Article
Cardiac & Cardiovascular Systems
Gary Tse, Sharen Lee, Jiandong Zhou, Tong Liu, Ian Chi Kei Wong, Chloe Mak, Ngai Shing Mok, Kamalan Jeevaratnam, Qingpeng Zhang, Shuk Han Cheng, Wing Tak Wong
Summary: This study examined the local epidemiology of congenital long QT syndrome and significant risk factors for ventricular arrhythmias, showing that the random survival forest model outperformed traditional Cox regression in risk prediction.
FRONTIERS IN CARDIOVASCULAR MEDICINE
(2021)
Article
Cardiac & Cardiovascular Systems
Edi Prifti, Ahmad Fall, Giovanni Davogustto, Alfredo Pulini, Isabelle Denjoy, Christian Funck-Brentano, Yasmin Khan, Alexandre Durand-Salmon, Fabio Badilini, Quinn S. Wells, Antoine Leenhardt, Jean-Daniel Zucker, Dan M. Roden, Fabrice Extramiana, Joe-Elie Salem
Summary: The study aimed to enhance the prediction of drug-induced TdP and diagnosis of cLQTS using convolutional neural network models analyzing ECG alterations induced by sotalol. The CNN models outperformed QTc measurements and were particularly effective shortly after a diTdP episode.
EUROPEAN HEART JOURNAL
(2021)
Article
Pharmacology & Pharmacy
Fei Mu, Chen Cui, Meng Tang, Guiping Guo, Haiyue Zhang, Jie Ge, Yujia Bai, Jinyi Zhao, Shanshan Cao, Jingwen Wang, Yue Guan
Summary: This study aimed to construct a machine learning framework for stratified predicting and interpreting vancomycin-associated acute kidney injury (AKI). By analyzing the medical records of 724 patients, the study developed risk prediction models for different underlying diseases. The study also identified some underappreciated risk factors.
FRONTIERS IN PHARMACOLOGY
(2022)
Article
Cardiac & Cardiovascular Systems
Marina Rieder, Paul Kreifels, Judith Stuplich, David Ziupa, Helge Servatius, Luisa Nicolai, Alessandro Castiglione, Christiane Zweier, Babken Asatryan, Katja E. Odening
Summary: This study aimed to assess the association of different electrical parameters with the genotype and symptoms in patients with LQTS. The results showed that electrical parameters can distinguish between symptomatic and asymptomatic patients with different genetic forms of LQTS.
FRONTIERS IN CARDIOVASCULAR MEDICINE
(2022)
Article
Computer Science, Information Systems
Yufang Huang, Yifan Liu, Peter A. D. Steel, Kelly M. Axsom, John R. Lee, Sri Lekha Tummalapalli, Fei Wang, Jyotishman Pathak, Lakshminarayanan Subramanian, Yiye Zhang
Summary: DICE is a self-supervised learning framework that can identify clinically similar and risk-stratified subgroups with superior performance metrics and predictive power. Clinical evaluation shows that DICE-generated subgroups have predictive value for outcome prediction.
JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION
(2021)
Review
Nutrition & Dietetics
Sara D'Imperio, Michelle M. Monasky, Emanuele Micaglio, Gabriele Negro, Carlo Pappone
Summary: A healthy lifestyle is crucial for preventing cardiovascular diseases, particularly in inherited heart conditions such as BrS and LQTS. By adjusting dietary habits and lifestyle factors, the risk of arrhythmic events and mortality can be influenced.
Article
Medicine, General & Internal
Bingbing Ke, Renchun Gong, Aidong Shen, Hui Qiu, Hui Chen, Zhizhong Zhang, Weiping Li, Yuan Xie, Hongwei Li
Summary: This study explores the relationship between baseline or visit hemoglobin and major adverse cardiovascular and cerebral events (MACCE) in patients undergoing percutaneous coronary intervention (PCI), and constructs risk stratification models to predict MACCE. The findings suggest that visit hemoglobin and long-term hemoglobin changes are more predictive of MACCE risk than baseline hemoglobin levels. A new risk stratification model is established, which may efficiently prioritize targeted screening for at-risk anemic patients undergoing PCI.
ANNALS OF MEDICINE
(2023)
Article
Radiology, Nuclear Medicine & Medical Imaging
Wei Liu, Wei Wang, Hanyi Zhang, Miaoran Guo, Yingxin Xu, Xiaoqi Liu
Summary: The study aimed to develop a prediction model that combines clinical, radiomics, and deep features to stratify the risk level of thymoma using transfer learning. The study enrolled 150 thymoma patients who underwent surgical resection and pathology confirmation. Features were extracted from CT images, and statistical methods were used for feature selection. A fusion model integrating clinical, radiomics, and deep features was developed, and its performance was evaluated using various metrics. The fusion model outperformed the clinical, radiomics, and deep models in stratifying high and low risk of thymoma.
JOURNAL OF DIGITAL IMAGING
(2023)
Article
Chemistry, Multidisciplinary
Song Cui, Li Li, Yongjiang Zhang, Jianwei Lu, Xiuzhen Wang, Xiantao Song, Jinghua Liu, Kefeng Li
Summary: The study identified metabolic signatures associated with future risk of angina recurrence after PCI, allowing for the prediction of high-risk patients during remission. A multi-metabolite predictive model constructed from these signatures showed high accuracy, sensitivity, and specificity across independent cohorts, providing a potential tool for early intervention and treatment.
Article
Radiology, Nuclear Medicine & Medical Imaging
Li-Fan Wang, Qiao Wang, Feng Mao, Shi-Hao Xu, Li-Ping Sun, Ting-Fan Wu, Bo-Yang Zhou, Hao-Hao Yin, Hui Shi, Ya-Qin Zhang, Xiao-Long Li, Yi-Kang Sun, Dan Lu, Cong-Yu Tang, Hai-Xia Yuan, Chong-Ke Zhao, Hui-Xiong Xu
Summary: This study aimed to evaluate the diagnostic performance of machine learning-based ultrasound radiomics models for risk stratification of gallbladder masses. Radiomics features were extracted from grayscale ultrasound images and relevant features were selected. The results showed that the optimal XGBoost-based ultrasound radiomics model performed better than the conventional ultrasound model in discriminating neoplastic from non-neoplastic gallbladder lesions. It also showed higher diagnostic performance in discriminating carcinomas from benign gallbladder lesions compared to the conventional ultrasound model.
EUROPEAN RADIOLOGY
(2023)
Article
Biology
Tim Smole, Bojan Zunkovic, Matej Piculin, Enja Kokalj, Marko Robnik-Sikonja, Matjaz Kukar, Dimitrios Fotiadis, Vasileios C. Pezoulas, Nikolaos S. Tachos, Fausto Barlocco, Francesco Mazzarotto, Dejana Popovic, Lars Maier, Lazar Velicki, Guy A. MacGowan, Iacopo Olivotto, Nenad Filipovic, Djordje G. Jakovljevic, Zoran Bosnic
Summary: Machine learning was used to develop a novel risk stratification tool for hypertrophic cardiomyopathy, which outperformed existing models in predicting sudden cardiac death, cardiac death, and all-cause death risks. The boosted trees achieved the best performance in predicting cardiac events with high accuracies.
COMPUTERS IN BIOLOGY AND MEDICINE
(2021)
Article
Biochemical Research Methods
Jian Ruan, Shuaishuai Xu, Ruyin Chen, Wenxin Qu, Qiong Li, Chanqi Ye, Wei Wu, Qi Jiang, Feifei Yan, Enhui Shen, Qinjie Chu, Yunlu Jia, Xiaochen Zhang, Wenguang Fu, Jinzhang Chen, Michael P. Timko, Peng Zhao, Longjiang Fan, Yifei Shen
Summary: We developed an algorithm for metastasis prediction and risk stratification in intrahepatic cholangio-carcinoma (ICC) by integrating proteome and transcriptome data sets and using machine learning. The algorithm achieved high accuracy in predicting metastasis and showed correlation with overall survival in a clinical cohort. We also established a web-based server for easy access to the algorithm for clinical application.
BRIEFINGS IN BIOINFORMATICS
(2022)
Article
Cardiac & Cardiovascular Systems
Andrea Mazzanti, Alessandro Trancuccio, Deni Kukavica, Eleonora Pagan, Meng Wang, Muhammed Mohsin, Derick Peterson, Vincenzo Bagnardi, Wojciech Zareba, Silvia G. Priori
Summary: This study validated a novel risk score model for patients with long QT syndrome (LQTS) in a geographically diverse cohort from the USA and demonstrated its clinical implications. The validated model can aid clinicians in identifying high-risk LQTS patients who could benefit from an implantable cardioverter-defibrillator (ICD) and avoid unnecessary implants.
Article
Clinical Neurology
Isaac Lage, Thomas H. Jr Jr McCoy, Roy H. Perlis, Finale Doshi-Velez
Summary: This study developed and validated a model to predict treatment resistance in major depressive disorder using coded clinical data from electronic health records. The model showed good performance in a second health system, demonstrating its potential for real-world clinical applications.
JOURNAL OF AFFECTIVE DISORDERS
(2022)
Article
Immunology
Laura Wiffen, Leon Gerard D'Cruz, Thomas Brown, Tim W. Higenbottam, Jonathan A. Bernstein, Courtney Campbell, Joseph Moellman, Debajyoti Ghosh, Clive Richardson, Wynne Weston-Davies, Anoop J. Chauhan
Summary: Risk stratification and clinical triage in COVID-19 can be facilitated by utilizing serum biomarker bioprofiling and machine learning to develop useful tools for prognosis. Complement-mediated lung injury plays a key role in COVID-19 pneumonia, and preliminary results suggest the potential usefulness of a C5 inhibitor in COVID-19 recovery.
FRONTIERS IN IMMUNOLOGY
(2023)
Article
Chemistry, Analytical
Meng-Che Tu, Han-Yi Chen, Yuxi Wang, Shabbir M. Moochhala, Palaniappan Alagappan, Bo Liedberg
ANALYTICA CHIMICA ACTA
(2015)
Article
Chemistry, Analytical
Seng Koon Lim, Peng Chen, Fook Loy Lee, Shabbir Moochha, Bo Liedberg
ANALYTICAL CHEMISTRY
(2015)
Article
Cardiac & Cardiovascular Systems
Boon Yew Tan, Rita Yu Yin Yong, Hector Barajas-Martinez, Robert Dumaine, Ying Xia Chew, Pavandip Singh Wasan, Chi Keong Ching, Kah Leng Ho, Linda Seo Hwee Gan, Nathalie Morin, Alicia Poh Leng Chong, Shiao Hui Yap, Jia Ling Neo, Eric Peng Huat Yap, Shabbir Moochhala, Daniel Thuan Tee Chong, Weien Chow, Swee Chong Seow, Dan Hu, Mahesh Uttamchandani, Wee Siong Teo
Article
Critical Care Medicine
Yong Chiat Wong, Yi Yang Lai, Mui Hong Tan, Chuen Seng Tan, Jian Wu, Lewis Zheng, Jie Zeng, Jia Lu, Shabbir Moochhala
Article
Transplantation
Linda Shavit, Lucia Chen, Fayha Ahmed, Pietro Manuel Ferraro, Shabbir Moochhala, Steven B. Walsh, Robert Unwin
NEPHROLOGY DIALYSIS TRANSPLANTATION
(2016)
Article
Urology & Nephrology
Linda Shavit, Daniela Girfoglio, Alex Kirkham, Darrell Allen, Pietro Manuel Ferraro, Shabbir Moochhala, Robert Unwin
Article
Chemistry, Analytical
Meng-Che Tu, Hari Krishna Svm, Alahakoon Thilini, Lim Tse Loong Wallace, Shabbir Moochhala, Umit Hakan Yildiz, Al. Palaniappan, Bo Liedberg
SENSORS AND ACTUATORS B-CHEMICAL
(2017)
Article
Cardiac & Cardiovascular Systems
Boon Yew Tan, Luokai Wang, Mahesh Uttamchandani, Hector Barajas-Martinez, Robert Dumaine, Nathalie Morin, Chi Keong Ching, Kah Leng Ho, Daniel Thuan Tee Chong, Weien Chow, Eric Peng Huat Yap, Shabbir Moochhala, Dan Hu, Rita Yu Yin Yong, Wee Siong Teo
JOURNAL OF ELECTROCARDIOLOGY
(2018)
Article
Transplantation
Linda Shavit, Pietro Manuel Ferraro, Nikhil Johri, William Robertson, Steven B. Walsh, Shabbir Moochhala, Robert Unwin
NEPHROLOGY DIALYSIS TRANSPLANTATION
(2015)
Article
Urology & Nephrology
Pietro Manuel Ferraro, Miguel Angel Arrabal-Polo, Giovambattista Capasso, Emanuele Croppi, Adamasco Cupisti, Thomas Ernandez, Daniel G. Fuster, Juan Antonio Galan, Felix Grases, Ewout J. Hoorn, Felix Knauf, Emmanuel Letavernier, Nilufar Mohebbi, Shabbir Moochhala, Kremena Petkova, Agnieszka Pozdzik, John Sayer, Christian Seitz, Pasquale Strazzullo, Alberto Trinchieri, Giuseppe Vezzoli, Corrado Vitale, Liffert Vogt, Robert J. Unwin, Olivier Bonny, Giovanni Gambaro
Article
Microbiology
Ryan Yuki Huang, Deron Raymond Herr, Shabbir Moochhala
Article
Biochemistry & Molecular Biology
Anna Karen Carrasco Laserna, Yiyang Lai, Guihua Fang, Rajaseger Ganapathy, Mohamed Shirhan Bin Mohamed Atan, Jia Lu, Jian Wu, Mahesh Uttamchandani, Shabbir M. Moochhala, Sam Fong Yau Li
Proceedings Paper
Engineering, Electrical & Electronic
Wee Jin Koh, Shabbir M. Moochhala
2018 JOINT IEEE INTERNATIONAL SYMPOSIUM ON ELECTROMAGNETIC COMPATIBILITY AND 2018 IEEE ASIA-PACIFIC SYMPOSIUM ON ELECTROMAGNETIC COMPATIBILITY (EMC/APEMC)
(2018)
Proceedings Paper
Optics
Katherine V. Oliver, Faith Matjiu, Cameron Davey, Shabbir Moochhala, Robert J. Unwin, Peter R. Rich
OPTICAL DIAGNOSTICS AND SENSING XV: TOWARD POINT-OF-CARE DIAGNOSTICS
(2015)
Article
Surgery
Elijah Zhengyang Cai, Chuan Han Ang, Ashvin Raju, Kong Bing Tan, Eileen Chor Hoong Hing, Yihua Loo, Yong Chiat Wong, Hanjing Lee, Jane Lim, Shabbir M. Moochhala, Charlotte A. E. Hauser, Thiam Chye Lim
ARCHIVES OF PLASTIC SURGERY-APS
(2014)
Review
Computer Science, Artificial Intelligence
Wei Gao, Shuangshuang Ge
Summary: This study provides a comprehensive review of slope stability research based on artificial intelligence methods, focusing on slope stability computation and evaluation. The review covers studies using quasi-physical intelligence methods, simulated evolutionary methods, swarm intelligence methods, hybrid intelligence methods, artificial neural network methods, vector machine methods, and other intelligence methods. The merits, demerits, and state-of-the-art research advancement of these studies are analyzed, and possible research directions for slope stability investigation based on artificial intelligence methods are suggested.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Khuong Le Nguyen, Hoa Thi Trinh, Saeed Banihashemi, Thong M. Pham
Summary: This study investigated the influence of input parameters on the shear strength of RC squat walls and found that ensemble learning models, particularly XGBoost, can effectively predict the shear strength. The axial load had a greater influence than reinforcement ratio, and longitudinal reinforcement had a more significant impact compared to horizontal and vertical reinforcement. The performance of XGBoost model outperforms traditional design models and reducing input features still yields reliable predictions.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Bo Hu, Huiyan Zhang, Xiaoyi Wang, Li Wang, Jiping Xu, Qian Sun, Zhiyao Zhao, Lei Zhang
Summary: A deep hierarchical echo state network (DHESN) is proposed to address the limitations of shallow coupled structures. By using transfer entropy, candidate variables with strong causal relationships are selected and a hierarchical reservoir structure is established to improve prediction accuracy. Simulation results demonstrate that DHESN performs well in predicting algal bloom.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Limin Wang, Lingling Li, Qilong Li, Kuo Li
Summary: This paper discusses the urgency of learning complex multivariate probability distributions due to the increase in data variability and quantity. It introduces a highly scalable classifier called TAN, which utilizes maximum weighted spanning tree (MWST) for graphical modeling. The paper theoretically proves the feasibility of extending one-dependence MWST to model high-dependence relationships and proposes a heuristic search strategy to improve the fitness of the extended topology to data. Experimental results demonstrate that this algorithm achieves a good bias-variance tradeoff and competitive classification performance compared to other high-dependence or ensemble learning algorithms.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Zhejing Hu, Gong Chen, Yan Liu, Xiao Ma, Nianhong Guan, Xiaoying Wang
Summary: Anxiety is a prevalent issue and music therapy has been found effective in reducing anxiety. To meet the diverse needs of individuals, a novel model called the spatio-temporal therapeutic music transfer model (StTMTM) is proposed.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Nur Ezlin Zamri, Mohd. Asyraf Mansor, Mohd Shareduwan Mohd Kasihmuddin, Siti Syatirah Sidik, Alyaa Alway, Nurul Atiqah Romli, Yueling Guo, Siti Zulaikha Mohd Jamaludin
Summary: In this study, a hybrid logic mining model was proposed by combining the logic mining approach with the Modified Niche Genetic Algorithm. This model improves the generalizability and storage capacity of the retrieved induced logic. Various modifications were made to address other issues. Experimental results demonstrate that the proposed model outperforms baseline methods in terms of accuracy, precision, specificity, and correlation coefficient.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
David Jacob Kedziora, Tien-Dung Nguyen, Katarzyna Musial, Bogdan Gabrys
Summary: The paper addresses the problem of efficiently optimizing machine learning solutions by reducing the configuration space of ML pipelines and leveraging historical performance. The experiments conducted show that opportunistic/systematic meta-knowledge can improve ML outcomes, and configuration-space culling is optimal when balanced. The utility and impact of meta-knowledge depend on various factors and are crucial for generating informative meta-knowledge bases.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
G. Sophia Jasmine, Rajasekaran Stanislaus, N. Manoj Kumar, Thangamuthu Logeswaran
Summary: In the context of a rapidly expanding electric vehicle market, this research investigates the ideal locations for EV charging stations and capacitors in power grids to enhance voltage stability and reduce power losses. A hybrid approach combining the Fire Hawk Optimizer and Spiking Neural Network is proposed, which shows promising results in improving system performance. The optimization approach has the potential to enhance the stability and efficiency of electric grids.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Zhijiang Wu, Guofeng Ma
Summary: This study proposes a natural language processing-based framework for requirement retrieval and document association, which can help to mine and retrieve documents related to project managers' requirements. The framework analyzes the ontology relevance and emotional preference of requirements. The results show that the framework performs well in terms of iterations and threshold, and there is a significant matching between the retrieved documents and the requirements, which has significant managerial implications for construction safety management.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Yung-Kuan Chan, Chuen-Horng Lin, Yuan-Rong Ben, Ching-Lin Wang, Shu-Chun Yang, Meng-Hsiun Tsai, Shyr-Shen Yu
Summary: This study proposes a novel method for dog identification using nose-print recognition, which can be applied to controlling stray dogs, locating lost pets, and pet insurance verification. The method achieves high recognition accuracy through two-stage segmentation and feature extraction using a genetic algorithm.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Shaohua Song, Elena Tappia, Guang Song, Xianliang Shi, T. C. E. Cheng
Summary: This study aims to optimize supplier selection and demand allocation decisions for omni-channel retailers in order to achieve supply chain resilience. It proposes a two-phase approach that takes into account various factors such as supplier evaluation and demand allocation.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Jinyan Hu, Yanping Jiang
Summary: This paper examines the allocation problem of shared parking spaces considering parking unpunctuality and no-shows. It proposes an effective approach using sample average approximation (SAA) combined with an accelerating Benders decomposition (ABD) algorithm to solve the problem. The numerical experiments demonstrate the significance of supply-demand balance for the operation and user satisfaction of the shared parking system.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Review
Computer Science, Artificial Intelligence
Soroor Motie, Bijan Raahemi
Summary: Financial fraud is a persistent problem in the finance industry, but Graph Neural Networks (GNNs) have emerged as a powerful tool for detecting fraudulent activities. This systematic review provides a comprehensive overview of the current state-of-the-art technologies in using GNNs for financial fraud detection, identifies gaps and limitations in existing research, and suggests potential directions for future research.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Review
Computer Science, Artificial Intelligence
Enhao Ning, Changshuo Wang, Huang Zhang, Xin Ning, Prayag Tiwari
Summary: This review provides a detailed overview of occluded person re-identification methods and conducts a systematic analysis and comparison of existing deep learning-based approaches. It offers important theoretical and practical references for future research in the field.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Jiajun Ma, Songyu Hu, Jianzhong Fu, Gui Chen
Summary: The article presents a novel visual hierarchical attention detector for multi-scale defect location and classification, utilizing texture, semantic, and instance features of defects through a hierarchical attention mechanism, achieving multi-scale defect detection in bearing images with complex backgrounds.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)