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8th International Conference on Medical and Health Informatics, ICMHI 2024

作者： Li, Ai-Hsien Adams Aruperes, Cristian Daniel Lai, Yen-Jun Chien, Ting-Ying Chiu, Yen-Ling Chan, Chien-Lung Division of Cardiology Far Eastern Memorial Hospital New Taipei TaiwanGraduate Program in Biomedical Informatics Yuan Ze University Taoyuan Taiwan Graduate Program in Biomedical Informatics Yuan Ze University Taoyuan Taiwan TaiwanDepartment of Computer Science and Engineering Yuan Ze University Taoyuan Taiwan TaiwanInnovation Center for Big Data and Digital Convergence Yuan Ze University Taoyuan Taiwan Department of Information Management Yuan Ze University Taoyuan Taiwan Graduate Institute of Medicine and Graduate Program in Biomedical Informatics Yuan Ze University Taoyuan Taiwan TaiwanGraduate Institute of Medicine Yuan Ze University Taoyuan Taiwan TaiwanDepartment of Medical Research Far Eastern Memorial Hospital New Taipei Taiwan Department of Radiology Far Eastern Memorial Hospital New Taipei Taiwan Department of Information Management Yuan Ze University Taoyuan Taiwan

ISBN: (纸本)9798400716874

In the realm of research, the global health challenge posed by lung cancer remains pronounced, contributing substantially to annual cancer-related fatalities. The critical imperative lies in the early identification of pulmonary nodules, frequently indicative of impending lung cancer, to enhance patient outcomes and diminish mortality rates. Computed Tomography (CT) imaging stands out as a pivotal diagnostic instrument for the timely detection of these nodules. The swift proliferation of medical imaging data has underscored the pressing necessity for precise and efficient methodologies dedicated to nodule segmentation and measurement. These approaches are crucial in assisting radiologists in their diagnostic and clinical decision-making endeavors. In this study, we introduced a thorough method for analyzing lung nodules, leveraging dataset from Far Eastern Memorial Hospital (FEMH) comprising original CT images and manually annotated ground truth masks obtained with the assistance of radiologists at FEMH. This dataset is utilized for the segmentation of nodules. We employed advanced deep learning models, specifically the U-Net architecture, identified as the optimal model through our training process. We made substantial progress in nodule segmentation, attaining an Intersection over Union (IoU) score of 0.824 and a Dice Coefficient of 0.903 for the FEMH dataset. Furthermore, our performance improved when utilizing the merged dataset comprising FEMH and Luna16, yielding an IoU score of 0.862 and a Dice Coefficient of 0.926. Luna16 has been extensively utilized in numerous studies related to nodule detection and segmentation. In the next phase of the study, the best-performing model from our segmentation phase was utilized to predict nodule masks on the merged dataset. Subsequently, we measured the size of each predicted nodule by comparing it with the size ground truth mask in millimeters. In detail, this study achieved the Pearson Correlation Coefficient (PCC) at 0.

关键词： Computerized tomography

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Hospital Transfers for Hip Fracture Patients Are Associated With Higher Cost and Mortality

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Journal of Arthroplasty 2025年

作者： Hutchison, Catherine E.T. Rollier, Genevieve Li, Hsin-Fang Wells, Erica Workman, Kimberly L. Duwelius, Paul J. Lieberman, Elizabeth G. Oregon Health & Science University Department of Orthopaedics and Rehabilitation Portland Oregon United States Providence St. Joseph Health Surgery & Orthopedic Program Portland Oregon United States Providence St. Joseph Health Center for Cardiovascular Analytics Research & Data Science (CARDS) Portland Oregon United States Orthopedic and Fracture Specialists Portland Oregon United States

Background: Transferring hip fracture patients may increase morbidity and cost. The purpose of this study was to examine time to surgery, outcomes, and cost in patients undergoing operative management of displaced femoral neck fractures (DFNFs), comparing those admitted through the emergency department (ED) and those transferred from outside hospitals. Methods: A retrospective review of surgically managed DFNF patients (age ≥ 65 years) at a single institution between May 29, 2020, and December 29, 2022, was performed. Patients were categorized into two groups: “ED arrivals” and “transfers.” Demographics, time from presentation to surgery, hospital length of stay, discharge disposition, 1-year readmission, reoperation, and mortality, and total cost of care were collected and compared. We identified 459 patients (ED = 422, 92.0%, Transfer = 37, 8.0%). There was no difference in age, sex, body mass index, or American Society of Anesthesiologists status. Results: Median time from ED to operating room was higher in the transfer group (41.6 versus 23.2 hours, P < 0.0001). Transfer and ED patients had similar length of stay (5.9 versus 6.0 days, P = 0.29) and similar rates of discharge to skilled nursing facility (66.1 versus 62.2%, P = 0.39). There was a higher rate of 1-year mortality (27.0 versus 13.5% for ED, P = 0.03) in transfer patients but no difference in 1-year readmission or reoperation. Adjusting for demographics, transfer patients had 2.6 higher odds of 1-year mortality than ED patients (odds ratio = 2.59 (1.13 to 5.94) P = 0.03). The cost of care was higher for transfer patients ($23,521 versus $13,980, P < 0.0001). Conclusions: Transferring DFNF patients was associated with delayed time to surgery, increased 1-year mortality, and higher costs. Transferring patients is a logistical reality for many conditions;however, efforts should be made to provide efficient, safe, and cost-effective care. © 2025 Elsevier Inc.

关键词： femoral neck fracture geriatric fracture health care costs hip fracture transfer of care

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Explainable machine learning models for estimating daily dissolved oxygen concentration of the Tualatin River

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Engineering Applications of Computational Fluid Mechanics 2024年第1期18卷

作者： Li, Shuguang Qasem, Sultan Noman Band, Shahab S. Ameri, Rasoul Pai, Hao-Ting Mehdizadeh, Saeid School of Computer Science and Technology Shandong Technology and Business University Yantai China Computer Science Department College of Computer and Information Sciences Imam Mohammad Ibn Saud Islamic University (IMSIU) Riyadh Saudi Arabia Computer Science Department Faculty of Applied Science Taiz University Taiz Yemen Future Technology Research Center National Yunlin University of Science and Technology Douliu Taiwan Department of Information Management International Graduate School of Artificial Intelligence National Yunlin University of Science and Technology Douliu Taiwan Department of Information Management National Yunlin University of Science and Technology Douliu Taiwan Bachelor Program of Big Data Applications in Business National Pingtung University Pingtung Taiwan Water Engineering Department Urmia University Urmia Iran

Monitoring the quality of river water is of fundamental importance and needs to be taken into consideration when it comes to the research into the hydrological field. In this context, the concentration of the dissolved oxygen (DO) is one of the most significant indicators of the quality of river water. The current study aimed to estimate the minimum, maximum, and mean DO concentrations (DO min, DO max, DO mean) at a gauging station located on Tualatin River, United States. To that end, four machine learning models, such as support vector regression (SVR), multi-layer perceptron (MLP), random forest (RF), and gradient boosting (GB) were established. Root mean square error (RMSE), mean absolute error (MAE), coefficient of correlation (R), and Nash-Sutcliffe efficiency (NSE) metrics were employed to better assess the accuracies of these models. The modeling results demonstrated that the SVR and MLP surpassed the RF and GB models. Despite this, the SVR was concluded to be the best-performing method when used to estimate the DO min, DO max, and DO mean. The best error statistics in the testing phase were related to the SVR model with full (four) inputs to estimate DO mean concentration (RMSE = 0.663 mg/l, MAE = 0.508 mg/l, R = 0.945, NSE = 0.875). Finally, the explainability of the superior models (i.e. SVR models) was conducted using SHapley Additive exPlanations (SHAP) for the first time to estimate DO concentration. In fact, evaluating the explainability of machine learning models can provide useful information about the impact of each of the input estimators used in the procedure of models development. It was concluded that the specific conductance (SC) and followed by water temperature (WT) could provide the most contributions for estimating the DO min, DO max, and DO mean concentrations. © 2024 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.

关键词： dissolved oxygen concentration estimation Explainable machine learning SHapley additive explanations

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Variational Transformer Ansatz for the Density Operator of Steady States in Dissipative Quantum Many-Body Systems

arXiv

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arXiv 2025年

作者： Wei, Lu Jia, Zhian Wang, Yufeng Kaszlikowski, Dagomir Ling, Haibin Department of Applied Mathematics & Statistics Stony Brook University Stony BrookNY11794 United States Program of Data Science Stony Brook University Stony BrookNY11794 United States Centre for Quantum Technologies National University of Singapore SG 117543 Singapore Department of Physics National University of Singapore SG 117543 Singapore Department of Computer Science Stony Brook University Stony BrookNY11794 United States

The transformer architecture, known for capturing long-range dependencies and intricate patterns, has extended beyond natural language processing. Recently, it has attracted significant attention in quantum information and condensed matter physics. In this work, we propose the transformer density operator ansatz for determining the steady states of dissipative quantum many-body systems. By vectorizing the density operator as a many-body state in a doubled Hilbert space, the transformer encodes the amplitude and phase of the state’s coefficients, with its parameters serving as variational variables. Our design preserves translation invariance while leveraging attention mechanisms to capture diverse long-range correlations. We demonstrate the effectiveness of our approach by numerically calculating the steady states of dissipative Ising and Heisenberg spin chain models, showing that our method achieves excellent accuracy in predicting steady states. © 2025, CC BY.

关键词： Hilbert spaces

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Empowering Precision Medicine: AI-Driven Schizophrenia Diagnosis via EEG Signals: A Comprehensive Review from 2002-2023

arXiv

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arXiv 2023年

作者： Jafari, Mahboobeh Sadeghi, Delaram Shoeibi, Afshin Alinejad-Rokny, Hamid Beheshti, Amin García, David López Chen, Zhaolin Acharya, U. Rajendra Gorriz, Juan M. Internship in BioMedical Machine Learning Lab The Graduate School of Biomedical Engineering UNSW Sydney SydneyNSW2052 Australia Data Science and Computational Intelligence Institute University of Granada Spain BioMedical Machine Learning Lab The Graduate School of Biomedical Engineering UNSW Sydney SydneyNSW2052 Australia UNSW Data Science Hub The University of New South Wales SydneyNSW2052 Australia Health Data Analytics Program Centre for Applied Artificial Intelligence Macquarie University Sydney2109 Australia Data Science Lab School of Computing Macquarie University SydneyNSW2109 Australia Monash University Melbourne Australia School of Mathematics Physics and Computing University of Southern Queensland Springfield Australia Department of Psychiatry University of Cambridge United Kingdom

Schizophrenia (SZ) is a prevalent mental disorder characterized by cognitive, emotional, and behavioral changes. Symptoms of SZ include hallucinations, illusions, delusions, lack of motivation, and difficulties in concentration. While the exact causes of SZ remain unproven, factors such as brain injuries, stress, and psychotropic drugs have been implicated in its development. SZ can be classified into different types, including paranoid, disorganized, catatonic, undifferentiated, and residual. Diagnosing SZ involves employing various tools, including clinical interviews, physical examinations, psychological evaluations, the Diagnostic and Statistical Manual of Mental Disorders (DSM), and neuroimaging techniques. Electroencephalography (EEG) recording is a significant functional neuroimaging modality that provides valuable insights into brain function during SZ. However, EEG signal analysis poses challenges for neurologists and scientists due to the presence of artifacts, long-term recordings, and the utilization of multiple channels. To address these challenges, researchers have introduced artificial intelligence (AI) techniques, encompassing conventional machine learning (ML) and deep learning (DL) methods, to aid in SZ diagnosis. This study reviews papers focused on SZ diagnosis utilizing EEG signals and AI methods. The introduction section provides a comprehensive explanation of SZ diagnosis methods and intervention techniques. Subsequently, review papers in this field are discussed, followed by an introduction to the AI methods employed for SZ diagnosis and a summary of relevant papers presented in tabular form. Additionally, this study reports on the most significant challenges encountered in SZ diagnosis, as identified through a review of papers in this field. Future directions to overcome these challenges are also addressed. The discussion section examines the specific details of each paper, culminating in the presentation of conclusions and findings. © 2023,

关键词： Electroencephalography

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Geochemistry and the optics of geospatial analysis as a preposition of water quality on a macroscale

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Environmental science and Pollution Research 2024年第22期31卷 32614-32636页

作者： Lozano, Liliana P. Taleb, Manal F. Abou Ibrahim, Mohamed M. Gonçalves, Janaína O. Neckel, Alcindo Schmitz, Guilherme P. Bodah, Brian William Korcelski, Cleiton Maculan, Laércio Stolfo Silva, Luis F. O. Department of Civil and Environmental Engineering Universidad de La Costa CUC Calle 58 #55-66 Atlántico Barranquilla Colombia Postgraduate Doctoral Program in Society Nature and Development Universidade Federal Do Oeste Do Pará UFOPA Paraná68040-255 Brazil Department of Chemistry College of Science and Humanities in Al-Kharj Prince Sattam Bin Abdulaziz University Al-Kharj11942 Saudi Arabia Department of Chemistry College of Science Taif University Taif21944 Saudi Arabia ATITUS Educação RS Passo Fundo30499070-220 Brazil Universidade Do Minho UMINHO Porto4710-057 Portugal Thaines and Bodah Center for Education and Development 840 South Meadowlark Lane OthelloWA99344 United States Workforce Education & amp Applied Baccalaureate Programs Yakima Valley College South 16 Avenue & Nob Hill Boulevard YakimaWA98902 United States CDLAC—Data Collection Laboratory and Scientific Analysis LTDA Porto Alegre90670-090 Brazil

The water treatment depends exclusively on the identification of residues containing toxic chemical elements accumulated in NPs (nanoparticles), and ultrafine particles sourced from waste piles located at old, abandoned sulfuric acid factories containing phosphogypsum requires global attention. The general objective of this study is to quantify and analyze the hazardous chemical elements present in the leachate of waste from deactivated sulfuric acid factories, coupled in NPs and ultrafine particles, in the port region of the city of Imbituba, Santa Catarina, Brazil. Samples were collected in 2020, 2021, and 2022. Corresponding images from the Sentinel-3B OLCI satellite, taken in the same general vicinity, detected the levels of absorption coefficient of Detritus and Gelbstoff (ADG443_NN) in 443 m−1, chlorophyll-a (CHL_NN (m−3)), and total suspended matter (TSM_NN (g m−3) at 72 points on the marine coast of the port region. The results of inductively coupled plasma atomic-emission spectrometry (ICP-AES) and inductively coupled plasma mass spectrometry (ICP-MS) demonstrate that the leaching occurring in waste piles at the port area of Imbituba was the likely source of hazardous chemical elements (e.g., Mg, Sr, Nd, and Pr) in the environment. These leachates were formed due to the presence of coal pyrite and Fe-acid sulfates in said waste piles. The mobility of hazardous chemical elements potentiates changes in the marine ecosystem, in relation to ADG443_NN (m−1), CHL_NN (m−3), and TSM NN (g m−3), with values greater than 20 g m−3 found in 2021 and 2022. This indicated changes in the natural conditions of the marine ecosystem up to 30 km from the coast in the Atlantic Ocean, justifying public initiatives for water treatment on a global scale. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Nanoparticles

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Human leukocyte antigen distributions do not share a copula across sub-populations

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Experimental Results 2022年 3卷 e24-e24页

作者： Schellhas, Dan Green, Robert C. Data Science Program Bowling Green State University Bowling Green 43402 OH United States Department of Computer Science Bowling Green State University Bowling Green 43402 OH United States

The distribution of human leukocyte antigens in the population assists in matching solid organ donors and recipients when the typing methods used do not provide sufficiently precise information. This is made possible by linkage disequilibrium (LD), where alleles co-occur more often than random chance would suggest. There is a trade-off between the high bias and low variance of a broad sample from the population and the low bias but high variance of a focused sample. Some of this trade-off could be alleviated if sub-populations shared LD despite having different allele frequencies. These experiments show that Bayesian estimation can balance bias and variance by tuning the effective sample size of the reference panel, but the LD as represented by an additive or multiplicative copula is not shared. © 2022. The Author(s).

关键词： copula estimation human leukocyte antigens imputation organ transplantation

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The impact of knowledge of hospitalization on mortality predictions

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Health Services and Outcomes Research Methodology 2025年 1-21页

作者： Qin, Chuanling Peterson, Curtis Weeks, William B. James O’Malley, A. Program in Quantitative Biomedical Sciences Geisel School of Medicine at Dartmouth Lebanon USA Dartmouth Institute for Health Policy and Clinical Practice and Quantitative Biomedical Sciences Geisel School of Medicine at Dartmouth Lebanon USA AI for Good Lab Microsoft Corporation Redmond USA Department of Biomedical Data Science and The Dartmouth Institute for Health Policy and Clinical Practice Geisel School of Medicine at Dartmouth Lebanon USA

Despite the rapid advancement of machine learning algorithms, the important problem of distinguishing patients based on the likelihood of their mortality remains a challenge. In this paper, we investigated the degree to which the incorporation of the time-varying factor, length of hospitalization could contribute to modeling mortality. A two-part modeling approach was proposed to capture the potential heterogeneity over follow-up time and to evaluate the extent to which allowing a predictor based on a fixed-time event like hospitalization (as a time-varying coefficient) enhanced mortality prediction. A test was then conducted to assess whether the association between hospitalization and mortality diminished with continued survival of a patient. Leveraging logistic regression models and the XGBoost procedure, the findings supported the claim that the baseline hospitalization is a risk factor whose importance diminishes the longer the patient survives. While simulation studies and theoretical considerations indicate that the two-part model provides deeper insight into the evolving dynamics of regression coefficients and enhances the prediction accuracy of the marginal probability of mortality, its application to the empirical data that motivated this research yielded less compelling results, a finding that aligns with previous findings. Factors such as class imbalance and the magnitude of heterogeneous effects can significantly impact the performance of the two-part model in empirical datasets.

关键词： Continuation-ratio multinomial model Interim observation Marginal probability Time-varying effect Two-part model

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Comparing Naïve Bayes, Decision Tree and Logistic Regression Methods in Fraudulent Credit Card Transactions

Comparing Naïve Bayes, Decision Tree and Logistic Regressio...

引用

2020 International Conference on data Analytics for Business and Industry: Way Towards a Sustainable Economy, ICDABI 2020

作者： Alanezi, Maha A. Homeed, Mawra T. Mohamed, Zahra S. Zeki, Ahmed M. University of Bahrain Big Data Science Analytics Master Program Collage of Science Sakhir Bahrain University of Bahrain Collage of Information Technology Department of Information System Sakhir Bahrain

ISBN: (纸本)9781728196756

data mining is utilized to explore banks' data to unravel any hidden scams and detect potential frauds. The aim of this paper is to compare between the Naïve Bayes, Decision Tree and Logistic Regression in fraudulent credit card transactions. Cross-Industry Standard Process for data Mining (CRISP-DM) is followed to achieve the aim of this research. In terms of accuracy, the best classification model was Logistic Regression with 94.6% accuracy, compared with the Decision Tree and Naïve Bayes that showed accuracy of 89.1% and 90.9% respectively. Other measures were also calculated like time needed to build the model among others. © 2020 IEEE.

关键词： Decision trees

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Applying Parallel Processing to Improve the Computation Speed of K-Nearest Neighbor Algorithm

Applying Parallel Processing to Improve the Computation Spee...

引用

2020 International Conference on data Analytics for Business and Industry: Way Towards a Sustainable Economy, ICDABI 2020

作者： Alanezi, Maha A. Alqaddoumi, Abdulla University of Bahrain Collage of Science Big Data Science and Analytics Master Program Sakhir Bahrain University of Bahrain Collage of Information Technology Department of Information System Sakhir Bahrain

ISBN: (纸本)9781728196756

K-Nearest Neighbor (KNN) is a widely used algorithm to gain an accurate and efficient classification. One of the drawbacks of the algorithm is the time required to calculate the distance for each point. In this paper, the aim is to speed up the KNN algorithm with the implementation of multi threads and multiprocessors to reduce the time to execute the algorithm. There are two medical datasets used to apply the KNN algorithm for the comparison of the sequential and parallel performance. The datasets utilized are Heart Test and Breast Cancer Wisconsin (Diagnostic) data Sets. The multiprocessing is used to parallelize the computation of the distance of KNN. The parallel KNN outperforms the sequential version with the speedup increase, leading to reduction in time for both the processes and threads. © 2020 IEEE.

关键词： Learning algorithms

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