this study, we develop a framework for learning healthcare data with imbalanced distribution via incorporating different rebalancing strategies.

Influence of Flavors on the Propagation of E-Cigarette-Related Information: Social Media Study.

Background: Modeling the influence of e-cigarette flavors on information propagation could provide quantitative policy decision support concerning smoking initiation and contagion, as well as e-cigarette regulations.

Objective: The objective of this study was to characterize the influence of flavors on e-cigarette-related information propagation on social media.

Methods: We collected a comprehensive dataset of e-cigarette-related discussions from public Pages on Facebook.

Predictive model integrating dynamic parameters for massive blood transfusion in major trauma patients: The Dynamic MBT score.

Background: Currently existing predictive models for massive blood transfusion in major trauma patients had limitations for sequential evaluation of patients and lack of dynamic parameters.

Objective: To establish a predictive model for predicting the need of massive blood transfusion major trauma patients, integrating dynamic parameters.

Design: Multi-center retrospective cohort study.

A universal deep learning approach for modeling the flow of patients under different severities.

Background And Objective: The Accident and Emergency Department (A&ED) is the frontline for providing emergency care in hospitals. Unfortunately, relative A&ED resources have failed to keep up with continuously increasing demand in recent years, which leads to overcrowding in A&ED. Knowing the fluctuation of patient arrival volume in advance is a significant premise to relieve this pressure.

Impact of hypotension after return of spontaneous circulation on survival in patients of out-of-hospital cardiac arrest.

Objective: To investigate the relationship between hypotension in the first 3h after return of spontaneous circulation (ROSC) in patients with out-of-hospital cardiac arrest.

Method: This retrospective cohort study occurred at two regional hospitals and included adult OHCA patients who experienced ROSC from July 1, 2014 to December 31, 2015. Hemodynamic and inotrope administration data were retrieved for 3h after ROSC.

Abrupt rise of end tidal carbon dioxide level was a specific but non-sensitive marker of return of spontaneous circulation in patient with out-of-hospital cardiac arrest.

Objective: To evaluate the diagnostic accuracy of an abrupt and sustained increase in end-tidal carbon dioxide (ETCO2) to indicate return of spontaneous circulation (ROSC) during resuscitation of patient with out-of-hospital cardiac arrest.

Design: Cross-sectional study.

Setting: Emergency department of two regional hospitals.

Presenting hydrothorax predicts failure of needle aspiration in primary spontaneous pneumothorax.

Objective: The objective was to evaluate if existence of hydrothorax in initial chest radiograph predicts treatment outcome in patients with primary spontaneous pneumothorax who received needle thoracostomy.

Methods: This is a retrospective cohort study carried out from January 2011 to August 2014 in 1 public hospital in Hong Kong. All consecutive adult patients aged 18years or above who attended the emergency department with the diagnosis of primary spontaneous pneumothorax with needle aspiration performed as primary treatment were included.

Prognostication of out-of-hospital cardiac arrest patients by 3-min end-tidal capnometry level in emergency department.

Objective: To evaluate the role of initial ETCO2 value in prognostication of OHCA patients in an Asian-Chinese cohort.

Design: Prospective cohort study.

Setting: Emergency departments of two regional hospitals in a cluster of Hong Kong.

Utilization of the Accident & Emergency Departments by Chinese elderly in Hong Kong.

Background: The epidemiological data on elderly patients attending Accident and Emergency Departments (AEDs) in Hong Kong is lacking. The study aimed to examine the epidemiology of geriatric patient visits to AEDs in Hong Kong, including demographic data and predictors of life-saving interventions (LSI) and admission.

Methods: A retrospective cross-sectional study of geriatric patients older than 64 years old attending three AEDs during the year 2012, with a sample of 1 200 patient visits recruited.

Role of copeptin in dual-cardiac marker strategy for patients with chest pain presented to ED.

Objective: The objective of the study is to evaluate the role of copeptin in the diagnosis of acute coronary syndrome (ACS) and its role in dual-cardiac marker diagnostic strategy with troponin.

Design: A prospective cohort study was carried out from May 2012 to October 2012.

Setting: The study was conducted at the emergency department (ED) of a public hospital in a cluster of Hong Kong.

A one-versus-all class binarization strategy for bearing diagnostics of concurrent defects.

In bearing diagnostics using a data-driven modeling approach, a concern is the need for data from all possible scenarios to build a practical model for all operating conditions. This paper is a study on bearing diagnostics with the concurrent occurrence of multiple defect types. The authors are not aware of any work in the literature that studies this practical problem.