Article
Computer Science, Interdisciplinary Applications
Giridhar Kaushik Ramachandran, Kevin Lybarger, Yaya Liu, Diwakar Mahajan, Jennifer J. Liang, Ching-Huei Tsou, Meliha Yetisgen, Ozlem Uzuner
Summary: An accurate and detailed record of patient medications, including changes, is crucial for healthcare providers to provide appropriate care. This study focuses on the automatic extraction of medication change information from clinical notes. The proposed systems, based on BERT models, successfully improve the classification performance of medication changes compared to previous work.
JOURNAL OF BIOMEDICAL INFORMATICS
(2023)
Article
Computer Science, Information Systems
Stuart J. Nelson, Allen Flynn, Mark S. Tuttle
Summary: The study aimed to develop an ontology for formalizing drug indications in a computable and comparable manner. A model was created to represent FDA-approved indications as disjuncts of conjuncts of assertions, with logical primitives chosen from 2 categories. The model successfully represented over 99% of approved treatment or prevention label indications, with challenges remaining in workflow design and terminology integration.
JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION
(2021)
Article
Computer Science, Artificial Intelligence
Han Su, Qi Wu, Xiaoan Tang, Ting Huang
Summary: This study proposes a method for linguistic information granulation in GDM scenarios. It first introduces an information granulation model to achieve the operational realization of linguistic information related to decision makers' relative importance. Then, it develops another information granulation model based on a new performance index, which combines consistency and consensus, to operationalize linguistic information associated with decision makers' preference over alternatives. The study also introduces the PSO approach to solve the two models and presents the framework of the proposed linguistic information granulation approach to address GDM with DLPRs. A case study demonstrates the application of the proposed method in practical decision scenarios, and comparisons with two linguistic quantization models show its advantages in an aeroengine risk assessment problem.
ARTIFICIAL INTELLIGENCE REVIEW
(2023)
Article
Computer Science, Information Systems
Thien Ho Huong, Kiet Tran-Trung, Vinh Truong Hoang
Summary: This study investigates a machine learning approach for gender determination based on Vietnamese names. A model based on N-gram for the full name, combined with the middle name feature based on the specificity of Vietnamese language, is proposed. The experimental evaluation on the GenderVN1.0 dataset (with 3 million Vietnamese names) achieves an accuracy of 90.9% in gender prediction tasks.
MOBILE INFORMATION SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Raghunathan Krishankumar, Arunodaya Raj Mishra, K. S. Ravichandran, Samarjit Kar, Amir H. Gandomi, Romualdas Bausys
Summary: Online reviews from the web are valuable data sources for tourism analytics. Restaurants play a crucial role in the growth of tourism in India. However, existing decision frameworks for restaurant selection lack effective handling of uncertainty and consideration of heterogeneous sources.
APPLIED SOFT COMPUTING
(2023)
Article
Computer Science, Interdisciplinary Applications
Sankaran Narayanan, Kaivalya Mannam, Pradeep Achan, Maneesha Ramesh, P. Venkat Rangan, Sreeranga P. Rajan
Summary: This study investigates the integration of contextualized language models and multi-task learning to enhance accurate medication extraction, proposing a novel multi-task adaptation method. Results demonstrate that combining multi-dataset BERT adaptation and multi-task learning outperforms prior medication extraction methods without needing additional features or data.
JOURNAL OF BIOMEDICAL INFORMATICS
(2022)
Article
Computer Science, Artificial Intelligence
Tanya Malhotra, Anjana Gupta
Summary: This article proposes a method to deal with unbalanced linguistic terms by using a specific algorithm and a 2-tuple model, aiming to assist experts in addressing difficulties in problem evaluation.
IEEE TRANSACTIONS ON FUZZY SYSTEMS
(2021)
Article
Computer Science, Artificial Intelligence
Qiang Zhang, Ting Huang, Xiaoan Tang, Kaijie Xu, Witold Pedrycz
Summary: This study introduces a model for linguistic information granulation and develops a penalty function-based co-evolutionary particle swarm optimization method to address the operational realization of linguistic information in group decision-making. A case study on car brands is conducted to demonstrate the applicability of the proposed model and the PFCPSO approach, with comparative studies showing their effectiveness.
INFORMATION FUSION
(2022)
Article
Multidisciplinary Sciences
Zhiwei Gong, Jian Lin, Ling Weng
Summary: This paper proposes a new approach to multi-attribute group decision making (MAGDM) using multiplicative linguistic information. The authors define a chi-square measure to quantify the difference between sets of multiplicative linguistic terms and introduce two operators based on this measure. They develop a novel approach to MAGDM with multiplicative linguistic term sets and validate its effectiveness and practicality through an evaluation of transport logistics enterprises.
Article
Computer Science, Interdisciplinary Applications
Qiwei Gan, Mengke Hu, Kelly S. Peterson, Hannah Eyre, Patrick R. Alba, Annie E. Bowles, Johnathan C. Stanley, Scott L. DuVall, Jianlin Shi
Summary: This article summarizes our approach to extracting medication and corresponding attributes from clinical notes, which is the focus of track 1 of the 2022 National Natural Language Processing (NLP) Clinical Challenges(n2c2) shared task. Our system consists of three components: medication named entity recognition (NER), event classification (EC), and context classification (CC). Our best performance systems achieved micro-average F1 scores of 0.973, 0.911, and 0.909 for the NER, EC, and CC, respectively.
JOURNAL OF BIOMEDICAL INFORMATICS
(2023)
Article
Computer Science, Information Systems
K. Krishnakumar, K. Vasandkumar
Summary: Principal component analysis (PCA) is a statistical tool used to reduce dimensionality by removing redundant information in a database. However, it has limitations when applied to a fusion of inter-similar but not intra-similar groups of databases, such as poor discriminatory power and large computational load. This work presents a new approach that improves the discriminatory power of traditional PCA by combining database information and Principal Components.
MULTIMEDIA TOOLS AND APPLICATIONS
(2023)
Article
Public, Environmental & Occupational Health
Jie Li, Yueying Chen, Xiaoquan Zhao, Xiaobing Yang, Fan Wang
Summary: This study examined Chinese citizens' ability to correctly identify COVID-19 vaccine misinformation in geographic areas with and without a regional outbreak. The results showed slightly higher levels of correct misinformation identification in surge areas compared to non-surge areas. Trust in official information sources was positively associated with correct misinformation identification, while trust in informal sources was negatively associated with the same outcome. Perceived information quality was positively associated with correct misinformation identification. Therefore, information providers in China should enhance the quality of vaccine information, and the Chinese public should balance their usage of different information sources.
FRONTIERS IN PUBLIC HEALTH
(2023)
Article
Computer Science, Information Systems
Sophie-Camille Hogue, Flora Chen, Genevieve Brassard, Denis Lebel, Jean-Francois Bussieres, Audrey Durand, Maxime Thibault
Summary: The study assessed the clinical performance of a machine learning model for identifying unusual medication orders. Results showed that the model performed better in identifying atypical pharmacological profiles compared to medication orders. Pharmacists found the model to be a useful screening tool and improvements in predictions should be prioritized for future research.
JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION
(2021)
Review
Biochemistry & Molecular Biology
Yeon-Kyoung Cho, Dae-Hun Park, In-Chul Jeon
Summary: Age-related macular degeneration (AMD) is central vision loss associated with aging and has various risk factors. Treatment options include device-based therapy, anti-inflammatory drugs, anti-VEGF drugs, and natural products. Natural products used as AMD drugs, mainly administered orally, can be combined with other treatments for potential therapeutic benefits.
INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES
(2021)
Article
Computer Science, Information Systems
Adam Wright, Scott Nelson, David Rubins, Richard Schreiber, Dean F. Sittig
Summary: This study aims to identify common causes of clinical decision support malfunctions related to medication routes and to provide best practices for avoiding these malfunctions. The results revealed that value set errors related to medication routes are widespread and can impact the accuracy of CDS interventions.
JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION
(2022)
Article
Computer Science, Interdisciplinary Applications
Leonardo Campillos, Louise Deleger, Cyril Grouin, Thierry Hamon, Anne-Laure Ligozat, Aurelie Neveol
LANGUAGE RESOURCES AND EVALUATION
(2018)
Article
Computer Science, Information Systems
Cyril Grouin, Natalia Grabar, Thierry Hamon, Sophie Rosset, Xavier Tannier, Pierre Zweigenbaum
JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION
(2013)
Article
Public, Environmental & Occupational Health
Thierry Hamon, Remi Gagnayre
PATIENT EDUCATION AND COUNSELING
(2013)
Article
Mathematical & Computational Biology
Marie Dupuch, Laetitia Dupuch, Thierry Hamon, Natalia Grabar
JOURNAL OF BIOMEDICAL SEMANTICS
(2014)
Proceedings Paper
Computer Science, Interdisciplinary Applications
Hanna Pylieva, Artem Chernodub, Natalia Grabar, Thierry Hamon
MEDINFO 2019: HEALTH AND WELLBEING E-NETWORKS FOR ALL
(2019)
Proceedings Paper
Computer Science, Artificial Intelligence
Tsanta Randriatsitohaina, Thierry Hamon
ARTIFICIAL INTELLIGENCE IN MEDICINE, AIME 2019
(2019)
Article
Computer Science, Artificial Intelligence
Thierry Hamon, Natalia Grabar, Fleur Mougin
Article
Linguistics
Natalia Grabar, Thierry Hamon
TRAITEMENT AUTOMATIQUE DES LANGUES
(2016)
Article
Linguistics
Amandine Perinet, Thierry Hamon
TRAITEMENT AUTOMATIQUE DES LANGUES
(2015)
Editorial Material
Linguistics
Patrick Drouin, Natalia Grabar, Thierry Hamon, Kyo Kageura
Proceedings Paper
Computer Science, Information Systems
Vincent Claveau, Thierry Hamon, Sebastien Le Maguer, Natalia Grabar
DIGITAL HEALTHCARE EMPOWERING EUROPEANS
(2015)
Proceedings Paper
Computer Science, Interdisciplinary Applications
Thierry Hamon, Fleur Mougin, Natalia Grabar
MEDINFO 2015: EHEALTH-ENABLED HEALTH
(2015)
Proceedings Paper
Computer Science, Artificial Intelligence
Amandine Perinet, Thierry Hamon
ADVANCES IN NATURAL LANGUAGE PROCESSING
(2014)
Proceedings Paper
Computer Science, Artificial Intelligence
Thierry Hamon, Christopher Engstrom, Sergei Silvestrov
ADVANCES IN NATURAL LANGUAGE PROCESSING
(2014)
Proceedings Paper
Health Care Sciences & Services
Thierry Hamon, Natalia Grabar, Dimitrios Kokkinakis
MEDINFO 2013: PROCEEDINGS OF THE 14TH WORLD CONGRESS ON MEDICAL AND HEALTH INFORMATICS, PTS 1 AND 2
(2013)