Article
Computer Science, Information Systems
Dmytro S. Lituiev, Benjamin Lacar, Sang Pak, Peter L. Abramowitsch, Emilia H. De Marchis, Thomas A. Peterson
Summary: This study applied natural language processing and inference methods to extract social determinants of health (SDoH) information from clinical notes of patients with chronic low back pain (cLBP), aiming to enhance future analyses of the associations between SDoH disparities and cLBP outcomes. The study developed and validated named entity recognition (NER) systems based on rule-based and machine learning approaches, and validated an entailment model. The annotated clinical notes corpus created in this study serves as a benchmark for training machine learning models and predictive models for NER and knowledge extraction from clinical texts related to SDoH.
JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION
(2023)
Article
Chemistry, Multidisciplinary
Leslie Marjorie Gallegos Salazar, Octavio Loyola-Gonzalez, Miguel Angel Medina-Perez
Summary: Mental disorders are a global issue, and computer science offers solutions for detecting depression based on social media analysis. This paper introduces a contrast pattern-based classifier that utilizes emotion and sentiment analysis from social media to achieve better classification results.
APPLIED SCIENCES-BASEL
(2021)
Article
Chemistry, Physical
Yue Liu, Xianyuan Ge, Zhengwei Yang, Shiyu Sun, Dahui Liu, Maxim Avdeev, Siqi Shi
Summary: Extracting descriptors automatically from materials science literature is still challenging. This study develops an automatic descriptors recognizer based on natural language processing (NLP) and demonstrates its potential utility in materials design.
JOURNAL OF POWER SOURCES
(2022)
Review
Computer Science, Information Systems
Ivan Otero-Gonzalez, Moises R. Pacheco-Lorenzo, Manuel J. Fernandez-Iglesias, Luis E. Anido-Rifon
Summary: This study explores the applications of conversational agents in detecting mental health disorders, specifically depression screening. The findings indicate that conversational agents are effective in detecting depression, and voice interaction is the future direction of development.
INTERNATIONAL JOURNAL OF MEDICAL INFORMATICS
(2024)
Article
Radiology, Nuclear Medicine & Medical Imaging
Jacob J. Visser, Marianne de Vries, Jan A. Kors
Summary: This study developed and validated classifiers for automatic detection of actionable findings and documentation of nonroutine communication in radiology reports. The classifiers were trained and evaluated using annotated training and test sets. The results showed that automatic detection of actionable findings and subsequent communication in radiology reports is feasible.
EUROPEAN RADIOLOGY
(2022)
Article
Computer Science, Theory & Methods
Hui Guan, Xipeng Shen, Hamid Krim
Summary: Egeria is the first automatic synthesizer of advising tools for High-Performance Computing (HPC), which constructs a text retrieval tool based on HPC programming guides to improve program performance and provide optimization knowledge. It utilizes natural language processing techniques and HPC-specific knowledge in its multi-layered design.
IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS
(2021)
Article
Medical Informatics
Yucong Lin, Jia Li, Huan Xiao, Lujie Zheng, Ying Xiao, Hong Song, Jingfan Fan, Deqiang Xiao, Danni Ai, Tianyu Fu, Feifei Wang, Han Lv, Jian Yang
Summary: This article introduces an automatic literature screening method based on artificial intelligence technology - the PAJO model. The PAJO model utilizes the pre-trained BERT model to analyze text and journal features, treating article screening as a classification problem. Experimental results demonstrate that the PAJO model outperforms existing baseline models in screening high-quality articles and significantly improves the efficiency of clinical practice guideline development.
BMC MEDICAL INFORMATICS AND DECISION MAKING
(2023)
Article
Computer Science, Information Systems
Stefano Ferilli
Summary: This paper focuses on stopword identification, proposing a novel method based on term and document frequency, and an automatic cutoff strategy for selecting stopwords in small corpora. These methods are generic, fully automatic, and do not require prior linguistic knowledge.
Article
Chemistry, Multidisciplinary
Ahmad Musyafa, Ying Gao, Aiman Solyman, Chaojie Wu, Siraj Khan
Summary: This paper proposes an automatic model for Indonesian grammar correction based on the Transformer architecture, addressing the lack of research on the GEC task for low-resource languages (especially Indonesian). It also builds a large corpus of the Indonesian language for evaluating future Indonesian GEC tasks. Experimental results demonstrate significant and satisfactory performance of the Transformer-based automatic error correction model.
APPLIED SCIENCES-BASEL
(2022)
Article
Computer Science, Information Systems
Ayato Kuwana, Atsushi Oba, Ranto Sawai, Incheon Paik
Summary: Automatic ontology generation has gained attention, and we proposed a new method based on neural networks, utilizing two-stage learning which improved accuracy by over 12.5%. Applied to a real dataset, some exceptions were shown, indicating future research directions to enhance quality.
Article
Psychology, Multidisciplinary
Lee A. Spitzley, Xinran Wang, Xunyu Chen, Judee K. Burgoon, Norah E. Dunbar, Saiying Ge
Summary: This study explored the relationships between multiple dimensions of personality and multiple features of language style, finding greater heterogeneity in language style in interactive contexts and extraverts maintaining greater linguistic style consistency during interactions.
FRONTIERS IN PSYCHOLOGY
(2022)
Article
Computer Science, Artificial Intelligence
Carlos Gonzalez-Santos, Miguel A. Vega-Rodriguez, Carlos J. Perez, Joaquin M. Lopez-Munoz, Inaki Martinez-Sarriegui
Summary: This study presents a Word Embedding-based Moral Foundation Assignment (WEMFA) approach for multiple assignment of moral foundations in the movie domain. The proposed approach outperforms the existing approach by 41.7% in terms of moral strength. Additionally, an extended Moral Foundations Dictionary (MFD24) with 14 new moral foundations has been created, enriching the moral context.
KNOWLEDGE-BASED SYSTEMS
(2023)
Review
Computer Science, Information Systems
Tingting Yang, Fei Li, Donghong Ji, Xiaohui Liang, Tian Xie, Shuwan Tian, Bobo Li, Peitong Liang
Summary: Depression is a prevalent issue in modern society, and analyzing it through Chinese microblog reviews has led to the creation of a dataset with 6100 manually annotated posts for predicting depression degree and cause. A neural model was developed for joint depression degree and cause prediction, outperforming other neural models with promising results but still room for improvement in social-media-based analysis of depression.
INFORMATION PROCESSING & MANAGEMENT
(2021)
Article
Psychiatry
Braja Gopal Patra, Zhaoyi Sun, Zilin Cheng, Praneet Kasi Reddy Jagadeesh Kumar, Abdullah Altammami, Yiyang Liu, Rochelle Joly, Caroline Jedlicka, Diana Delgado, Jyotishman Pathak, Yifan Peng, Yiye Zhang
Summary: Objective evidence suggests that high-quality health education and effective communication within the framework of social support hold significant potential in preventing postpartum depression. Yet, developing trustworthy and engaging health education and communication materials requires extensive expertise and substantial resources.
FRONTIERS IN PSYCHIATRY
(2023)
Article
Public, Environmental & Occupational Health
Surbhi Bhatia, Mohammed Alojail, Sudhakar Sengan, Pankaj Dadheech
Summary: This article focuses on the extraction of information from medical images and texts, as well as the automatic categorization of these data using deep learning methods. It emphasizes the importance of semantic annotation and named entity recognition for effective use of clinical text data. The article also highlights the significance of multi-scale structures in extracting information from high-resolution medical images.
FRONTIERS IN PUBLIC HEALTH
(2022)
Article
Computer Science, Interdisciplinary Applications
Alireza Karimi, Reza Razaghi, Siddharth Daniel D'costa, Saeed Torbati, Sina Ebrahimi, Seyed Mohammadali Rahmati, Mary J. Kelley, Ted S. Acott, Haiyan Gong
Summary: This study investigated the biomechanical properties of the conventional aqueous outflow pathway using fluid-structure interaction. The results showed that the distribution of aqueous humor wall shear stress within this pathway is not uniform, which may contribute to our understanding of the underlying selective mechanisms.
COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE
(2024)
Article
Computer Science, Interdisciplinary Applications
Robert V. Bergen, Jean-Francois Rajotte, Fereshteh Yousefirizi, Arman Rahmim, Raymond T. Ng
Summary: This article introduces a 3D generative model called TrGAN, which can generate medical images with important features and statistical properties while protecting privacy. By evaluating through a membership inference attack, the fidelity, utility, and privacy trade-offs of the model were studied.
COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE
(2024)
Article
Computer Science, Interdisciplinary Applications
Hoda Mashayekhi, Mostafa Nazari, Fatemeh Jafarinejad, Nader Meskin
Summary: In this study, a novel model-free adaptive control method based on deep reinforcement learning (DRL) is proposed for cancer chemotherapy drug dosing. The method models the state variables and control action in their original infinite spaces, providing a more realistic solution. Numerical analysis shows the superior performance of the proposed method compared to the state-of-the-art RL-based approach.
COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE
(2024)
Article
Computer Science, Interdisciplinary Applications
Hao Sun, Bao Li, Liyuan Zhang, Yanping Zhang, Jincheng Liu, Suqin Huang, Xiaolu Xi, Youjun Liu
Summary: In cases of moderate stenosis in the internal carotid artery, the A1 segment of the anterior cerebral artery or the posterior communicating artery within the Circle of Willis may show a hemodynamic environment with high OSI and low TAWSS, increasing the risk of atherosclerosis development and stenosis in the CoW.
COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE
(2024)
Article
Computer Science, Interdisciplinary Applications
Ilaria Toniolo, Paola Pirini, Silvana Perretta, Emanuele Luigi Carniel, Alice Berardo
Summary: This study compared the outcomes of endoscopic sleeve gastroplasty (ESG) and laparoscopic sleeve gastrectomy (LSG) in weight loss surgery using computational models of specific patients. The results showed significant differences between the two procedures in terms of stomach volume reduction and mechanical stimulation. A predictive model was proposed to support surgical planning and estimation of volume reduction after ESG.
COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE
(2024)
Article
Computer Science, Interdisciplinary Applications
Chun-You Chen, Ya-Lin Chen, Jeremiah Scholl, Hsuan-Chia Yang, Yu-Chuan (Jack) Li
Summary: This study evaluated the overall performance of a machine learning-based CDSS (MedGuard) in triggering clinically relevant alerts and intercepting inappropriate drug errors and LASA drug errors. The results showed that MedGuard has the ability to improve patients' safety by triggering clinically valid alerts.
COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE
(2024)
Article
Computer Science, Interdisciplinary Applications
Lingzhi Tang, Xueqi Wang, Jinzhu Yang, Yonghuai Wang, Mingjun Qu, HongHe Li
Summary: In this paper, a dynamical local feature fusion net for automatically recognizing aortic valve calcification (AVC) from echocardiographic images is proposed. The network segments high-echo areas and adjusts the selection of local features to better integrate global and local semantic representations. Experimental results demonstrate the effectiveness of the proposed approach.
COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE
(2024)
Article
Computer Science, Interdisciplinary Applications
You-Lei Fu, Wu Song, Wanni Xu, Jie Lin, Xuchao Nian
Summary: This study investigates the combination of surface electromyographic signals (sEMG) and deep learning-based CNN networks to study the interaction between humans and products and the impact on body comfort. It compares the advantages and disadvantages of different CNN networks and finds that DenseNet has unique advantages over other algorithms in terms of accuracy and ease of training, while mitigating issues of gradient disappearance and model degradation.
COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE
(2024)
Article
Computer Science, Interdisciplinary Applications
Moritz Rempe, Florian Mentzel, Kelsey L. Pomykala, Johannes Haubold, Felix Nensa, Kevin Kroeninger, Jan Egger, Jens Kleesiek
Summary: In this study, a deep learning-based skull stripping algorithm for MRI was proposed, which works directly in the complex valued k-space and preserves the phase information. The results showed that the algorithm achieved similar results to the ground truth, with higher accuracy in the slices above the eye region. This approach not only preserves valuable information for further diagnostics, but also enables immediate anonymization of patient data before being transformed into the image domain.
COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE
(2024)
Article
Computer Science, Interdisciplinary Applications
Ziyang Chen, Laura Cruciani, Elena Lievore, Matteo Fontana, Ottavio De Cobelli, Gennaro Musi, Giancarlo Ferrigno, Elena De Momi
Summary: In this paper, a deep learning-based approach is proposed to recover 3D information of intra-operative scenes, which can enhance the safety of robot-assisted surgery by implementing depth estimation using stereo images.
COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE
(2024)
Article
Computer Science, Interdisciplinary Applications
Ao Leng, Bolun Zeng, Yizhou Chen, Puxun Tu, Baoxin Tao, Xiaojun Chen
Summary: This study presents a novel training system for zygomatic implant surgery, which offers a more realistic simulation and training solution. By integrating visual, haptic, and auditory feedback, the system achieves global rigid-body collisions and soft tissue simulation, effectively improving surgeons' proficiency.
COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE
(2024)
Article
Computer Science, Interdisciplinary Applications
Yingjie Wang, Xueqing Yin
Summary: This study developed an integrated computational model combining coronary flow and myocardial perfusion models to achieve physiologically accurate simulations. The model has the potential for clinical application in diagnosing insufficient myocardial perfusion.
COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE
(2024)
Article
Computer Science, Interdisciplinary Applications
Nitzan Avidan, Moti Freiman
Summary: This study aims to enhance the generalization capabilities of DNN-based MRI reconstruction methods for undersampled k-space data. By introducing a mask-aware DNN architecture and training method, the under-sampled data and mask are encoded within the model structure, leading to improved performance. Rigorous testing on the widely accessible fastMRI dataset reveals that this approach demonstrates better generalization capabilities and robustness compared to traditional DNN methods.
COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE
(2024)
Article
Computer Science, Interdisciplinary Applications
Enhao Zhang, Saeed Miramini, Lihai Zhang
Summary: This study investigates the combined effects of osteoporosis and diabetes on fracture healing process by developing numerical models. The results show that osteoporotic fractures have higher instability and disruption in mesenchymal stem cells' proliferation and differentiation compared to non-osteoporotic fractures. Moreover, when osteoporosis coexists with diabetes, the healing process of fractures can be severely impaired.
COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE
(2024)
Article
Computer Science, Interdisciplinary Applications
Yunhao Bai, Wenqi Li, Jianpeng An, Lili Xia, Huazhen Chen, Gang Zhao, Zhongke Gao
Summary: This study proposes an effective MIL method for classifying WSI of esophageal cancer. The use of self-supervised learning for feature extractor pretraining enhances feature extraction from esophageal WSI, leading to more robust and accurate performance. The proposed framework outperforms existing methods, achieving an accuracy of 93.07% and AUC of 95.31% on a comprehensive dataset of esophageal slide images.
COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE
(2024)