Review
Food Science & Technology
Leonieke M. van den Bulk, Yamine Bouzembrak, Anand Gavai, Ningjing Liu, Lukas J. van den Heuvel, Hans J. P. Marvin
Summary: Systematic reviews are an important method used in researching food safety topics. However, they can be time-consuming and require expert knowledge. This study aims to reduce the time needed by using machine learning techniques to automatically classify relevant articles. The results show that this approach can save experts time while maintaining the quality of the review.
CURRENT RESEARCH IN FOOD SCIENCE
(2022)
Article
Multidisciplinary Sciences
Qinghua Wang, Jonathan Olshin, K. Vijay-Shanker, Cathy H. Wu
Summary: Chinese hamster ovary (CHO) cells are widely used in the pharmaceutical industry for mass production of therapeutic proteins. Research on CHO cell line development and bioprocess has been increasing in recent decades. Bibliographic mapping and classification of relevant research studies are important for identifying research gaps and trends.
Review
Computer Science, Interdisciplinary Applications
Alexander Schniedermann
Summary: The question of how citation impact relates to academic quality is a recurring theme in bibliometric research. While experts have used more complex conceptions of research quality for evaluation, detailed analyses of how impact relates to dimensions like methodological rigor are lacking. Increasing formal guidelines for biomedical research not only provide insight into the social dynamics of standardization, but also their relationships to scientific rewards.
Review
Business
Ahmet Yucel, Ali Dag, Asil Oztekin, Mark Carpenter
Summary: The objective of this study is to classify customer reviews for four different types of products/services. A novel classification framework is built by devising a unique classifier that includes rich information gathered from all extracted features. The proposed framework outperforms other methods for each dataset employed.
JOURNAL OF BUSINESS RESEARCH
(2022)
Review
Computer Science, Artificial Intelligence
David M. Goldberg, Alan S. Abrahams
Summary: Many firms struggle with monitoring product safety due to the potential negative impacts on consumers and financial standings. Monitoring online reviews can provide important safety insights, but the large volume of data poses practical challenges. This study proposes two new methods for identifying safety hazards, which show improvement over traditional approaches and demonstrate promise for cross-category analysis.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Article
Automation & Control Systems
Mohamed Yassine Landolsi, Lobna Hlaoua, Lotfi Ben Romdhane
Summary: The paper presents an automatic section detection method in the medical field to improve information extraction tasks. Rules were constructed to prepare the training set, and a machine learning model was trained using various features to find titles. Experiments showed that combining these features using a Convolutional Neural Network led to better results in real medical documents.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2023)
Article
Computer Science, Hardware & Architecture
Shengbin Liang, Fuqi Sun, Haoran Sun, Tingting Chen, Wencai Du
Summary: This paper introduces a Chinese medical text classification model using a BERT-based Chinese text encoder, N-gram representations, and a capsule network. The model extracts features using the capsule network and enhances medical text representation and feature extraction through the design of an N-gram medical dictionary. The experimental results demonstrate that the model outperforms the baseline models in terms of precision, recall, and F1-score.
JOURNAL OF SUPERCOMPUTING
(2023)
Article
Computer Science, Artificial Intelligence
Mickael Febrissy, Aghiles Salah, Melissa Ailem, Mohamed Nadif
Summary: Non-negative Matrix Factorization (NMF) and its variants are widely used for clustering text documents. However, these methods do not explicitly consider the contextual dependencies between words. To address this issue, researchers draw inspiration from neural word embedding and propose jointly factorizing the document-word and word-word co-occurrence matrices. Empirical results show that this approach significantly improves the clustering performance of NMF on multiple real-world datasets.
Article
Computer Science, Information Systems
Min Zhang, Brandon Fan, Ning Zhang, Wenjun Wang, Weiguo Fan
Summary: Online customer reviews play a crucial role in product innovation, but current research lacks focus on extracting innovation ideas from reviews. This study introduces a deep learning-based approach that effectively identifies sentences containing innovation ideas from online reviews.
INFORMATION PROCESSING & MANAGEMENT
(2021)
Review
Computer Science, Information Systems
Raymon van Dinter, Bedir Tekinerdogan, Cagatay Catal
Summary: This study conducts a systematic literature review on the automation of SLR studies, aiming to collect and synthesize the current research in this area for further exploration. The review analyzes 41 primary studies and identifies the objectives, application domains, automated steps, techniques, challenges, and solution directions in SLR automation. The study reveals that while automation approaches for SLR have focused on the selection of primary studies, there is a lack of automation techniques applied in the planning and reporting phases, indicating a need for further research in automating other activities of the SLR process.
INFORMATION AND SOFTWARE TECHNOLOGY
(2021)
Review
Computer Science, Artificial Intelligence
David M. Goldberg, Alan S. Abrahams
Summary: In recent years, online reviews have become an important way for consumers to express their opinions and feedback. However, the unstructured and voluminous nature of textual data makes it challenging for companies to effectively utilize this feedback. This study proposes a method for prioritizing online reviews by using text mining tools, focusing on identifying the most useful reviews pertaining to innovation opportunities for firms. The results demonstrate the effectiveness of the proposed technique in improving upon existing methods, and senior managers at a large manufacturing firm also validate the usefulness of the selected attribute types in online reviews.
DECISION SUPPORT SYSTEMS
(2022)
Article
Computer Science, Information Systems
Jurgen Schedlbauer, Georgios Raptis, Bernd Ludwig
Summary: This study used web crawling and text mining techniques to analyze German job advertisements, finding that soft skills and professional expertise are equally important. The results highlight the importance of practical experience and can guide the development of medical informatics curricula.
INTERNATIONAL JOURNAL OF MEDICAL INFORMATICS
(2021)
Article
Computer Science, Artificial Intelligence
Bichitrananda Behera, G. Kumaravelan
Summary: The fuzzy rough set (FRS) and FRS-RNN based on robust nearest neighbor perform well in handling real-valued datasets, but have not been studied for text document classification. A modified CNN structure is proposed for text document classification and feature extraction. Experimental results show that the proposed FRS-RNN model outperforms traditional classification models.
Article
Computer Science, Information Systems
Qinghui Zhang, Qihao Yuan, Pengtao Lv, Mengya Zhang, Lei Lv
Summary: This paper proposes a Capsule network model for electronic medical record classification in Chinese. The model combines LSTM and GRU models and utilizes a unique routing structure to extract complex Chinese medical text features.
Article
Computer Science, Artificial Intelligence
Sahar Behpour, Mohammadmahdi Mohammadi, Mark V. Albert, Zinat S. Alam, Lingling Wang, Ting Xiao
Summary: The study demonstrates the importance of emphasizing time in trend detection by introducing a weighted temporal feature. By analyzing finance journal abstracts, trending finance topics that are not identifiable with standard clustering methods are discovered. The use of silhouette score divided by standard deviation to identify and validate trending topics showcases the effectiveness of the approach.
KNOWLEDGE-BASED SYSTEMS
(2021)
Article
Health Care Sciences & Services
William R. McIvor, Arna Banerjee, John R. Boulet, Tanja Bekhuis, Eugene Tseytlin, Laurence Torsher, Samuel DeMaria, John P. Rask, Matthew S. Shotwell, Amanda Burden, Jeffrey B. Cooper, David M. Gaba, Adam Levine, Christine Park, Elizabeth Sinz, Randolph H. Steadman, Matthew B. Weinger
SIMULATION IN HEALTHCARE-JOURNAL OF THE SOCIETY FOR SIMULATION IN HEALTHCARE
(2017)
Article
Computer Science, Interdisciplinary Applications
Sergio M. Castro, Eugene Tseytlin, Olga Medvedeva, Kevin Mitchell, Shyam Visweswaran, Tanja Bekhuis, Rebecca S. Jacobson
JOURNAL OF BIOMEDICAL INFORMATICS
(2017)
Article
Health Care Sciences & Services
Tanja Bekhuis, Marcos Kreinacke, Heiko Spallek, Mei Song, Jean A. O'Donnell
JOURNAL OF MEDICAL INTERNET RESEARCH
(2011)
Review
Multidisciplinary Sciences
Tanja Bekhuis, Eugene Tseytlin, Kevin J. Mitchell, Dina Demner-Fushman
Article
Dentistry, Oral Surgery & Medicine
Mei Song, Jean A. O'Donnell, Tanja Bekhuis, Heiko Spallek
Article
Dentistry, Oral Surgery & Medicine
Cheryl L. Straub-Morarend, Christine R. Wankiiri-Hale, Derek R. Blanchette, Sharon K. Lanning, Tanja Bekhuis, Becky M. Smith, Abby J. Brodie, Deise Cruz Oliveira, Robert A. Handysides, Deborah V. Dawson, Heiko Spallek
JOURNAL OF DENTAL EDUCATION
(2016)
Proceedings Paper
Computer Science, Interdisciplinary Applications
Tanja Bekhuis, Eugene Tseytlin, Kevin J. Mitchell
PROCEEDINGS 2015 IEEE INTERNATIONAL CONFERENCE ON BIOINFORMATICS AND BIOMEDICINE
(2015)
Article
Information Science & Library Science
John J. Frazier, Corey D. Stein, Eugene Tseytlin, Tanja Bekhuis
JOURNAL OF THE MEDICAL LIBRARY ASSOCIATION
(2015)
Article
Information Science & Library Science
Tanja Bekhuis, Dina Demner-Fushman, Rebecca Crowley
JOURNAL OF THE MEDICAL LIBRARY ASSOCIATION
(2013)
Article
Nutrition & Dietetics
PM Kris-Etherton, DS Taylor, H Smiciklas-Wright, DC Mitchell, TC Bekhuis, BH Olson, AB Slonim
JOURNAL OF THE AMERICAN DIETETIC ASSOCIATION
(2002)
Article
Computer Science, Artificial Intelligence
Qianghua Liu, Yu Tian, Tianshu Zhou, Kewei Lyu, Ran Xin, Yong Shang, Ying Liu, Jingjing Ren, Jingsong Li
Summary: This study proposes a few-shot disease diagnosis decision making model based on a model-agnostic meta-learning algorithm (FSDD-MAML). It significantly improves the diagnostic process in primary health care and helps general practitioners diagnose few-shot diseases more accurately.
ARTIFICIAL INTELLIGENCE IN MEDICINE
(2024)
Article
Computer Science, Artificial Intelligence
Balazs Borsos, Corinne G. Allaart, Aart van Halteren
Summary: The study demonstrates the feasibility of predicting functional outcomes for ischemic stroke patients and the usability of multimodal deep learning architectures for this purpose.
ARTIFICIAL INTELLIGENCE IN MEDICINE
(2024)
Article
Computer Science, Artificial Intelligence
Abdelmoniem Helmy, Radwa Nassar, Nagy Ramdan
Summary: This study utilizes machine learning models to detect depression symptoms in Arabic and English texts, and provides manually and automatically annotated tweet corpora. The study also develops an application that can detect tweets with depression symptoms and predict depression trends.
ARTIFICIAL INTELLIGENCE IN MEDICINE
(2024)