Risk Stratification with Extreme Learning Machine: A Retrospective Study on Emergency Department Patients

This paper presents a novel risk stratification method using extreme learning machine (ELM). ELM was integrated into a scoring system to identify the risk of cardiac arrest in emergency department (ED) patients. The experiments were conducted on a cohort of 1025 critically ill patients presented to the ED of a tertiary hospital. ELM and voting based ELM (V-ELM) were evaluated. To enhance the prediction performance, we proposed a selective V-ELM (SV-ELM) algorithm. The results showed that ELM based scoring methods outperformed support vector machine (SVM) based scoring method in the receiver operation characteristic analysis

Ni-based bimetallic heterogeneous catalysts for energy and environmental applications

Bimetallic catalysts have attracted extensive attention for a wide range of applications in energy production and environmental remediation due to their tunable chemical/physical properties. These properties are mainly governed by a number of parameters such as compositions of the bimetallic systems, their preparation method, and their morphostructure. In this regard, numerous efforts have been made to develop “designer” bimetallic catalysts with specific nanostructures and surface properties as a result of recent advances in the area of materials chemistry. The present review highlights a detailed overview of the development of nickel-based bimetallic catalysts for energy and environmental applications. Starting from a materials science perspective in order to obtain controlled morphologies and surface properties, with a focus on the fundamental understanding of these bimetallic systems to make a correlation with their catalytic behaviors, a detailed account is provided on the utilization of these systems in the catalytic reactions related to energy production and environmental remediation. We include the entire library of nickel-based bimetallic catalysts for both chemical and electrochemical processes such as catalytic reforming, dehydrogenation, hydrogenation, electrocatalysis and many other reactions

Gaps in Singapore’s 3Ms healthcare safety net : the 4th M – membership.

Healthcare costs are rising but healthcare remains a basic need. It is important that healthcare remains affordable to Singaporeans. This paper thus aims to evaluate the comprehensiveness of Singapore's existing healthcare safety net of Medisave, Medishield and Medifund -- the 3Ms. Our findings suggest that there are five groups of individuals who still fall through the perceived well-constructed safety net. Termed as the uninsured, they are the middle income group; elderly; housewives; students; and those with congenital anomalies, hereditary conditions and disorders. To strengthen the safety net, we propose a radical inclusion into Singapore's healthcare system: the 4th M, Membership – A flat rate all-inclusive Medishield.Bachelor of Art

Evolutionary Voting-Based Extreme Learning Machines

Voting-based extreme learning machine (V-ELM) was proposed to improve learning efficiency where majority voting was employed. V-ELM assumes that all individual classifiers contribute equally to the decision ensemble. However, in many real-world scenarios, this assumption does not work well. In this paper, we aim to enhance V-ELM by introducing weights to distinguish the importance of each individual ELM classifier in decision making. Genetic algorithm is used for optimizing these weights. This evolutionary V-ELM is named as EV-ELM. Results on several benchmark databases show that EV-ELM achieves the highest classification accuracy compared with V-ELM and ELM

Ensemble-Based Risk Scoring with Extreme Learning Machine for Prediction of Adverse Cardiac Events

Accurate prediction of adverse cardiac events for the emergency department (ED) chest pain patients is essential in risk stratification due to the current ambiguity in diagnosing acute coronary syndrome. While most current practices rely on human decision by measuring clinical vital signs, computerized solutions are gaining popularity. We have previously proposed an ensemble-based scoring system (ESS). In this paper, we aim to extend the ESS system using extreme learning machine (ELM), a fast learning algorithm for neural networks. We recruited patients from the ED of Singapore General Hospital, and extracted features such as heart rate variability, 12-lead ECG parameters, and vital signs. We also proposed a novel algorithm called ESS-ELM to predict adverse cardiac events. Different from the original ESS algorithm, ESS-ELM uses the under-sampling technique only in model training. Our proposed method was compared to the original ESS algorithm and several clinical scores in predicting patient outcome. With a cohort of 797 recruited patients, we demonstrated that ESS-ELM outperformed the original ESS algorithm and three established clinical scores, namely HEART, TIMI, and GRACE, in terms of receiver operating characteristic analysis. Furthermore, we have investigated the impact of hidden node number and ensemble size on the predictive performance. ELM has demonstrated the flexibility in its integration with the ESS algorithm. Experiments showed the value of ESS-ELM in prediction of adverse cardiac events. Future works may include the use of new ELM-based learning methods and further validation with a new cohort of patients.Accepted versio

Utilizing machine learning dimensionality reduction for risk stratification of chest pain patients in the emergency department

10.1186/s12874-021-01265-2BMC Medical Research Methodology2117

Heart Rate Variability Analysis in Patients Who Have Bradycardia Presenting to the Emergency Department with Chest Pain

Background: Heart rate variability (HRV) is a noninvasive method to measure the function of the autonomic nervous system. It has been used to risk stratify patients with undifferentiated chest pain in the emergency department (ED). However, bradycardia can have a modifying effect on HRV. Objective: In this study, we aimed to determine how bradycardia affected HRV analysis in patients who presented with chest pain to the ED. Methods: Adult patients presenting to the ED at Singapore General Hospital with chest pain were included in the study. Patients with non-sinus rhythm on electrocardiogram (ECG) were excluded. HRV parameters, including time domain, frequency domain, and nonlinear variables, were analyzed from a 5-min ECG segment. Occurrence of a major adverse cardiac event ([ MACE], e.g., acute myocardial infarction, percutaneous coronary intervention, coronary artery bypass graft, or mortality) within 30 days of presentation to the ED was also recorded. Results: A total of 797 patients were included for analysis with 248 patients (31.1%) with 30-day MACE and 135 patients with bradycardia (16.9%). Compared to non-bradycardic patients, bradycardic patients had significant differences in all HRV parameters suggesting an increased parasympathetic component. Among non-bradycardic patients, comparing those who did and did not have 30-day MACE, there were significant differences predominantly in time domain variables, suggesting decreased HRV. In bradycardic patients, the same analysis revealed significant differences in predominantly frequency-domain variables suggesting decreased parasympathetic input. Conclusions: Chest pain patients with bradycardia have increased HRV compared to those without bradycardia. This may have important implications on HRV modeling strategies for risk stratification of bradycardic and non-bradycardic chest pain patients. (c) 2017 Elsevier Inc. All rights reserved