Article
Health Care Sciences & Services
Hang Dong, Matus Falis, William Whiteley, Beatrice Alex, Joshua Matterson, Shaoxiong Ji, Jiaoyan Chen, Honghan Wu
Summary: Clinical coding is the process of transforming medical information into structured codes for statistical analysis. Automated clinical coding is a promising task for AI, but there are challenges in achieving explainability and consistency. Involvement of coders is necessary in the development process.
NPJ DIGITAL MEDICINE
(2022)
Review
Computer Science, Information Systems
Brian Ondov, Kush Attal, Dina Demner-Fushman
Summary: Plain language in medicine has long been advocated to improve patient understanding and engagement. As Natural Language Processing advances, methods for automatic simplification of biomedical text have become increasingly sophisticated, with procedural and neural methods being the main approaches.
JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION
(2022)
Article
Computer Science, Artificial Intelligence
Selen Yuecesoy Kahraman, Tuerkay Dereli, Alptekin Durmusoglu
Summary: The continuous development of technology leads to an increase in new inventions and patents. Each new technology is assigned to an existing patent class, or a new class may be created. This supports the development of automated patent classification methods. This paper evaluates the studies and patents published in this field using bibliometric analysis and focuses on important references, authors, journals, countries, and assignees. It also examines recent developments and presents evaluations on future research topics.
INTERNATIONAL JOURNAL OF INFORMATION TECHNOLOGY & DECISION MAKING
(2023)
Article
Chemistry, Multidisciplinary
Dominik Wunderlich, Daniel Bernau, Francesco Alda, Javier Parra-Arnau, Thorsten Strufe
Summary: This study investigates the privacy-utility trade-off in hierarchical text classification with differential privacy guarantees and identifies neural network architectures that offer superior trade-offs. The results show that using large differential privacy parameters can completely mitigate membership inference attacks and only have a moderate impact on model utility.
APPLIED SCIENCES-BASEL
(2022)
Article
Computer Science, Information Systems
Ahmed S. Almasoud, Siwar Ben Haj Hassine, Fahd N. Al-Wesabi, Mohamed K. Nour, Anwer Mustafa Hilal, Mesfer Al Duhayyim, Manar Ahmed Hamza, Abdelwahed Motwakel
Summary: Due to advancements in the Internet and information technologies, there has been a significant increase in electronic data in the biomedical sector. This paper proposes a deep learning model, DL-ALSTM, for multi-document biomedical text summarization. The model preprocesses the data and uses chaotic glowworm swarm optimization algorithm to improve its summarization performance. Experimental analysis on the PubMed dataset demonstrates the effectiveness of the proposed model.
CMC-COMPUTERS MATERIALS & CONTINUA
(2022)
Article
Chemistry, Multidisciplinary
Carlos Adriano Oliveira Goncalves, Rui Camacho, Celia Talma Goncalves, Adrian Seara Vieira, Lourdes Borrajo Diz, Eva Lorenzo Iglesias
Summary: The study aims to test how assigning different weights to different sections of documents affects text classification efficiency. The results show that using text from specific sections achieves better results.
APPLIED SCIENCES-BASEL
(2021)
Article
Psychology, Multidisciplinary
Mohammad Abuhassan, Tarique Anwar, Matthew Fuller-Tyszkiewicz, Hannah K. Jarman, Adrian Shatte, Chengfei Liu, Suku Sukunesan
Summary: Recent evidence suggests that individuals with eating disorders are seeking help via social media. Advanced approaches are needed to distinguish those in need of help from those only commenting on eating disorders. This research utilizes a deep learning model to differentiate users engaged in eating disorder-related communication on social media and facilitate effective communication with healthcare professionals.
COMPUTERS IN HUMAN BEHAVIOR
(2023)
Article
Chemistry, Multidisciplinary
Omer Koksal, Bedir Tekinerdogan
Summary: This paper presents an automated bug classification approach applied in an industrial case study. The approach utilizes machine learning, text mining, and natural language processing techniques to support the classification of software bugs. The results show that bug classification can be automated and even outperform manual classification.
APPLIED SCIENCES-BASEL
(2022)
Article
Computer Science, Artificial Intelligence
Linda Zhou, Andrew Caines, Ildiko Pete, Alice Hutchings
Summary: This paper discusses the presence of hate speech in underground hacking and extremist forums, and proposes an approach to extract hateful spans from longer texts. The research results show that using multiple data sources for training a classifier does not always improve performance.
NATURAL LANGUAGE ENGINEERING
(2023)
Article
Ecology
Surangi W. Punyasena, Derek S. Haselhorst, Shu Kong, Charless C. Fowlkes, J. Enrique Moreno
Summary: Pollen analysis is a complex task that plays a crucial role in identifying plant-pollinator relationships and tracking plant flowering phenomena. The authors proposed an automated workflow for pollen analysis, involving scanning pollen sample slides and using convolutional neural networks for pollen detection and identification.
METHODS IN ECOLOGY AND EVOLUTION
(2022)
Article
Computer Science, Interdisciplinary Applications
Halil Kilicoglu, Graciela Rosemblat, Linh Hoang, Sahil Wadhwa, Zeshan Peng, Mario Malicki, Jodi Schneider, Gerben ter Riet
Summary: The objective of the study was to annotate RCT publications with CONSORT checklist items and develop text mining methods. Different annotation methods were used to annotate and analyze a corpus containing 10,709 sentences, demonstrating the performance of various methods in recognizing methodology-related items.
JOURNAL OF BIOMEDICAL INFORMATICS
(2021)
Article
Computer Science, Artificial Intelligence
Orestis Lampridis, Laura State, Riccardo Guidotti, Salvatore Ruggieri
Summary: In this paper, we present xspells, a model-agnostic approach for explaining black box model decisions in short text classification. The explanations consist of a set of exemplar and counter-exemplar sentences. Experimental results show that xspells outperforms the lime method in terms of explanation quality, diversity, and usefulness, while being comparable in stability.
Article
Computer Science, Software Engineering
Beytullah Yildiz, Murat Tezgider
Summary: Deep learning has had a great impact in various fields, with recent advances showing significant improvements in text analysis and classification. Optimizing hyperparameters for word embeddings can lead to increased classification success rates.
CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE
(2021)
Article
Computer Science, Interdisciplinary Applications
Muhammad Ali Ibrahim, Muhammad Usman Ghani Khan, Faiza Mehmood, Muhammad Nabeel Asim, Waqar Mahmood
Summary: The exponential growth of biomedical literature and clinical data requires robust computational methodologies for extracting insights and accurate disease coding. Current computational text classification methods for biomedical data struggle with performance issues when dealing with different types of texts.
JOURNAL OF BIOMEDICAL INFORMATICS
(2021)
Article
Biochemical Research Methods
Kazi Zainab, Gautam Sriyastava, Vijay Mago
Summary: This study presents a novel method of detecting the occupations of Twitter users engaged in the medical domain by combining word embedding with state-of-art neural networks. The results demonstrate that our approach outperforms traditional machine learning techniques in detecting medical occupations among users.
BMC BIOINFORMATICS
(2022)
Article
Environmental Sciences
Christine L. Gray, Danelle T. Lobdell, Kristen M. Rappazzo, Yun Jian, Jyotsna S. Jagai, Lynne C. Messer, Achal P. Patel, Stephanie A. DeFlorio-Barker, Christopher Lyttle, Julian Solway, Andrey Rzhetsky
ENVIRONMENTAL RESEARCH
(2018)
Article
Pediatrics
Ryan L. Mork, Patrick G. Hogan, Carol E. Muenks, Mary G. Boyle, Ryley M. Thompson, John J. Morelli, Melanie L. Sullivan, Sarah J. Gehlert, David G. Ross, Alicia Yn, Juliane Bubeck Wardenburg, Andrey Rzhetsky, Carey-Ann D. Burnham, Stephanie A. Fritz
PEDIATRIC RESEARCH
(2018)
Article
Oncology
Isabel Romero-Calvo, Christopher R. Weber, Mohana Ray, Miguel Brown, Kori Kirby, Rajib K. Nandi, Tiha M. Long, Samantha M. Sparrow, Andrey Ugolkov, Wenan Qiang, Yilin Zhang, Tonya Brunetti, Hedy Kindler, Jeremy P. Segal, Andrey Rzhetsky, Andrew P. Mazar, Mary M. Buschmann, Ralph Weichselbaum, Kevin Roggin, Kevin P. White
MOLECULAR CANCER RESEARCH
(2019)
Article
Infectious Diseases
Patrick G. Hogan, Ryan L. Mork, Mary G. Boyle, Carol E. Muenks, John J. Morelli, Ryley M. Thompson, Melanie L. Sullivan, Sarah J. Gehlert, Jessica R. Merlo, Matt G. McKenzie, Juliane Bubeck Wardenburg, Andrey Rzhetsky, Carey-Ann D. Burnham, Stephanie A. Fritz
JOURNAL OF INFECTION
(2019)
Article
Genetics & Heredity
Gokhan Unlu, Eric R. Gamazon, Xinzi Qi, Daniel S. Levic, Lisa Bastarache, Joshua C. Denny, Dan M. Roden, Ilya Mayzus, Max Breyer, Xue Zhong, Anuar Konkashbaev, Andrey Rzhetsky, Ela W. Knapik, Nancy J. Cox
AMERICAN JOURNAL OF HUMAN GENETICS
(2019)
Article
Multidisciplinary Sciences
Ce Zhang, Hsiung-Lin Tu, Gengjie Jia, Tanzila Mukhtar, Verdon Taylor, Andrey Rzhetsky, Savas Tay
Article
Biology
Valentin Danchev, Andrey Rzhetsky, James A. Evans
Article
Biochemistry & Molecular Biology
Atif Khan, Oleguer Plana-Ripoll, Sussie Antonsen, Jorgen Brandt, Camilla Geels, Hannah Landecker, Patrick F. Sullivan, Carsten Bocker Pedersen, Andrey Rzhetsky
Article
Biochemical Research Methods
Hanxin Zhang, Torsten Dahlen, Atif Khan, Gustaf Edgren, Andrey Rzhetsky
PLOS COMPUTATIONAL BIOLOGY
(2020)
Article
Multidisciplinary Sciences
Brooke Schuster, Michael Junkin, Sara Saheb Kashaf, Isabel Romero-Calvo, Kori Kirby, Jonathan Matthews, Christopher R. Weber, Andrey Rzhetsky, Kevin P. White, Savas Tay
NATURE COMMUNICATIONS
(2020)
Article
Biochemistry & Molecular Biology
Hanxin Zhang, Atif Khan, Qi Chen, Henrik Larsson, Andrey Rzhetsky
Summary: This study examines the annual patterns of psychiatric disorders in the U.S. and Sweden using large datasets. The findings show remarkable similarities in annual patterns across studied diseases, with greater variation in Sweden for psychiatric disorders. Results varied for different age groups in terms of healthcare-seeking visit patterns. The study suggests that uncorrected results may capture real trends, while corrected results may reflect artifacts influenced by fluctuating health-seeking visits.
Article
Biochemical Research Methods
Yanan H. Long, Qi Chen, Henrik H. Larsson, Andrey Rzhetsky
Summary: The study investigates the association between the human sex ratio at birth and environmental factors using large datasets from the US and Sweden. While seasonal and temperature variations were not found to affect the sex ratio, various pollutants, including industrial and agricultural activities, were associated with lower sex ratios. Additionally, some environmental toxins were found to induce higher sex ratios.
PLOS COMPUTATIONAL BIOLOGY
(2021)
Article
Psychiatry
Hanxin Zhang, Atif Khan, Steven A. Kushner, Andrey Rzhetsky
Summary: Schizophrenia is a leading cause of disability worldwide, and its etiology involves genetic and environmental factors. This study utilized unique data sources and mathematical models to estimate the contributions of genetic and environmental factors to schizophrenia risk, finding that environmental factors are an important source of explanatory variance.
Article
Geriatrics & Gerontology
Chengjian Shi, Niser Babiker, Jacek K. Urbanek, Robert L. Grossman, Megan Huisingh-Scheetz, Andrey Rzhetsky
Summary: This study used accelerometer data to predict cognitive decline in older adults within 1 or 5 years, with high accuracy. The proposed models can be applied to clinical practices serving aging populations.
Meeting Abstract
Chemistry, Multidisciplinary
Olga Tarasova, Ivan Mayorov, Dmitry Filimonov, Vladimir Poroikov, Ilya Mayzus, Andrey Rzhetsky
ABSTRACTS OF PAPERS OF THE AMERICAN CHEMICAL SOCIETY
(2018)