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The Effect of Epidemiological Cohort Creation on the Machine Learning Prediction of Homelessness and Police Interaction Outcomes Using Administrative Health Care Data

arXiv

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arXiv 2023年

作者： Shahidi, Faezehsadat MacDonald, M. Ethan Seitz, Dallas Messier, Geoffrey Electrical and Software Engineering Schulich School of Engineering University of Calgary CalgaryAB Canada Biomedical Engineering Schulich School of Engineering University of Calgary CalgaryAB Canada Hotchkiss Brain Institute University of Calgary CalgaryAB Canada Department of Psychiatry Cumming School of Medicine University of Calgary CalgaryAB Canada

Background: Mental illness can lead to adverse outcomes such as homelessness and police interaction and understanding of the events leading up to these adverse outcomes is important. Predictive models may help identify individuals at risk of such adverse outcomes. Using a fixed observation window cohort with logistic regression (LR) or machine learning (ML) models can result in lower performance when compared with adaptive and parcellated windows. Method: An administrative healthcare dataset was used, comprising of 240,219 individuals in Calgary, Alberta, Canada who were diagnosed with addiction or mental health (AMH) between April 1, 2013, and March 31, 2018. The cohort was followed for 2 years to identify factors associated with homelessness and police interactions. To understand the benefit of flexible windows to predictive models, an alternative cohort was created. Then LR and ML models, including random forests (RF), and extreme gradient boosting (XGBoost) were compared in the two cohorts. Results: Among 237,602 individuals, 0.8% (1,800) experienced first homelessness, while 0.32% (759) reported initial police interaction among 237,141 individuals. Male sex (AORs: H=1.51, P=2.52), substance disorder (AORs: H=3.70, P=2.83), psychiatrist visits (AORs: H=1.44, P=1.49), and drug abuse (AORs: H=2.67, P=1.83) were associated with initial homelessness (H) and police interaction (P). XGBoost showed superior performance using the flexible method (sensitivity =91%, AUC =90% for initial homelessness, and sensitivity =90%, AUC=89% for initial police interaction) Conclusion: This study identified key features associated with initial homelessness and police interaction and demonstrated that flexible windows can improve predictive modeling. © 2023, CC BY-NC-ND.

关键词： Law enforcement

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Mesenchymal stromal cell-derived small extracellular vesicles modulate macrophage polarization and enhance angio-osteogenesis to promote bone healing

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Genes & Diseases 2022年第4期9卷 841-844页

作者： Shang Jiunn Chuah Chee Weng Yong Kristeen Ye Wen Teo Jacob Ren Jie Chew Yi Ann Cheow Shipin Zhang Raymond Chung Wen Wong Asher Ah Tong Lim Sai Kiang Lim Wei Seong Toh Faculty of Dentistry National University of Singapore119085Singapore Institute of Molecular and Cell Biology Agency for ScienceTechnology and Research138648Singapore Department of Orthopaedic Surgery Yong Loo Lin School of MedicineNational University of Singapore119288Singapore Department of Biomedical Engineering Faculty of EngineeringNational University of Singapore117583Singapore Tissue Engineering Program Life Sciences InstituteNational University of Singapore117456Singapore Integrative Sciences and Engineering Program NUS Graduate SchoolNational University of Singapore119077Singapore

Mesenchymal stem/stromal cells(MSCs)have demonstrated therapeutic efficacy for bone regeneration in animal and clinical *** MSCs were initially thought to differentiate to various cell types to replace the injured/diseased tissue,it is now accepted that these cells secrete factors to promote tissue repair.1 Among these factors,small extracellular vesicles(sEVs)of size 50–200 nm,which include the exosomes,have been identified as the principal agent mediating the wide-ranging therapeutic efficacy of MSCs.2 Several studies have also reported the therapeutic effects of MSC-sEVs to enhance bone repair in animal models,as recently reviewed.3 However,the cellular processes and mechanisms mediated by MSC-sEVs in bone regeneration remain to be fully elucidated.

关键词： healing clinical macrophage

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Reflectance outperforms force and position in model-free needle puncture detection

Reflectance outperforms force and position in model-free nee...

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Annual International Conference of the IEEE engineering in Medicine and Biology Society (EMBC)

作者： Rachael L’Orsa Anupam Bisht Linhui Yu Kartikeya Murari David T. Westwick Garnette R. Sutherland Katherine J. Kuchenbecker Engineering’s Department of Electrical and Software Engineering Schulich School University of Calgary Calgary Canada Project NeuroArm Cumming School of Medicine University of Calgary Calgary Canada Haptic Intelligence Department Max Planck Institute for Intelligent Systems Stuttgart Germany Hotchkiss Brain Institute Cumming School of Medicine University of Calgary Calgary Canada Department of Biomedical Engineering Schulich School of Engineering’s University of Calgary Calgary Canada

ISBN: (数字)9798350371499

ISBN: (纸本)9798350371505

The surgical procedure of needle thoracostomy temporarily corrects accidental over-pressurization of the space between the chest wall and the lungs. However, failure rates of up to 94.1% have been reported, likely because this procedure is done blind: operators estimate by feel when the needle has reached its target. We believe instrumented needles could help operators discern entry into the target space, but limited success has been achieved using force and/or position to try to discriminate needle puncture events during simulated surgical procedures. We thus augmented our needle insertion system with a novel in-bore double-fiber optical setup. Tissue reflectance measurements as well as 3D force, torque, position, and orientation were recorded while two experimenters repeatedly inserted a bevel-tipped percutaneous needle into ex vivo porcine ribs. We applied model-free puncture detection to various filtered time derivatives of each sensor data stream offline. In the held-out test set of insertions, puncture-detection precision improved substantially using reflectance measurements compared to needle insertion force alone (3.3-fold increase) or position alone (11.6-fold increase).

关键词： Reflectivity Force measurement Torque Three-dimensional displays Force Surgery Position measurement Aerospace electronics Needles Torque measurement

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Continuum Damage Modeling of Biaxial Fatigue Failure in Whole Bone: A Hybrid Approach with Machine Learning Integration

arXiv

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arXiv 2025年

作者： Kakavand, Reza Dimnik, Jonah M. Haider, Ifaz T. Brent Edwards, W. Human Performance Laboratory Faculty of Kinesiology University of Calgary Calgary Canada McCaig Institute for Bone and Joint Health University of Calgary Calgary Canada Department of Biomedical Engineering Schulich School of Engineering University of Calgary Canada

Repetitive loading of bone is associated with microdamage accumulation and material property degradation that may ultimately result in fatigue fracture. Our previous work used continuum damage mechanics (CDM)-based finite element (FE) modeling to predict stiffness loss and fatigue failure in whole bone;however, this model did not account for inter-specimen variability in fatigue behaviour and multiaxial loading effects, which limited its applied efficacy. In this study, we refined the CDM-based FE model to predict experimental fatigue-life measurements from 21 whole rabbit tibiae subjected to cyclic axial compression with different magnitudes of superposed torsion. Machine learning (ML) methods were used to predict damage parameters from initial, undamaged, conditions, which were then used to predicted stiffness degradation and fatigue failure with the CDM-based FE modeling pipeline. Regression analysis was used to compare CDM-based FE fatigue-life predictions with experimental measurements. A random forest ML model predicted specimen-specific damage parameters with high accuracy (R2 = 0.85) and the CDM-based FE models demonstrated remarkable predictive capability, explaining up to 91% of the variance in fatigue-life measurements. Stiffness degradation profiles also followed a similar trend to experimental measurements;however, this agreement worsened with the level of superposed torsion suggesting additional refinements to the model may be necessary. These findings demonstrate the efficacy of integrating ML with CDM-based FE modeling to predict fatigue life and stiffness degradation. The observed agreement with experimental measurements suggests the modeling framework may provide valuable information regarding the mechanisms of fatigue fracture in whole bone. © 2025, CC BY.

关键词： Continuum damage mechanics

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Early detection and staging of retinitis pigmentosa using multifocal electroretinogram parameters and machine learning algorithms

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Physical and engineering sciences in medicine 2025年 2025 Jun 16页

作者： Bayram Karaman Ayse Öner Aysegül Güven Graduate School of Natural and Applied Sciences Biomedical Engineering Graduate Program Erciyes University Kayseri Turkey. baryamkaraman103@***. Department of Electrical and Electronics Engineering Engineering and Architecture Faculty Tokat Gaziosmanpasa University Tokat Turkey. baryamkaraman103@***. Department of Ophthalmology Acibadem Taksim Hospital Istanbul Turkey. Department of Biomedical Engineering Engineering Faculty Erciyes University Kayseri Turkey.

Retinitis pigmentosa is an inherited retinal disease caused by damage to photoreceptor cells. Diagnosis and staging of this disease are crucial for early intervention and effective treatment planning. In this study, the amplitude and latency features of N1, P1, and N2 waves obtained from multifocal electroretinogram responses over five rings were used with binary and multiclass classification methods using four different machine learning algorithms to distinguish retinitis pigmentosa patients from healthy individuals and to evaluate the stages of the disease. Binary classifications were performed for six different groups, and the Naive Bayes (NB) algorithm performed the best on all evaluation metrics, achieving 99% accuracy in distinguishing healthy individuals from each disease stage. Furthermore, multiclass classification was applied in two different steps. In the first step, the Naive Bayes model achieved 82% accuracy in four-class classification, including healthy individuals. Considering the near-perfect separability of healthy individuals, in the second step, a three-class classification including only disease stages was performed, and the model achieved 76% accuracy. These results indicate that the proposed approach provides objective and accurate staging for retinitis pigmentosa and can serve as a valuable decision support system to assist ophthalmologists in clinical practice.

关键词： Logistic regression Multifocal electroretinogram Multilayer perceptron neural networks Naive Bayes Retinitis pigmentosa Support vector machine

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Title: Challenges in single cells sequencing Microbial community and biofilm: A case of Oleidesulfovibrio alaskensis G20 NGS protocol

Title: Challenges in single cells sequencing Microbial commu...

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2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022

作者： Singh, Ram Nageena Gnimpieba, Etienne Z. Sani, Rajesh Kumar Chemical and Biological Engineering South Dakota School of Mines and Technology Rapid City United States University of South Dakota Gear Center Biomedical Engineering Program Sioux Falls United States

ISBN: (纸本)9781665468190

Biofilm, is a special-complex organization of bacterial cells with multiple layers, formed in certain environmental conditions. In the complex biofilm different bacterial (single) cells perform different functions and contribute to overall biofilm activity, where single bacterial cells are in different physiological states of growth, stress and signaling. Sulphate reducing bacteria (SRB) contributes to huge economic loss ({}4B) causing microbial induced corrosion. To effectively combat the challenges posed by SRB, it is essential to understand their molecular mechanisms of biofilm formation and biocorrosion. Single Cell Genomics (single cell transcriptomics) is a technique which investigates the gene expression at the level of a single biological cell and could not be attainable in bulk analysis (RNASeq) could decipher the uniqueness of each cell in complex group. We are developing a NGS (oxford Nanopore)-workflow for single cell transcriptomics of biofilms using Oleidesulfovibrio alaskensis (earlier Desulfovibrio alaskensis) strain G20 (OA-G20) as a model. We encountered several challenges to design and develop the workflow such 1) absence of ploy(A) tail in mRNAs, 2) amount of total RNA, 3) half-lives of mRNAs, 4) perforation of cell membrane, 5) low copy number of mRNAs, 6) single cell suspension and cell sorting, 7) quantification of bacterial cells, 8) fixation of cells, 9) designing of probes, 10) hybridization of probes and 11) amplification of mRNAs. Here, we are presenting how we resolved some of these challenges to design and develop a single cell genomics (transcriptomics) workflow for SRB biofilms. © 2022 IEEE.

关键词： Genome

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A comprehensive overview of diffuse correlation spectroscopy: theoretical framework, recent advances in hardware, analysis, and applications

arXiv

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arXiv 2024年

作者： Wang, Quan Pan, Mingliang Kreiss, Lucas Samaei, Saeed Carp, Stefan A. Johansson, Johannes D. Zhang, Yuanzhe Wu, Melissa Horstmeyer, Roarke Diop, Mamadou Li, David Day-Uei University of Strathclyde Department of Biomedical Engineering Faculty of Engineering Glasgow United Kingdom Duke University Department of Biomedical Engineering DurhamNC United States Western University Department of Medical and Biophysics Schulich School of Medical & Dentistry LondonON Canada Harvard Medical School Massachusetts General Hospital Optics at Athinoula A. Martinos Center for Biomedical Imaging CharlestownMA United States Linköping University Department of Biomedical Engineering Linköping Sweden Imaging Program Lawson Health Research Institute LondonON Canada

Diffuse correlation spectroscopy (DCS) is a powerful tool for assessing microvascular hemodynamic in deep tissues. Recent advances in sensors, lasers, and deep learning have further boosted the development of new DCS methods. However, newcomers might feel overwhelmed, not only by the already complex DCS theoretical framework but also by the broad range of component options and system architectures. To facilitate new entry into this exciting field, we present a comprehensive review of DCS hardware architectures (continuous-wave, frequency-domain, and time-domain) and summarize corresponding theoretical models. Further, we discuss new applications of highly integrated silicon single-photon avalanche diode (SPAD) sensors in DCS, compare SPADs with existing sensors, and review other components (lasers, fibers, and correlators), as well as new data analysis tools, including deep learning. Potential applications in medical diagnosis are discussed, and an outlook for the future directions is provided, to offer effective guidance to embark on DCS research. Copyright © 2024, The Authors. All rights reserved.

关键词： Correlators

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Meta-Learning for Generalizable Connectome Modeling Across Heterogeneous Atlas Spaces 22

Meta-Learning for Generalizable Connectome Modeling Across H...

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22nd IEEE International Symposium on biomedical Imaging, ISBI 2025

作者： Liang, Qinghao Adkinson, Brendan D. Scheinost, Dustin Department of Biomedical Engineering Yale University New HavenCT United States Interdepartmental Neuroscience Program Yale University New HavenCT United States Department of Radiology and Biomedical Imaging Yale School of Medicine New HavenCT United States Department of Statistics and Data Science Yale University New HavenCT United States

ISBN: (纸本)9798331520526

Leveraging large-scale neuroimaging datasets to improve predictive modeling in smaller, specialized cohorts is essential for translating research findings into clinical applications. To address this challenge, we propose a meta-learning inspired approach that enhances phenotype prediction by exploiting inter-phenotype correlations across independent datasets, without requiring uniform imaging data processing. By employing mappings derived from a small, independent cohort with data processed using different atlases, we align the data in the meta-training set to the target space of the meta-testing set. This approach eliminates the need for extensive data reprocessing. Evaluation in the UK Biobank dataset shows that our framework significantly improves predictive performance on unseen phenotypes, demonstrating its potential to generalize connectome-based models to small, heterogeneous cohorts. © 2025 IEEE.

关键词： Sorting

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Detection of physiological changes in rheumatoid arthritis using time-domain near-infrared imaging: an in silico phantom study 15

Detection of physiological changes in rheumatoid arthritis u...

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Optical Tomography and Spectroscopy of Tissue XV 2023

作者： Ioussoufovitch, Seva Diop, Mamadou School of Biomedical Engineering Collaborative Training Program in Musculoskeletal Health Research Bone & Joint Institute Western University London Canada Dept. of Medical Biophysics Western University London Canada Imaging Program Lawson Health Research Institute London Canada

ISBN: (纸本)9781510658578

Early treatment improves rheumatoid arthritis (RA) prognosis;however, 30% of patients still fail their first treatment and current methods can take as long as 3–6 months to detect treatment failure. Time-domain near-infrared diffuse optical imaging (TD-DOI) has the potential to track gradual changes in RA disease activity and could be a more sensitive technique for RA treatment monitoring. Nevertheless, there has been very little investigation into the relationships between TD-DOI and the specific physiological changes that occur in RA. To this end, this work’s objective was to investigate the effects of RA-associated physiological changes on TD-DOI images in silico. Virtual finger phantoms—derived from an MRI segmentation of a healthy human finger—were used to simulate changes in 5 physiological parameters that are typically affected in RA. Four levels of disease activity were considered for each parameter and each parameter was altered individually;parameter alterations were then translated into changes in phantom geometry and optical properties. MCXLAB was used to simulate the propagation of time-domain light (800 nm) through phantoms’ proximal interphalangeal joints and Poisson noise was added to the TD-DOI data to simulate experimental conditions. Spatiotemporal Fourier decomposition was applied to the TD-DOI data to extract image components, which were grouped into datasets based on the physiological parameter that was altered. Component sensitivity to each physiological parameter was assessed using the ratio between a component’s range and standard deviation within each dataset (range-to-error ratio;RER). Mean RER was highest for changes in synovial membrane and fluid volume, suggesting that this parameter may be a primary source of contrast for monitoring RA treatment response with TD-DOI. © 2023 SPIE.

关键词： Phantoms

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Denoising Simulated Low-Field MRI (70mT) using Denoising Autoencoders (DAE) and Cycle-Consistent Generative Adversarial Networks (Cycle-GAN)

arXiv

引用

arXiv 2023年

作者： Vega, Fernando Addeh, Abdoljalil MacDonald, M. Ethan Department of Biomedical Engineering Schulich School of Engineering University of Calgary CalgaryAB Canada Department of Electrical & Software Engineering Schulich School of Engineering University of Calgary CalgaryAB Canada Hotchkiss Brain Institute Cumming School of Medicine University of Calgary CalgaryAB Canada Department of Radiology Cumming School of Medicine University of Calgary CalgaryAB Canada

In this work, a denoising Cycle-GAN (Cycle Consistent Generative Adversarial Network) is implemented to yield high-field, high resolution, high signal-to-noise ratio (SNR) Magnetic Resonance Imaging (MRI) images from simulated low-field, low resolution, low SNR MRI images. Resampling and additive Rician noise were used to simulate low-field MRI. Images were utilized to train a Denoising Autoencoder (DAE) and a Cycle-GAN, with paired and unpaired cases. Both networks were evaluated using SSIM and PSNR image quality metrics. This work demonstrates the use of a generative deep learning model that can outperform classical DAEs to improve low-field MRI images and does not require image pairs. Copyright © 2023, The Authors. All rights reserved.

关键词： Magnetic resonance imaging

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