Article
Computer Science, Information Systems
Ellen Wright Clayton, Peter J. Embi, Bradley A. Malin
Summary: The recent overturning of established case law by the Supreme Court on the Constitutional right to abortion for pregnant individuals has led to restrictions or bans on abortion in some states, affecting healthcare delivery and patient privacy.
JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION
(2022)
Article
Computer Science, Information Systems
Judith Santos-Pereira, Le Gruenwald, Jorge Bernardino
Summary: This paper presents a survey of popular open-source data mining tools and proposes tool selection criteria based on healthcare application requirements. KNIME and RapidMiner are identified as the best tools for healthcare data mining.
JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES
(2022)
Article
Engineering, Environmental
Edoardo Ramalli, Timoteo Dinelli, Andrea Nobili, Alessandro Stagni, Barbara Pernici, Tiziano Faravelli
Summary: Validation and analysis of experiments and models are crucial in various engineering fields. This study proposes a systematic and automated methodology that utilizes the concept of a 'data ecosystem' to provide comprehensive insights about experiments and predictive models. The methodology focuses on data assessment, model performance measurement, and behavior insight extraction through data science techniques. It can be applied to different domains where predictive models are validated against big data in chemical engineering.
CHEMICAL ENGINEERING JOURNAL
(2023)
Article
Multidisciplinary Sciences
Fatemeh Amrollahi, Supreeth P. Shashikumar, Andre L. Holder, Shamim Nemati
Summary: A privacy-preserving learning algorithm named WUPERR was developed and validated using data from four healthcare systems. The algorithm has the ability to transfer knowledge across institutional boundaries and learn from new patient cohorts without forgetting previously learned patterns.
SCIENTIFIC REPORTS
(2022)
Article
Computer Science, Information Systems
Arshia Rehman, Saeeda Naz, Imran Razzak
Summary: Clinical decisions are benefiting from evidence-based big data analytics, promising early detection, prediction, prevention and quality of life improvement. Various tools and techniques are used to process healthcare data, while sub-disciplines in healthcare are exploring the potential of big data. Challenges and notable applications in healthcare big data analytics are discussed, indicating a positive impact on healthcare.
MULTIMEDIA SYSTEMS
(2022)
Article
Business
Roozmehr Safi
Summary: As e-commerce expands, more sellers are providing unassembled product kits to improve efficiency and reduce costs. However, this practice shifts assembly work to consumers, impacting their satisfaction and recommendations.
JOURNAL OF BUSINESS RESEARCH
(2022)
Review
Pharmacology & Pharmacy
Hiroshi Komura, Reiko Watanabe, Hitoshi Kawashima, Rikiya Ohashi, Masataka Kuroda, Tomohiro Sato, Teruki Honma, Kenji Mizuguchi
Summary: Supported by the Japan Agency for Medical Research and Development, a novel framework for a public-private partnership was established for the Development of a Drug Discovery Informatics System. The partnership consortium developed a database of pharmacokinetic and cardiotoxic properties by integrating proprietary data from private-sector members, resulting in robust in silico prediction models with higher performance. This unique partnership has the potential to substantially strengthen drug discovery capabilities in both sectors.
DRUG DISCOVERY TODAY
(2021)
Article
Multidisciplinary Sciences
Vishu Gupta, Kamal Choudhary, Francesca Tavazza, Carelyn Campbell, Wei-Keng Liao, Alok Choudhary, Ankit Agrawal
Summary: Artificial intelligence and machine learning are increasingly used in materials science, but limited availability of large datasets for most properties hinders their widespread application. Researchers propose a cross-property deep-transfer-learning framework to build accurate models with limited data.
NATURE COMMUNICATIONS
(2021)
Article
Engineering, Multidisciplinary
Wensheng Gan, Kaixia Hu, Gengsen Huang, Wei-Che Chien, Han-Chieh Chao, Weizhi Meng
Summary: This article introduces the application of AI-powered healthcare cyber-physical systems in healthcare services and the techniques of data analysis. The authors propose the problem of contiguous negative sequential pattern mining and present a novel algorithm to address this problem. Through experiments and analysis, it is demonstrated that the proposed algorithm can effectively discover meaningful patterns from medical data.
IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING
(2023)
Review
Computer Science, Artificial Intelligence
Vankamamidi S. Naresh, Muthusamy Thamarai
Summary: Data mining and machine learning applications in medical diagnostic systems are growing, but data privacy is a major concern due to the sensitive nature of healthcare data. This article discusses the privacy and security challenges in these systems and explores privacy-preserving computation techniques for secure data evaluation and processing. The state-of-the-art applications and open challenges in healthcare are analyzed, including privacy-preserving data mining and machine learning, and federated learning.
WILEY INTERDISCIPLINARY REVIEWS-DATA MINING AND KNOWLEDGE DISCOVERY
(2023)
Article
Computer Science, Software Engineering
Furui Cheng, Dongyu Liu, Fan Du, Yanna Lin, Alexandra Zytek, Haomin Li, Huamin Qu, Kalyan Veeramachaneni
Summary: Machine learning is increasingly used in Electronic Health Records for clinical prediction tasks, but issues with model transparency and interpretability limit its adoption in clinical practice. By collaborating with clinicians, three key challenges were identified: clinicians' unfamiliarity with ML features, lack of contextual information, and the need for cohort-level evidence.
IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS
(2022)
Article
Medicine, General & Internal
Jenna M. Reps, Patrick Ryan, P. R. Rijnbeek
Summary: The study aimed to quantifying the generalisability of prediction models by investigating the impact of different development and internal validation designs in big data. Results showed that even with large data, models tend to overfit without a proper validation process. Validation processes to select hyperparameters and assess internal validation are crucial for accurate model performance evaluation.
Review
Medicine, General & Internal
Sri Venkat Gunturi Subrahmanya, Dasharathraj K. Shetty, Vathsala Patil, B. M. Zeeshan Hameed, Rahul Paul, Komal Smriti, Nithesh Naik, Bhaskar K. Somani
Summary: Data science plays a crucial role in healthcare applications, assisting in handling and analyzing large amounts of medical data to support healthcare decisions. Through data-driven approaches, new possibilities are opened up for improving healthcare system quality and decision-making effectiveness.
IRISH JOURNAL OF MEDICAL SCIENCE
(2022)
Article
Computer Science, Hardware & Architecture
Ahmad Musamih, Khaled Salah, Raja Jayaraman, Ibrar Yaqoob, Deepak Puthal, Samer Ellahham
Summary: NFTs, representing ownership of unique items stored on a blockchain, have gained popularity as digital assets in various sectors. This article explores the potential opportunities and challenges of using NFTs in healthcare, including supply chain management, patient-centric data management, digital twins, clinical trial management, and genomics.
IEEE CONSUMER ELECTRONICS MAGAZINE
(2023)
Review
Biochemical Research Methods
Ke Shen, Ahmad Ud Din, Baivab Sinha, Yi Zhou, Fuliang Qian, Bairong Shen
Summary: With the rapid development of human intestinal microbiology and diverse microbiome-related studies and investigations, a large amount of data have been generated and accumulated. Meanwhile, different computational and bioinformatics models have been developed for pattern recognition and knowledge discovery using these data. Given the heterogeneity of these resources and models, we aimed to provide a landscape of the data resources, a comparison of the computational models, and a summary of the translational informatics applied to microbiota data.
BRIEFINGS IN BIOINFORMATICS
(2023)
Article
Health Care Sciences & Services
Shahadat Uddin, Shangzhou Wang, Arif Khan, Haohui Lu
Summary: This study examines the progression of chronic diseases and their risk factors using a healthcare dataset sample of hospitalized patients. The results show that certain chronic diseases, such as cardiovascular diseases and diabetes, have a high prevalence in progressing to other chronic diseases, which is statistically significant. The progression frequencies increase with time and age, and the patients' sex also affects the disease progressions differently.
Article
Biology
Phasit Charoenkwan, Chonlatip Pipattanaboon, Chanin Nantasenamat, Md Mehedi Hasan, Mohammad Ali Moni, Pietro Lio, Watshara Shoombuatong
Summary: Despite existing cancer therapies, the development of new and effective treatments is necessary to address the ongoing cancer recurrence and new cases. This study proposes a new machine learning-based approach, PSRTTCA, for improving the identification and characterization of tumor T cell antigens (TTCAs) based on their primary sequences.
COMPUTERS IN BIOLOGY AND MEDICINE
(2023)
Article
Multidisciplinary Sciences
Shahadat Uddin, Stephen Ong, Petr Matous
Summary: Stakeholder engagement is a crucial factor affecting project outcomes, but there is a lack of empirical evidence on the differences in stakeholder engagement patterns between public, private, and public-private partnership (PPP) projects. This study uses social network research methods to capture and compare these engagement structures quantitatively. The findings reveal significant differences in network size, edge number, density, and betweenness centralization across the three types of projects. Additionally, the density varies significantly between 'within budget' and cost overrun projects for private and PPP projects. The study highlights the importance of network data and analytical techniques in managing relationships in complex project ecosystems.
Article
Computer Science, Artificial Intelligence
Taima Rahman Mim, Maliha Amatullah, Sadia Afreen, Mohammad Abu Yousuf, Shahadat Uddin, Salem A. Alyami, Khondokar Fida Hasan, Mohammad Ali Moni
Summary: Human Activity Recognition (HAR) is a valuable research field for clinical applications, where machine learning algorithms play a significant role. The proposed Gated Recurrent Unit-Inception (GRU-INC) model effectively utilizes both temporal and spatial information of time-series data, achieving high F1-scores on various publicly available datasets. The combination of GRU with Attention Mechanism and Inception module with Convolutional Block Attention Module (CBAM) contributes to the superior recognition rate and lower computational cost of the GRU-INC model. This framework has the potential to be applied in activity-associated clinical and rehabilitation applications.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Article
Computer Science, Artificial Intelligence
Md Shofiqul Islam, Khondokar Fida Hasan, Sunjida Sultana, Shahadat Uddin, Pietro Lio', Julian M. W. Quinn, Mohammad Ali Moni
Summary: We propose a hybrid hierarchical attention-based bidirectional recurrent neural network with dilated CNN (HARDC) method for arrhythmia classification. This method fully exploits the dilated CNN and bidirectional recurrent neural network unit (BiGRU-BiLSTM) architecture to generate fusion features, improving the model's performance for prediction. By combining the fusion features with a dilated CNN and a hierarchical attention mechanism, the trained HARDC model showed significantly improved classification results and interpretability of feature extraction on the PhysioNet 2017 challenge dataset.
Article
Chemistry, Multidisciplinary
Nazim Choudhury, Shahadat Uddin
Summary: One of the characteristics of dynamic networks is the evolution of their actors and links. The link prediction mechanism in dynamic networks can capture the growth mechanisms of social networks. Researchers have utilized the temporal patterns of dynamic networks for dynamic link prediction. However, little attention has been given to the temporal variations of actor-level network structure and neighborhood information. This study attempts to build dynamic similarity metrics considering the temporal similarity and correlation between different actor-level evolutionary information of non-connected actor pairs. These metrics are used as dynamic features in the link prediction model and show improved performance compared to static similarity metrics.
APPLIED SCIENCES-BASEL
(2023)
Article
Medicine, General & Internal
Md. Tarek Aziz, S. M. Hasan Mahmud, Md. Fazla Elahe, Hosney Jahan, Md Habibur Rahman, Dip Nandi, Lassaad K. Smirani, Kawsar Ahmed, Francis M. Bui, Mohammad Ali Moni
Summary: In this paper, a hybrid framework was proposed to improve the efficiency of osteosarcoma tumor classification by merging different types of CNN-based architectures with a multilayer perceptron algorithm. The proposed model achieved high accuracy for both multiclass and binary classification of osteosarcoma, outperforming existing methods. Experimental findings indicate the potential applicability of this model in supporting osteosarcoma diagnosis in clinics.
Review
Health Care Sciences & Services
Palak Mahajan, Shahadat Uddin, Farshid Hajati, Mohammad Ali Moni
Summary: Machine learning models are utilized to create and improve disease prediction frameworks, and ensemble learning is a technique that combines multiple classifiers to enhance performance. In this study, the performance accuracies of different ensemble techniques (bagging, boosting, stacking, and voting) are assessed against five highly researched diseases. The findings reveal that stacking has the most accurate performance and can assist researchers in understanding current trends in disease prediction models that employ ensemble learning.
Article
Computer Science, Hardware & Architecture
Md. Monirul Islam, Mohammod Abul Kashem, Salem A. Alyami, Mohammad Ali Moni
Summary: This paper presents an IoT framework for aquaculture that allows real-time monitoring and effective control of water-related parameters. The proposed system utilizes sensors and an Arduino microcontroller to collect and store data in an IoT cloud platform. The collected data is then analyzed using various machine learning algorithms, with Random Forest achieving the highest performance scores. The study also includes hardware details of the IoT system and calculates biochemical and chemical oxygen demands.
MICROPROCESSORS AND MICROSYSTEMS
(2023)
Article
Genetics & Heredity
Rabea Khatun, Maksuda Akter, Md. Manowarul Islam, Md. Ashraf Uddin, Md. Alamin Talukder, Joarder Kamruzzaman, Akm Azad, Bikash Kumar Paul, Muhammad Ali Abdulllah Almoyad, Sunil Aryal, Mohammad Ali Moni
Summary: This article proposes an ensemble rank-based feature selection method and classifier to address the challenge of high-dimensional data in cancer diagnosis. The method efficiently discovers the most relevant and useful features by aggregating rankings from different selection methods. The results show high accuracy on multiple datasets and the study identifies a subset of the most important cancer-causing genes and demonstrates their significance.
Article
Computer Science, Interdisciplinary Applications
Shahadat Uddin, Arif Khan, Haohui Lu
Summary: Research on COVID-19 has seen significant growth in recent years and has been a dominant topic in health-related publications. This study explores the impact of COVID-19 research on journal performance using the Impact Factor and six years of data. The results show that journals publishing COVID-19-related articles experienced a significant increase in their Impact Factor, with lower Impact Factor journals contributing the most to this growth. It suggests that journals prioritizing COVID-19 research may experience increased visibility and Impact Factor growth in the long term.
JOURNAL OF INFORMETRICS
(2023)
Proceedings Paper
Computer Science, Theory & Methods
Fangyu Zhou, Shahadat Uddin
Summary: In recent years, there has been an exponential growth in drug-related data and adverse drug reactions (ADRs), leading to a comparatively high hospitalization rate worldwide. To minimize risks, extensive research has been conducted to predict ADRs. Due to the high cost and time-consuming nature of lab experiments, researchers are exploring the use of data mining and machine learning techniques in this field. This paper constructs a weighted drug-drug network by integrating various data sources, revealing underlying relationships between drugs based on common ADRs. Network features are extracted from this network, such as weighted degree centrality and weighted PageRanks, which are concatenated with original drug features to train and test seven classical machine learning algorithms. Experiment results show that adding these network measures benefits all tested machine learning methods, with logistic regression achieving the highest mean AUROC score (0.821) across all ADRs. Weighted degree centrality and weighted PageRanks are identified as the most important network features in the logistic regression classifier. This evidence strongly supports the fundamental role of the network approach in future ADR prediction, where network edge weights play a crucial role in the logistic regression model.
PROCEEDINGS OF 2023 AUSTRALIAN COMPUTER SCIENCE WEEK, ACSW 2023
(2023)