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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Navya Martin Kollapally Mahshad Koohi H. Dehkordi Yehoshua Perl James Geller Fadi P. Deek Hao Liu Vipina K. Keloth Gai Elhanan Andrew J. Einstein Shuxin Zhou Department of Computer Science Kean University USA Department of Computer Science New Jersey Institue of Technology Newark USA Department of DataScience New Jersey Institue of Technology Newark USA Department of Informatics New Jeresy Institue of Technology Newark USA School of Computing Montclair State University USA Section of Biomedical Informatics and DataScience Yale University New Haven USA School of Medicine University of Nevada Nevada USA Dept. of Medicine Columbia Medical Center New York USA

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Highlighting of Electronic Health Records (EHRs) involves marking essential content of EHR notes, corresponding to concepts of a clinical terminology. However, employing the best clinical terminology (SNOMED CT) for highlighting EHRs, captures only a portion of their crucial content. In this paper, we describe the curation of a Cardiology Interface Terminology (CIT) dedicated to the application of highlighting EHRs of cardiology patients. We utilize a Clinical-Named Entity Recognition (Clinical NER) approach for extracting phrases, of higher granularity than SNOMED CT concepts, from EHRs, for enriching CIT. For this purpose, we train a neural network model with BIOE-tagged (Beginning, Inside, End, and Outside) cardiology entities. Transfer Learning can be used to facilitate the curation of an interface terminology for highlighting EHRs for other specialties e.g. Nephrology. Large-scale highlighting enables overworked physicians and other healthcare providers to fast skim the dense volume of EHRs they regularly read. Secondary research and EHRs interoperability are other applications that can be supported by highlighting.

关键词： Training Nephrology Terminology Biological system modeling Transfer learning Neural networks Medical services Cardiology Electronic medical records Interoperability

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Evaluating online and offline accuracy traversal algorithms for k-complete neural network architectures

arXiv

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arXiv 2021年

作者： Alparslan, Yigit Moyer, Ethan Jacob Kim, Edward College of Computing & Informatics Drexel University PhiladelphiaPA United States School of Biomedical Engineering Drexel University PhiladelphiaPA United States

Architecture sizes for neural networks have been studied widely and several search methods have been offered to find the best architecture size in the shortest amount of time possible. In this paper, we study compact neural network architectures for binary classification and investigate improvements in speed and accuracy when favoring overcomplete architecture candidates that have a very high-dimensional representation of the input. We hypothesize that an overcomplete model architecture that creates a relatively high-dimensional representation of the input will be not only be more accurate but would also be easier and faster to find. In an N ×M search space, we propose an online traversal algorithm that finds the best architecture candidate in O(1) time for best case and O(N) amortized time for average case for any compact binary classification problem by using k-completeness as heuristics in our search. The two other offline search algorithms we implement are brute force traversal and diagonal traversal, which both find the best architecture candidate in O(N × M) time. We compare our new algorithm to brute force and diagonal searching as a baseline and report search time improvement of 52.1% over brute force and of 15.4% over diagonal search to find the most accurate neural network architecture when given the same dataset. In all cases discussed in the paper, our online traversal algorithm can find an accurate, if not better, architecture in significantly shorter amount of time. © 2021, CC BY-NC-SA.

关键词： Network architecture

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Association of beat-to-beat P-wave analysis index to the extent of left atrial low-voltage areas in patients with paroxysmal atrial fibrillation

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Hellenic Journal of Cardiology 2024年

作者： Sakellaropoulou, Antigoni Giannopoulos, Georgios Tachmatzidis, Dimitrios Letsas, Konstantinos P. Antoniadis, Antonios Asvestas, Dimitrios Filos, Dimitrios Mililis, Panagiotis Efremidis, Michael Chouvarda, Ioanna Vassilikos, Vassilios P. 2nd Department of Cardiology Laboratory of Cardiac Electrophysiology Evangelismos General Hospital of Athens Athens Greece 3rd Department of Cardiology School of Medicine Aristotle University of Thessaloniki Thessaloniki Greece Laboratory of Computing Medical Informatics and Biomedical Imaging Technologies School of Medicine Aristotle University of Thessaloniki Thessaloniki Greece

Background: Left atrial (LA) fibrosis has been shown to be associated with atrial fibrillation (AF) recurrence. Beat-to-beat (B2B) index is a non-invasive classifier, based on B2B P-wave morphological and wavelet analysis, shown to be associated with AF incidence and recurrence. In this study, we tested the hypothesis that the B2B index is associated with the extent of LA low-voltage areas (LVAs) on electroanatomical mapping. Methods: Patients with paroxysmal AF scheduled for pulmonary vein isolation, without evident structural remodeling, were included. Pre-ablation electroanatomical voltage maps were used to calculate the surface of LVAs (<0.5 mV). B2B index was compared between patients with small versus large LVAs. Results: 35 patients were included (87% male, median age 62). The median surface area of LVAs was 7.7 (4.4–15.8) cm² corresponding to 5.6 (3.3–12.1) % of LA endocardial surface. B2B index was 0.57 (0.52–0.59) in patients with small LVAs (below the median) compared to 0.65 (0.56–0.77) in those with large LVAs (above the median) (p = 0.009). In the receiver operator characteristic curve analysis for predicting large LVAs, the c-statistic was 0.75 (p = 0.006) for B2B index and 0.81 for the multivariable model including B2B index (multivariable p = 0.04) and P-wave duration. Conclusion: In patients with paroxysmal AF without overt atrial myopathy, B2B P-wave analysis appears to be a useful non-invasive correlate of low-voltage areas—and thus fibrosis—in the LA. This finding establishes a pathophysiological basis for B2B index and its potential usefulness in the selection process of patients who are likely to benefit most from further invasive treatment. © 2024 Hellenic Society of Cardiology

关键词： Beat-to-beat variation Fibrosis Left atrium Low-voltage areas Paroxysmal atrial fibrillation Wavelet

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Digital Health during COVID-19: informatics Dialogue with the World Health Organization

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Yearbook of Medical informatics 2021年第1期30卷 13-16页

作者： Koch, Sabine Hersh, William R. Bellazzi, Riccardo Leong, Tze Yun Yedaly, Moctar Al-Shorbaji, Najeeb Health Informatics Centre Department of Learning Informatics Management and Ethics Karolinska Institutet Stockholm Sweden Department of Medical Informatics & Clinical Epidemiology School of Medicine Oregon Health & Science University Portland OR United States Department of Electrical Computer and Biomedical Engineering University of Pavia Italy IRCCS ICS Maugeri Pavia Italy Department of Computer Science School of Computing National University of Singapore and AI Singapore Singapore Information Society Division African Union Commission Addis Ababa Ethiopia eHealth Development Association Amman Jordan

Background: On December 16, 2020 representatives of the International Medical informatics Association (IMIA), a Non-Governmental Organization in official relations with the World Health Organization (WHO), along with its International Academy for Health Sciences informatics (IAHSI), held an open dialogue with WHO Director General (WHO DG) Tedros Adhanom Ghebreyesus about the opportunities and challenges of digital health during the COVID-19 global pandemic. Objectives: The aim of this paper is to report the outcomes of the dialogue and discussions with more than 200 participants representing different civil society organizations (CSOs). Methods: The dialogue was held in form of a webinar. After an initial address of the WHO DG, short presentations by the panelists, and live discussions between panelists, the WHO DG and WHO representatives took place. The audience was able to post questions in written. These written discussions were saved with participants’ consent and summarized in this paper. Results: The main themes that were brought up by the audience for discussion were: (a) opportunities and challenges in general;(b) ethics and artificial intelligence;(c) digital divide;(d) education. Proposed actions included the development of a roadmap based on the lessons learned from the COVID-19 pandemic. Conclusions: Decision making by policy makers needs to be evidence-based and health informatics research should be used to support decisions surrounding digital health, and we further propose next steps in the collaboration between IMIA and WHO such as future engagement in the World Health Assembly. © 2021 Thieme Medical Publishers, Inc.. All rights reserved.

关键词： Artificial Intelligence COVID-19 Global Health Medical informatics World Health Organization

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Sequential machine learning in prediction of common cancers

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informatics in Medicine Unlocked 2022年 30卷

作者： Andjelkovic, Jovan Ljubic, Branimir Hai, Ameen Abdel Stanojevic, Marija Pavlovski, Martin Diaz, Wilson Obradovic, Zoran Temple University Center for Data Analytics and Biomedical Informatics (DABI) Philadelphia 19121 PA United States Rutgers University The Office of Advanced Research Computing (OARC) Piscataway 08854 NJ United States

Cancer is one of the most common causes of death in the world. It is characterized by the multi-stage transformation of normal cells into tumor cells. Early cancer detection can significantly reduce its consequences, which was the objective of many machine learning (ML) published studies. However, most of them focused on microarray, gene expression, or publicly available medical datasets. Almost none offered an approach for predicting cancer through analysis of sequential data, such as Electronic Health Record (EHR) data. This paper presents a sequential ML approach to predict the occurrence of lung cancer, breast cancer, cervical cancer, and liver cell cancer using EHR data. The accuracy of sequence learning models based on long short-term memory (LSTM) and bidirectional gated recurrent units (GRU) were compared to traditional ML methods based on multilayer perceptron, random forest, decision tree, and K-nearest neighbor. The models were trained and tested on 50,606 patient hospitalization histories. Unsupervised and supervised data reduction methods (singular value decomposition (SVD) and a neural network embedding layer) were applied to overcome the challenges of high-dimensionality and sparsity of EHR data. The results provided evidence that for this application GRU outperforms alternatives based on accuracy, Area Under the Receiver Operating Characteristic curve (AUROC), sensitivity (recall), specificity, precision, and F1 score. It was the best performing model with accuracy between 81% (breast cancer) and 88% (liver cancer) on balanced out of sample EHRs. Multilayer perceptron and LSTM manifested comparable performances (accuracies between 78% and 87%) among the alternatives, while decision tree was the worst-performing model. The findings of this study could potentially aid medical professionals in cancer diagnostics, treatment, and prevention. In particular, experiments confirmed that GRU could accurately predict cancer by learning from simplified patient r

关键词： Breast cancer Cervix uteri cancer Liver cell cancer Lung cancer Machine learning

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Reducing operator complexity in Algebraic Multigrid with Machine Learning Approaches

arXiv

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arXiv 2023年

作者： Huang, Ru Chang, Kai He, Huan Li, Rui Peng Xi, Yuanzhe Department of Mathematics Emory University AtlantaGA30322 United States Department of Biomedical Informatics Harvard University BostonMA02130 United States Center for Applied Scientific Computing Lawrence Livermore National Laboratory P. O. Box 808 L-561 LivermoreCA94551 United States

We propose a data-driven and machine-learning-based approach to compute non-Galerkin coarse-grid operators in algebraic multigrid (AMG) methods, addressing the well-known issue of increasing operator complexity. Guided by the AMG theory on spectrally equivalent coarse-grid operators, we have developed novel ML algorithms that utilize neural networks (NNs) combined with smooth test vectors from multigrid eigenvalue problems. The proposed method demonstrates promise in reducing the complexity of coarse-grid operators while maintaining overall AMG convergence for solving parametric partial differential equation (PDE) problems. Numerical experiments on anisotropic rotated Laplacian and linear elasticity problems are provided to showcase the performance and compare with existing methods for computing non-Galerkin coarse-grid operators. © 2023, CC BY-NC-ND.

关键词： Machine learning

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Prediction of COVID-19 Patients’ Emergency Room Revisit using Multi-Source Transfer Learning

Prediction of COVID-19 Patients’ Emergency Room Revisit usi...

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IEEE International Conference on Healthcare informatics (ICHI)

作者： Yuelyu Ji Yuhe Gao Runxue Bao Qi Li Disheng Liu Yiming Sun Ye Ye Department of Information Science School of Computing and Information University of Pittsburgh Pittsburgh USA Department of Biomedical Informatics School of Medicine University of Pittsburgh Pittsburgh USA Department of Electrical and Computer Engineering Swanson School of Engineering University of Pittsburgh Pittsburgh USA School of Business State University of New York at New Paltz New Paltz USA

The coronavirus disease 2019 (COVID-19) has led to a global pandemic of significant severity. In addition to its high level of contagiousness, COVID-19 can have a heterogeneous clinical course, ranging from asymptomatic carriers to severe and potentially life-threatening health complications. Many patients have to revisit the emergency room (ER) within a short time after discharge, which significantly increases the workload for medical staff. Early identification of such patients is crucial for helping physicians focus on treating life-threatening cases. In this study, we obtained Electronic Health Records (EHRs) of 3,210 encounters from 13 affiliated ERs within the University of Pittsburgh Medical Center between March 2020 and January 2021. We leveraged a Natural Language Processing technique, ScispaCy, to extract clinical concepts and used the 1001 most frequent concepts to develop 7-day revisit models for COVID-19 patients in ERs. The research data we collected were obtained from 13 ERs, which may have distributional differences that could affect the model development. To address this issue, we employed a classic deep transfer learning method called the Domain Adversarial Neural Network (DANN) and evaluated different modeling strategies, including the Multi-DANN algorithm (which considers the source differences), the Single-DANN algorithm (which doesn’t consider the source differences), and three baseline methods: using only source data, using only target data, and using a mixture of source and target data. Results showed that the Multi-DANN models outperformed the Single-DANN models and baseline models in predicting revisits of COVID-19 patients to the ER within 7 days after discharge (median AUROC = 0.8 vs. 0.5). Notably, the Multi-DANN strategy effectively addressed the heterogeneity among multiple source domains and improved the adaptation of source data to the target domain. Moreover, the high performance of Multi-DANN models indicates that EHRs are informat

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Cross-Image Siamese Graph Convolutional Network For Fine-Grained Image Retrieval in Diabetic Retinopathy

SSRN

引用

SSRN 2023年

作者： Chen, Fang Zhao, Weiling Zhou, Xiaobo School of Electronics and Information Engineering Tongji University Shanghai201804 China Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Shanghai201804 China School of Biomedical Informatics The University of Texas Health Science Center at Houston HoustonTX77030 United States Center for Computational Systems Medicine

The categories of diabetic retinopathy (DR) are interrelated, and different ophthalmologists often give different results for the same fundus image. Automatic cross image retrieval of DR can provide an effective diagnostic solution for ophthalmologists and is of great significance in clinical practice. Cross-image (i.e. left and right fundus images for a patient) information is highly correlated and complementary and can be harnessed to improve various computer vision tasks such as image classification, object detection, image segmentation, and image retrieval. Previous studies did not explore the correlation between lesion areas in left and right fundus images of patients, limiting the effective diagnosis of DR. In this study, we proposed a cross-image siamese graph convolutional network(CIS-GCN) to retrieve fine-grained diabetic retinopathy fundus images. First, we constructed a global-specific structure to obtain the specific features of the left and right eyes. Then, we passed the specific features through the pathological localization network to obtain the location features of the lesion. Finally, a graph convolutional neural network was introduced to construct node sets for the left and right eyes, respectively, to represent relatively consistent regions in the fundus images of patients and learn their correlations. We tested our method using Diabetic Retinopathy Detection datasets and the results showed that our algorithm outperforms other state-of-the-art methods by 2.2% ~ 3.7% in image data retrieval. © 2023, The Authors. All rights reserved.

关键词： Object detection

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Laplacian salience-gated feature pyramid network for accurate liver vessel segmentation

TechRxiv

引用

TechRxiv 2023年

作者： Gao, Zhan Zong, Qiuhao Wang, Yiqi Yan, Yan Wang, Yuqing Zhu, Ning Zhang, Jin Wang, Yunfu Zhao, Liang Taihe Hospital Hubei University of Medicine Shiyan442000 China The School of Biomedical Engineering Hubei University of Medicine Shiyan442000 China The Taihe Hospital Hubei Key Laboratory of Embryonic Stem Cell Research Hubei University of Medicine Shiyan442000 China The School of Computing and Electronic Informatics Guangxi University Nanning530004 China

Liver vessels generated from computed tomography are usually pretty small, which poses big challenges for satisfactory vessel segmentation, including 1) the scarcity of high-quality and large-volume vessel masks, 2) the difficulty in capturing vessel-specific features, and 3) the heavily imbalanced distribution of vessels and liver tissues. To advance, a sophisticated model and an elaborated dataset have been built. The model has a newly conceived Laplacian salience filter, highlighting vessel-like regions and suppressing other liver regions, to shape the vessel-specific feature learning and to balance vessels against others. It is further coupled with a pyramid deep learning architecture to capture various levels of features, hence the enhancement of feature formulation. Experiments show that this model markedly outperforms the state-of-the-art approaches, achieving a relative improvement of Dice score by at least 1.63% compared to the existing best model on available datasets. More promisingly, the averaged Dice score produced by existing models on the newly constructed dataset is as high as 0.728 ± 0.067, which is at least 19.1% higher than that obtained from the existing best dataset under the same settings. These observations suggest that the proposed Laplacian salience, along with the elaborated dataset, can be helpful for liver vessel segmentation. © 2023, CC BY.

关键词： Pattern recognition

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Anterior Cruciate Ligament Classification in Knee MRI using Automated Pseudo-Masking

Anterior Cruciate Ligament Classification in Knee MRI using ...

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Medical Imaging 2023: Computer-Aided Diagnosis

作者： Dadsetan, Saba Albers, Marcio Weinstock, Allison Musahl, Volker Kitamura, Gene Arefan, Dooman Wu, Shandong Intelligent Systems Program School of Computing and Information University of Pittsburgh PittsburghPA15213 United States Department of Radiology School of Medicine University of Pittsburgh PittsburghPA15213 United States Department of Biomedical Informatics School of Medicine University of Pittsburgh PittsburghPA15213 United States Department of Bioengineering School of Engineering University of Pittsburgh PittsburghPA15213 United States Department of Orthopedic Surgery School of Medicine University of Pittsburgh PittsburghPA15213 United States Department of Radiology Loma Linda University Medical Center Loma LindaCA92354 United States

ISBN: (纸本)9781510660359

Anterior cruciate ligament (ACL) is one of the most common injuries associated with sports. Knee osseous morphology can play a role in increased knee instability. Our hypothesis is that the morphological features of the knee, as seen in knee osseous morphology, can contribute to increased knee instability and, thus, increase the likelihood of ACL tear. To test this relationship, it is necessary to segment the femur and tibia bones and extract relevant imaging features. However, manual annotation of 3D medical images, such as on magnetic resonance imaging (MRI) scans, can be a time-consuming and challenging task. In this work, we propose an automated pipeline for creating pseudo-masks of the femur and tibia bones in knee MRI. Our approach involves unsupervised segmentation and deep learning models to classify ACL integrity (intact or torn). Our results demonstrate a high agreement between the automated pseudo-masks and a radiologist's manual segmentation, which also leads to comparable AUC values for the ACL integrity classification. © 2023 SPIE.

关键词： Magnetic resonance imaging

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