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Automated detection of myocardial infarction based on an improved state refinement module for LSTM/GRU Artif. Intell. Med. (IF 7.5) Pub Date : 2024-04-05 Jibin Wang, Xingtian Guo
Myocardial infarction (MI) is a common cardiovascular disease caused by the blockages of coronary arteries. The visual inspection of electrocardiogram (ECG) is the main diagnosis pattern, while it is taxing and time-consuming. Motivated from state refinement module for long short term memory (SRM-LSTM), we proposed two improved state refinement frameworks based on LSTM and gated recurrent unit (GRU)
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Application of machine learning in affordable and accessible insulin management for type 1 and 2 diabetes: A comprehensive review Artif. Intell. Med. (IF 7.5) Pub Date : 2024-04-04 Maryam Eghbali-Zarch, Sara Masoud
Proper insulin management is vital for maintaining stable blood sugar levels and preventing complications associated with diabetes. However, the soaring costs of insulin present significant challenges to ensuring affordable management. This paper conducts a comprehensive review of current literature on the application of machine learning (ML) in insulin management for diabetes patients, particularly
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Harnessing machine learning for EEG signal analysis: Innovations in depth of anaesthesia assessment Artif. Intell. Med. (IF 7.5) Pub Date : 2024-04-04 Thomas Schmierer, Tianning Li, Yan Li
Anaesthesia, crucial to surgical practice, is undergoing renewed scrutiny due to the integration of artificial intelligence in its medical use. The precise control over the temporary loss of consciousness is vital to ensure safe, pain-free procedures. Traditional methods of depth of anaesthesia (DoA) assessment, reliant on physical characteristics, have proven inconsistent due to individual variations
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Deep learning supported echocardiogram analysis: A comprehensive review Artif. Intell. Med. (IF 7.5) Pub Date : 2024-04-04 Sanjeevi G., Uma Gopalakrishnan, Rahul Krishnan Parthinarupothi, Thushara Madathil
An echocardiogram is a sophisticated ultrasound imaging technique employed to diagnose heart conditions. The transthoracic echocardiogram, one of the most prevalent types, is instrumental in evaluating significant cardiac diseases. However, interpreting its results heavily relies on the clinician’s expertise. In this context, artificial intelligence has emerged as a vital tool for helping clinicians
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Learnable weight initialization for volumetric medical image segmentation Artif. Intell. Med. (IF 7.5) Pub Date : 2024-04-03 Shahina Kunhimon, Abdelrahman Shaker, Muzammal Naseer, Salman Khan, Fahad Shahbaz Khan
Hybrid volumetric medical image segmentation models, combining the advantages of local convolution and global attention, have recently received considerable attention. While mainly focusing on architectural modifications, most existing hybrid approaches still use conventional data-independent weight initialization schemes which restrict their performance due to ignoring the inherent volumetric nature
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Towards classification and comprehensive analysis of AI-based COVID-19 diagnostic techniques: A survey Artif. Intell. Med. (IF 7.5) Pub Date : 2024-04-01 Amna Kosar, Muhammad Asif, Maaz Bin Ahmad, Waseem Akram, Khalid Mahmood, Saru Kumari
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Challenges and strategies for wide-scale artificial intelligence (AI) deployment in healthcare practices: A perspective for healthcare organizations Artif. Intell. Med. (IF 7.5) Pub Date : 2024-03-30 Pouyan Esmaeilzadeh
Healthcare organizations have realized that Artificial intelligence (AI) can provide a competitive edge through personalized patient experiences, improved patient outcomes, early diagnosis, augmented clinician capabilities, enhanced operational efficiencies, or improved medical service accessibility. However, deploying AI-driven tools in the healthcare ecosystem could be challenging. This paper categorizes
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Ontology-based decision support systems for diabetes nutrition therapy: A systematic literature review Artif. Intell. Med. (IF 7.5) Pub Date : 2024-03-30 Daniele Spoladore, Martina Tosi, Erna Cecilia Lorenzini
Diabetes is a non-communicable disease that has reached epidemic proportions, affecting 537 million people globally. Artificial Intelligence can support patients or clinicians in diabetes nutrition therapy – the first medical therapy in most cases of Type 1 and Type 2 diabetes. In particular, ontology-based recommender and decision support systems can deliver a computable representation of experts'
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Two-step interpretable modeling of ICU-AIs Artif. Intell. Med. (IF 7.5) Pub Date : 2024-03-28 G. Lancia, M.R.J. Varkila, O.L. Cremer, C. Spitoni
We present a novel methodology for integrating longitudinal data with the dynamic prediction capabilities of survival models. The aim is two-fold: to improve the predictive power while maintaining the interpretability of the models. To go beyond the paradigm of artificial neural networks, we propose a parsimonious and robust semi-parametric approach (i.e., a landmarking competing risks model) that
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iAFPs-Mv-BiTCN: Predicting antifungal peptides using self-attention transformer embedding and transform evolutionary based multi-view features with bidirectional temporal convolutional networks Artif. Intell. Med. (IF 7.5) Pub Date : 2024-03-26 Shahid Akbar, Quan Zou, Ali Raza, Fawaz Khaled Alarfaj
Globally, fungal infections have become a major health concern in humans. Fungal diseases generally occur due to the invading fungus appearing on a specific portion of the body and becoming hard for the human immune system to resist. The recent emergence of COVID-19 has intensely increased different nosocomial fungal infections. The existing wet-laboratory-based medications are expensive, time-consuming
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Pathways to democratized healthcare: Envisioning human-centered AI-as-a-service for customized diagnosis and rehabilitation Artif. Intell. Med. (IF 7.5) Pub Date : 2024-03-26 Tommaso Turchi, Giuseppe Prencipe, Alessio Malizia, Silvia Filogna, Francesco Latrofa, Giuseppina Sgandurra
The ongoing digital revolution in the healthcare sector, emphasized by bodies like the US Food and Drug Administration (FDA), is paving the way for a shift towards person-centric healthcare models. These models consider individual needs, turning patients from passive recipients to active participants. A key factor in this shift is Artificial Intelligence (AI), which has the capacity to revolutionize
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Building large-scale registries from unstructured clinical notes using a low-resource natural language processing pipeline Artif. Intell. Med. (IF 7.5) Pub Date : 2024-03-22 Nazgol Tavabi, James Pruneski, Shahriar Golchin, Mallika Singh, Ryan Sanborn, Benton Heyworth, Assaf Landschaft, Amir Kimia, Ata Kiapour
Building clinical registries is an important step in clinical research and improvement of patient care quality. Natural Language Processing (NLP) methods have shown promising results in extracting valuable information from unstructured clinical notes. However, the structure and nature of clinical notes are very different from regular text that state-of-the-art NLP models are trained and tested on,
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Cracking the Chronic Pain code: A scoping review of Artificial Intelligence in Chronic Pain research Artif. Intell. Med. (IF 7.5) Pub Date : 2024-03-21 Md Asif Khan, Ryan G.L. Koh, Sajjad Rashidiani, Theodore Liu, Victoria Tucci, Dinesh Kumbhare, Thomas E. Doyle
The aim of this review is to identify gaps and provide a direction for future research in the utilization of Artificial Intelligence (AI) in chronic pain (CP) management. A comprehensive literature search was conducted using various databases, including Ovid MEDLINE, Web of Science Core Collection, IEEE Xplore, and ACM Digital Library. The search was limited to studies on AI in CP research, focusing
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Medical knowledge graph completion via fusion of entity description and type information Artif. Intell. Med. (IF 7.5) Pub Date : 2024-03-21 Xiaochen Wang, Runtong Zhang, Butian Zhao, Yuhan Yao, Hongmei Zhao, Xiaomin Zhu
Medical Knowledge Graphs (MKGs) are vital in propelling big data technologies in healthcare and facilitating the realization of medical intelligence. However, large-scale MKGs often exhibit characteristics of data sparsity and missing facts. Following the latest advances, knowledge embedding addresses these problems by performing knowledge graph completion. Most knowledge embedding algorithms rely
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Hierarchical medical image report adversarial generation with hybrid discriminator Artif. Intell. Med. (IF 7.5) Pub Date : 2024-03-21 Junsan Zhang, Ming Cheng, Qiaoqiao Cheng, Xiuxuan Shen, Yao Wan, Jie Zhu, Mengxuan Liu
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De-identification of clinical free text using natural language processing: A systematic review of current approaches Artif. Intell. Med. (IF 7.5) Pub Date : 2024-03-20 Aleksandar Kovačević, Bojana Bašaragin, Nikola Milošević, Goran Nenadić
Electronic health records (EHRs) are a valuable resource for data-driven medical research. However, the presence of protected health information (PHI) makes EHRs unsuitable to be shared for research purposes. De-identification, i.e. the process of removing PHI is a critical step in making EHR data accessible. Natural language processing has repeatedly demonstrated its feasibility in automating the
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Cellular data extraction from multiplexed brain imaging data using self-supervised Dual-loss Adaptive Masked Autoencoder Artif. Intell. Med. (IF 7.5) Pub Date : 2024-03-15 Son T. Ly, Bai Lin, Hung Q. Vo, Dragan Maric, Badrinath Roysam, Hien V. Nguyen
Reliable large-scale cell detection and segmentation is the fundamental first step to understanding biological processes in the brain. The ability to phenotype cells at scale can accelerate preclinical drug evaluation and system-level brain histology studies. The impressive advances in deep learning offer a practical solution to cell image detection and segmentation. Unfortunately, categorizing cells
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Multicentric development and validation of a multi-scale and multi-task deep learning model for comprehensive lower extremity alignment analysis Artif. Intell. Med. (IF 7.5) Pub Date : 2024-03-14 Nikolas J. Wilhelm, Claudio E. von Schacky, Felix J. Lindner, Matthias J. Feucht, Yannick Ehmann, Jonas Pogorzelski, Sami Haddadin, Jan Neumann, Florian Hinterwimmer, Rüdiger von Eisenhart-Rothe, Matthias Jung, Maximilian F. Russe, Kaywan Izadpanah, Sebastian Siebenlist, Rainer Burgkart, Marco-Christopher Rupp
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Leveraging code-free deep learning for pill recognition in clinical settings: A multicenter, real-world study of performance across multiple platforms Artif. Intell. Med. (IF 7.5) Pub Date : 2024-03-13 Amir Reza Ashraf, Anna Somogyi-Végh, Sára Merczel, Nóra Gyimesi, András Fittler
Preventable patient harm, particularly medication errors, represent significant challenges in healthcare settings. Dispensing the wrong medication is often associated with mix-up of lookalike and soundalike drugs in high workload environments. Replacing manual dispensing with automated unit dose and medication dispensing systems to reduce medication errors is not always feasible in clinical facilities
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GravityNet for end-to-end small lesion detection Artif. Intell. Med. (IF 7.5) Pub Date : 2024-03-13 Ciro Russo, Alessandro Bria, Claudio Marrocco
This paper introduces a novel one-stage end-to-end detector specifically designed to detect small lesions in medical images. Precise localization of small lesions presents challenges due to their appearance and the diverse contextual backgrounds in which they are found. To address this, our approach introduces a new type of pixel-based anchor that dynamically moves towards the targeted lesion for detection
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An innovative artificial intelligence-based method to compress complex models into explainable, model-agnostic and reduced decision support systems with application to healthcare (NEAR) Artif. Intell. Med. (IF 7.5) Pub Date : 2024-03-12 Karim Kassem, Michela Sperti, Andrea Cavallo, Andrea Mario Vergani, Davide Fassino, Monica Moz, Alessandro Liscio, Riccardo Banali, Michael Dahlweid, Luciano Benetti, Francesco Bruno, Guglielmo Gallone, Ovidio De Filippo, Mario Iannaccone, Fabrizio D'Ascenzo, Gaetano Maria De Ferrari, Umberto Morbiducci, Emanuele Della Valle, Marco Agostino Deriu
In everyday clinical practice, medical decision is currently based on clinical guidelines which are often static and rigid, and do not account for population variability, while individualized, patient-oriented decision and/or treatment are the paradigm change necessary to enter into the era of precision medicine. Most of the limitations of a guideline-based system could be overcome through the adoption
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Stable feature selection utilizing Graph Convolutional Neural Network and Layer-wise Relevance Propagation for biomarker discovery in breast cancer Artif. Intell. Med. (IF 7.5) Pub Date : 2024-03-11 Hryhorii Chereda, Andreas Leha, Tim Beißbarth
High-throughput technologies are becoming increasingly important in discovering prognostic biomarkers and in identifying novel drug targets. With Mammaprint, Oncotype DX, and many other prognostic molecular signatures breast cancer is one of the paradigmatic examples of the utility of high-throughput data to deliver prognostic biomarkers, that can be represented in a form of a rather short gene list
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PGKD-Net: Prior-guided and Knowledge Diffusive Network for Choroid Segmentation Artif. Intell. Med. (IF 7.5) Pub Date : 2024-03-11 Yaqi Wang, Zehua Yang, Xindi Liu, Zhi Li, Chengyu Wu, Yizhen Wang, Kai Jin, Dechao Chen, Gangyong Jia, Xiaodiao Chen, Juan Ye, Xingru Huang
The thickness of the choroid is considered to be an important indicator of clinical diagnosis. Therefore, accurate choroid segmentation in retinal OCT images is crucial for monitoring various ophthalmic diseases. However, this is still challenging due to the blurry boundaries and interference from other lesions. To address these issues, we propose a novel prior-guided and knowledge diffusive network
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Monitoring multistage healthcare processes using state space models and a machine learning based framework Artif. Intell. Med. (IF 7.5) Pub Date : 2024-03-10 Ali Yeganeh, Arne Johannssen, Nataliya Chukhrova, Mohammad Rasouli
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Trustworthy clinical AI solutions: A unified review of uncertainty quantification in Deep Learning models for medical image analysis Artif. Intell. Med. (IF 7.5) Pub Date : 2024-03-04 Benjamin Lambert, Florence Forbes, Senan Doyle, Harmonie Dehaene, Michel Dojat
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HR-BGCN [formula omitted] Predicting readmission for heart failure from electronic health records Artif. Intell. Med. (IF 7.5) Pub Date : 2024-03-02 Huiting Ma, Dengao Li, Jumin Zhao, Wenjing Li, Jian Fu, Chunxia Li
Heart failure has become a huge public health problem, and failure to accurately predict readmission will further lead to the disease’s high cost and high mortality. The construction of readmission prediction model can assist doctors in making decisions to prevent patients from deteriorating and reduce the cost burden. This paper extracts the patient discharge records from the MIMIC-III database. It
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Never tell me the odds: Investigating pro-hoc explanations in medical decision making Artif. Intell. Med. (IF 7.5) Pub Date : 2024-03-01 Federico Cabitza, Chiara Natali, Lorenzo Famiglini, Andrea Campagner, Valerio Caccavella, Enrico Gallazzi
This paper examines a kind of explainable AI, centered around what we term , that is a form of support that consists of offering alternative explanations (one for each possible outcome) a specific explanation following specific advice. Specifically, our support mechanism utilizes , featuring analogous cases for each category in a binary setting. Pro-hoc explanations are an instance of what we called
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Automatic quantitative stroke severity assessment based on Chinese clinical named entity recognition with domain-adaptive pre-trained large language model Artif. Intell. Med. (IF 7.5) Pub Date : 2024-02-27 Zhanzhong Gu, Xiangjian He, Ping Yu, Wenjing Jia, Xiguang Yang, Gang Peng, Penghui Hu, Shiyan Chen, Hongjie Chen, Yiguang Lin
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RM-GPT: Enhance the comprehensive generative ability of molecular GPT model via LocalRNN and RealFormer Artif. Intell. Med. (IF 7.5) Pub Date : 2024-02-27 Wenfeng Fan, Yue He, Fei Zhu
Due to the surging of cost, artificial intelligence-assisted de novo drug design has supplanted conventional methods and become an emerging option for drug discovery. Although there have arisen many successful examples of applying generative models to the molecular field, these methods struggle to deal with conditional generation that meet chemists’ practical requirements which ask for a controllable
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ECG-based cardiac arrhythmias detection through ensemble learning and fusion of deep spatial–temporal and long-range dependency features Artif. Intell. Med. (IF 7.5) Pub Date : 2024-02-24 Sadia Din, Marwa Qaraqe, Omar Mourad, Khalid Qaraqe, Erchin Serpedin
Cardiac arrhythmia is one of the prime reasons for death globally. Early diagnosis of heart arrhythmia is crucial to provide timely medical treatment. Heart arrhythmias are diagnosed by analyzing the electrocardiogram (ECG) of patients. Manual analysis of ECG is time-consuming and challenging. Hence, effective automated detection of heart arrhythmias is important to produce reliable results. Different
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Machine learning algorithms to predict outcomes in children and adolescents with COVID-19: A systematic review Artif. Intell. Med. (IF 7.5) Pub Date : 2024-02-24 Adriano Lages dos Santos, Clara Pinhati, Jonathan Perdigão, Stella Galante, Ludmilla Silva, Isadora Veloso, Ana Cristina Simões e Silva, Eduardo Araújo Oliveira
We aimed to analyze the study designs, modeling approaches, and performance evaluation metrics in studies using machine learning techniques to develop clinical prediction models for children and adolescents with COVID-19. We searched four databases for articles published between 01/01/2020 and 10/25/2023, describing the development of multivariable prediction models using any machine learning technique
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Automated peripancreatic vessel segmentation and labeling based on iterative trunk growth and weakly supervised mechanism Artif. Intell. Med. (IF 7.5) Pub Date : 2024-02-24 Liwen Zou, Zhenghua Cai, Liang Mao, Ziwei Nie, Yudong Qiu, Xiaoping Yang
Peripancreatic vessel segmentation and anatomical labeling are pivotal aspects in aiding surgical planning and prognosis for patients with pancreatic tumors. Nevertheless, prevailing techniques often fall short in achieving satisfactory segmentation performance for the peripancreatic vein (PPV), leading to predictions characterized by poor integrity and connectivity. Besides, unsupervised labeling
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Predicting drug activity against cancer through genomic profiles and SMILES Artif. Intell. Med. (IF 7.5) Pub Date : 2024-02-23 Maryam Abbasi, Filipa G. Carvalho, Bernardete Ribeiro, Joel P. Arrais
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Predicting time-to-intubation after critical care admission using machine learning and cured fraction information Artif. Intell. Med. (IF 7.5) Pub Date : 2024-02-22 Michela Venturini, Ingrid Van Keilegom, Wouter De Corte, Celine Vens
Intubation for mechanical ventilation (MV) is one of the most common high-risk procedures performed in Intensive Care Units (ICUs). Early prediction of intubation may have a positive impact by providing timely alerts to clinicians and consequently avoiding high-risk late intubations. In this work, we propose a new machine learning method to predict the time to intubation during the first five days
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Analyzing entropy features in time-series data for pattern recognition in neurological conditions Artif. Intell. Med. (IF 7.5) Pub Date : 2024-02-22 Yushan Huang, Yuchen Zhao, Alexander Capstick, Francesca Palermo, Hamed Haddadi, Payam Barnaghi
In the field of medical diagnosis and patient monitoring, effective pattern recognition in neurological time-series data is essential. Traditional methods predominantly based on statistical or probabilistic learning and inference often struggle with multivariate, multi-source, state-varying, and noisy data while also posing privacy risks due to excessive information collection and modeling. Furthermore
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A label information fused medical image report generation framework Artif. Intell. Med. (IF 7.5) Pub Date : 2024-02-22 Shuifa Sun, Zhoujunsen Mei, Xiaolong Li, Tinglong Tang, Zhanglin Su, Yirong Wu
Medical imaging is an important tool for clinical diagnosis. Nevertheless, it is very time-consuming and error-prone for physicians to prepare imaging diagnosis reports. Therefore, it is necessary to develop some methods to generate medical imaging reports automatically. Currently, the task of medical imaging report generation is challenging in at least two aspects: (1) medical images are very similar
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Online biomedical named entities recognition by data and knowledge-driven model Artif. Intell. Med. (IF 7.5) Pub Date : 2024-02-21 Lulu Cao, Chaochen Wu, Guan Luo, Chao Guo, Anni Zheng
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Intelligent decision support systems for dementia care: A scoping review Artif. Intell. Med. (IF 7.5) Pub Date : 2024-02-19 Amirhossein Eslami Andargoli, Nalika Ulapane, Tuan Anh Nguyen, Nadeem Shuakat, John Zelcer, Nilmini Wickramasinghe
In the context of dementia care, Artificial Intelligence (AI) powered clinical decision support systems have the potential to enhance diagnosis and management. However, the scope and challenges of applying these technologies remain unclear. This scoping review aims to investigate the current state of AI applications in the development of intelligent decision support systems for dementia care. We conducted
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Patient-specific game-based transfer method for Parkinson's disease severity prediction Artif. Intell. Med. (IF 7.5) Pub Date : 2024-02-17 Zaifa Xue, Huibin Lu, Tao Zhang, Max A. Little
Dysphonia is one of the early symptoms of Parkinson's disease (PD). Most existing methods use feature selection methods to find the optimal subset of voice features for all PD patients. Few have considered the heterogeneity between patients, which implies the need to provide specific prediction models for different patients. However, building the specific model faces the challenge of small sample size
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GCNGAT: Drug–disease association prediction based on graph convolution neural network and graph attention network Artif. Intell. Med. (IF 7.5) Pub Date : 2024-02-17 Runtao Yang, Yao Fu, Qian Zhang, Lina Zhang
Predicting drug–disease associations can contribute to discovering new therapeutic potentials of drugs, and providing important association information for new drug research and development. Many existing drug–disease association prediction methods have not distinguished relevant background information for the same drug targeted to different diseases. Therefore, this paper proposes a drug–disease association
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A bimodal feature fusion convolutional neural network for detecting obstructive sleep apnea/hypopnea from nasal airflow and oximetry signals Artif. Intell. Med. (IF 7.5) Pub Date : 2024-02-16 Dandan Peng, Huijun Yue, Wenjun Tan, Wenbin Lei, Guozhu Chen, Wen Shi, Yanchun Zhang
The most prevalent sleep-disordered breathing condition is Obstructive Sleep Apnea (OSA), which has been linked to various health consequences, including cardiovascular disease (CVD) and even sudden death. Therefore, early detection of OSA can effectively help patients prevent the diseases induced by it. However, many existing methods have low accuracy in detecting hypopnea events or even ignore them
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Clinical knowledge-guided deep reinforcement learning for sepsis antibiotic dosing recommendations Artif. Intell. Med. (IF 7.5) Pub Date : 2024-02-15 Yuan Wang, Anqi Liu, Jucheng Yang, Lin Wang, Ning Xiong, Yisong Cheng, Qin Wu
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Triplet-branch network with contrastive prior-knowledge embedding for disease grading Artif. Intell. Med. (IF 7.5) Pub Date : 2024-02-15 Yuexiang Li, Yanping Wang, Guang Lin, Yawen Huang, Jingxin Liu, Yi Lin, Dong Wei, Qirui Zhang, Kai Ma, Zhiqiang Zhang, Guangming Lu, Yefeng Zheng
Since different disease grades require different treatments from physicians, the low-grade patients may recover with follow-up observations whereas the high-grade may need immediate surgery, the accuracy of disease grading is pivotal in clinical practice. In this paper, we propose a Triplet-Branch Network with ContRastive priOr-knoWledge embeddiNg (TBN-CROWN) for the accurate disease grading, which
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CSCA U-Net: A channel and space compound attention CNN for medical image segmentation Artif. Intell. Med. (IF 7.5) Pub Date : 2024-02-14 Xin Shu, Jiashu Wang, Aoping Zhang, Jinlong Shi, Xiao-Jun Wu
Image segmentation is one of the vital steps in medical image analysis. A large number of methods based on convolutional neural networks have emerged, which can extract abstract features from multiple-modality medical images, learn valuable information that is difficult to recognize by humans, and obtain more reliable results than traditional image segmentation approaches. U-Net, due to its simple
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Detecting mental and physical disorders using multi-task learning equipped with knowledge graph attention network Artif. Intell. Med. (IF 7.5) Pub Date : 2024-02-14 Wei Zhang, Ling Kong, Soobin Lee, Yan Chen, Guangxu Zhang, Hao Wang, Min Song
Mental and physical disorders (MPD) are inextricably linked in many medical cases; psychosomatic diseases can be induced by mental concerns and psychological discomfort can ensue from physiological diseases. However, existing medical informatics studies focus on identifying mental or physical disorders from a unilateral perspective. Consequently, no existing domain knowledge base, corpus, or detection
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Automated image label extraction from radiology reports — A review Artif. Intell. Med. (IF 7.5) Pub Date : 2024-02-14 Sofia C. Pereira, Ana Maria Mendonça, Aurélio Campilho, Pedro Sousa, Carla Teixeira Lopes
Machine Learning models need large amounts of annotated data for training. In the field of medical imaging, labeled data is especially difficult to obtain because the annotations have to be performed by qualified physicians. Natural Language Processing (NLP) tools can be applied to radiology reports to extract labels for medical images automatically. Compared to manual labeling, this approach requires
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Healthcare facilities management: A novel data-driven model for predictive maintenance of computed tomography equipment Artif. Intell. Med. (IF 7.5) Pub Date : 2024-02-12 Haopeng Zhou, Qilin Liu, Haowen Liu, Zhu Chen, Zhenlin Li, Yixuan Zhuo, Kang Li, Changxi Wang, Jin Huang
The breakdown of healthcare facilities is a huge challenge for hospitals. Medical images obtained by Computed Tomography (CT) provide information about the patients' physical conditions and play a critical role in diagnosis of disease. To deliver high-quality medical images on time, it is essential to minimize the occurrence frequencies of anomalies and failures of the equipment. We extracted the real-time
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Prognostic prediction of sepsis patient using transformer with skip connected token for tabular data Artif. Intell. Med. (IF 7.5) Pub Date : 2024-02-12 Jee-Woo Choi, Minuk Yang, Jae-Woo Kim, Yoon Mi Shin, Yong-Goo Shin, Seung Park
Sepsis is known as a common syndrome in intensive care units (ICU), and severe sepsis and septic shock are among the leading causes of death worldwide. The purpose of this study is to develop a deep learning model that supports clinicians in efficiently managing sepsis patients in the ICU by predicting mortality, ICU length of stay (>14 days), and hospital length of stay (>30 days). The proposed model
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Temporal self-attention for risk prediction from electronic health records using non-stationary kernel approximation Artif. Intell. Med. (IF 7.5) Pub Date : 2024-02-10 Rawan AlSaad, Qutaibah Malluhi, Alaa Abd-alrazaq, Sabri Boughorbel
Effective modeling of patient representation from electronic health records (EHRs) is increasingly becoming a vital research topic. Yet, modeling the non-stationarity in EHR data has received less attention. Most existing studies follow a strong assumption of stationarity in patient representation from EHRs. However, in practice, a patient’s visits are irregularly spaced over a relatively long period
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Teleconsultation dynamic scheduling with a deep reinforcement learning approach Artif. Intell. Med. (IF 7.5) Pub Date : 2024-02-09 Wenjia Chen, Jinlin Li
In this study, the start time of teleconsultations is optimized for the clinical departments of class A tertiary hospitals to improve service quality and efficiency. For this purpose, first, a general teleconsultation scheduling model is formulated. In the formulation, the number of services (NS) is one of the objectives because of demand intermittency and service mobility. Demand intermittency means
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Improving deep-learning electrocardiogram classification with an effective coloring method Artif. Intell. Med. (IF 7.5) Pub Date : 2024-02-09 Wei-Wen Chen, Chien-Chao Tseng, Ching-Chun Huang, Henry Horng-Shing Lu
Cardiovascular diseases, particularly arrhythmias, remain a leading cause of mortality worldwide. Electrocardiogram (ECG) analysis plays a pivotal role in cardiovascular disease diagnosis. Although previous studies have focused on waveform analysis and model training, integrating additional clinical information, especially demographic data, remains challenging. In this study, we present an innovative
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A novel intelligent model for visualized inference of medical diagnosis: A case of TCM Artif. Intell. Med. (IF 7.5) Pub Date : 2024-02-08 Jiang Qi-yu, Huang Wen-heng, Liang Jia-fen, Sun Xiao-sheng
How to present an intelligent model based on known diagnostic knowledge to assist medical diagnosis and display the reasoning process is an interesting issue worth exploring. This study developed a novel intelligent model for visualized inference of medical diagnosis with a case of Traditional Chinese Medicine (TCM). Four classes of TCM's diagnosis composed of Yin deficiency, Liver Yin deficiency,
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Corrigendum to “DeepGA for automatically estimating fetal gestational age through ultrasound imaging” [Artif. Intell. Med. 135 (2023) 102453] Artif. Intell. Med. (IF 7.5) Pub Date : 2024-02-06 Tingting Dan, Xijie Chen, Miao He, Hongmei Guo, Xiaoqin He, Jiazhou Chen, Jianbo Xian, Yu Hu, Bin Zhang, Nan Wang, Hongning Xie, Hongmin Cai
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DISCOVER: 2-D multiview summarization of Optical Coherence Tomography Angiography for automatic diabetic retinopathy diagnosis Artif. Intell. Med. (IF 7.5) Pub Date : 2024-02-06 Mostafa El Habib Daho, Yihao Li, Rachid Zeghlache, Hugo Le Boité, Pierre Deman, Laurent Borderie, Hugang Ren, Niranchana Mannivanan, Capucine Lepicard, Béatrice Cochener, Aude Couturier, Ramin Tadayoni, Pierre-Henri Conze, Mathieu Lamard, Gwenolé Quellec
Diabetic Retinopathy (DR), an ocular complication of diabetes, is a leading cause of blindness worldwide. Traditionally, DR is monitored using Color Fundus Photography (CFP), a widespread 2-D imaging modality. However, DR classifications based on CFP have poor predictive power, resulting in suboptimal DR management. Optical Coherence Tomography Angiography (OCTA) is a recent 3-D imaging modality offering
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NPB-REC: A non-parametric Bayesian deep-learning approach for undersampled MRI reconstruction with uncertainty estimation Artif. Intell. Med. (IF 7.5) Pub Date : 2024-02-05 Samah Khawaled, Moti Freiman
The ability to reconstruct high-quality images from undersampled MRI data is vital in improving MRI temporal resolution and reducing acquisition times. Deep learning methods have been proposed for this task, but the lack of verified methods to quantify the uncertainty in the reconstructed images hampered clinical applicability. We introduce “NPB-REC”, a non-parametric fully Bayesian framework, for
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Scalable Swin Transformer network for brain tumor segmentation from incomplete MRI modalities Artif. Intell. Med. (IF 7.5) Pub Date : 2024-02-02 Dongsong Zhang, Changjian Wang, Tianhua Chen, Weidao Chen, Yiqing Shen
Deep learning methods have shown great potential in processing multi-modal Magnetic Resonance Imaging (MRI) data, enabling improved accuracy in brain tumor segmentation. However, the performance of these methods can suffer when dealing with incomplete modalities, which is a common issue in clinical practice. Existing solutions, such as missing modality synthesis, knowledge distillation, and architecture-based
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Modelling-based joint embedding of histology and genomics using canonical correlation analysis for breast cancer survival prediction Artif. Intell. Med. (IF 7.5) Pub Date : 2024-01-26 Vaishnavi Subramanian, Tanveer Syeda-Mahmood, Minh N. Do
Traditional approaches to predicting breast cancer patients’ survival outcomes were based on clinical subgroups, the PAM50 genes, or the histological tissue’s evaluation. With the growth of multi-modality datasets capturing diverse information (such as genomics, histology, radiology and clinical data) about the same cancer, information can be integrated using advanced tools and have improved survival
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Development and validation of a deep interpretable network for continuous acute kidney injury prediction in critically ill patients Artif. Intell. Med. (IF 7.5) Pub Date : 2024-01-26 Meicheng Yang, Songqiao Liu, Tong Hao, Caiyun Ma, Hui Chen, Yuwen Li, Changde Wu, Jianfeng Xie, Haibo Qiu, Jianqing Li, Yi Yang, Chengyu Liu
Early detection of acute kidney injury (AKI) may provide a crucial window of opportunity to prevent further injury, which helps improve clinical outcomes. This study aimed to develop a deep interpretable network for continuously predicting the 24-hour AKI risk in real-time and evaluate its performance internally and externally in critically ill patients. A total of 21,163 patients' electronic health