Computers in biology and medicine最新文献

{"title":"Trends and advances in image-based mosquito identification and classification using machine learning models: A systematic review","authors":"Alice Bagyiereyele Lakyiere ,&nbsp;Rose-Mary Owusuaa Gyening Mensah ,&nbsp;Nutifafa Yao Agbenor-Efunam ,&nbsp;Edmund Yamba ,&nbsp;Kingsley Badu","doi":"10.1016/j.compbiomed.2025.110373","DOIUrl":"10.1016/j.compbiomed.2025.110373","url":null,"abstract":"<div><div>Mosquito-borne diseases, such as Yellow fever, Dengue, and Zika, pose a significant global health threat, causing millions of deaths annually. Traditional mosquito identification methods, reliant on expert analysis, are time-consuming and resource-intensive. Machine Learning (ML) has emerged as a transformative solution, enabling rapid and accurate species identification and classification. Recent studies leverage morphological features, such as wings and body structures, to determine species, sex, and age. These innovations aim to revolutionize vector control strategies, making them faster, more accurate, and widely accessible. This systematic review evaluates ML-based mosquito identification research, highlighting its strengths, limitations, and geographic disparities in contributions. Data was collected from Google Scholar, PubHub, IEEE Xplore, and ScienceDirect (2000–2024), with 52 studies meeting the inclusion criteria out of an initial pool of 1,050 papers. A key highlight of this review is the role of feature extraction techniques in achieving high classification accuracy by capturing fine-grained morphological traits. The findings also reveal critical limitations that hinder real-world applicability. These include limited dataset diversity, inconsistent preprocessing practices across devices, all of which reduce the generalizability of models in varied environments. Furthermore, high computational requirements and morphological similarities between certain species challenge the scalability and robustness of machine learning models. To address these gaps, measures such as expanding annotated and diverse datasets, investing in low-resource model deployment strategies, and supporting African-led research initiatives can be utilized to ensure more inclusive and context-relevant mosquito surveillance systems.</div></div>","PeriodicalId":10578,"journal":{"name":"Computers in biology and medicine","volume":"193 ","pages":"Article 110373"},"PeriodicalIF":7.0,"publicationDate":"2025-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144116761","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A multimodal deep learning framework for enzyme turnover prediction with missing modality","authors":"Xin Sun ,&nbsp;Yu Guang Wang ,&nbsp;Yiqing Shen","doi":"10.1016/j.compbiomed.2025.110348","DOIUrl":"10.1016/j.compbiomed.2025.110348","url":null,"abstract":"<div><div>Accurate prediction of the turnover number (<span><math><msub><mrow><mi>k</mi></mrow><mrow><mi>cat</mi></mrow></msub></math></span>), which quantifies the maximum rate of substrate conversion at an enzyme’s active site, is essential for assessing catalytic efficiency and understanding biochemical reaction mechanisms. Traditional wet-lab measurements of <span><math><msub><mrow><mi>k</mi></mrow><mrow><mi>cat</mi></mrow></msub></math></span> are time-consuming and resource-intensive, making deep learning (DL) methods an appealing alternative. However, existing DL models often overlook the impact of reaction products on <span><math><msub><mrow><mi>k</mi></mrow><mrow><mi>cat</mi></mrow></msub></math></span> due to feedback inhibition, resulting in suboptimal performance. The multimodal nature of this <span><math><msub><mrow><mi>k</mi></mrow><mrow><mi>cat</mi></mrow></msub></math></span> prediction task, involving enzymes, substrates, and products as inputs, presents additional challenges when certain modalities are unavailable during inference due to incomplete data or experimental constraints, leading to the inapplicability of existing DL models. To address these limitations, we introduce <strong>MMKcat</strong>, a novel framework employing a prior-knowledge-guided missing modality training mechanism, which treats substrates and enzyme sequences as essential inputs while considering other modalities as maskable terms. Moreover, an innovative auxiliary regularizer is incorporated to encourage the learning of informative features from various modal combinations, enabling robust predictions even with incomplete multimodal inputs. We demonstrate the superior performance of MMKcat compared to state-of-the-art methods, including DLKcat, TurNup, UniKP, EITLEM-Kinetic, DLTKcat and GELKcat, using BRENDA and SABIO-RK. Our results show significant improvements under both complete and missing modality scenarios in RMSE, <span><math><msup><mrow><mi>R</mi></mrow><mrow><mn>2</mn></mrow></msup></math></span>, and SRCC metrics, with average improvements of 6.41%, 22.18%, and 8.15%, respectively. Codes are available at <span><span>https://github.com/ProEcho1/MMKcat</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":10578,"journal":{"name":"Computers in biology and medicine","volume":"193 ","pages":"Article 110348"},"PeriodicalIF":7.0,"publicationDate":"2025-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144107113","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A hybrid frequency-spatial domain unsupervised denoising model for Gaussian-Poisson mixed noise in medical imaging","authors":"Cheng Zhang,&nbsp;Kin Sam Yen","doi":"10.1016/j.compbiomed.2025.110374","DOIUrl":"10.1016/j.compbiomed.2025.110374","url":null,"abstract":"<div><div>This paper proposes an unsupervised denoising model designed to address Gaussian-Poisson hybrid noise in CT, MRI, and X-ray images. Traditional deep image prior (DIP) methods suffer from slow convergence, spectral bias, and overfitting, limiting their clinical applicability. In this paper, by applying the Fourier transform, we incorporate frequency-domain priors extracted from the observed noisy image at the input stage. Instead of using both amplitude and phase, we rely solely on the amplitude spectrum, which captures the energy distribution of various frequency components while avoiding the instability associated with phase information. Meanwhile, we retain the spatial domain information to preserve the image's structural integrity, ensuring the effective capture of both low-frequency details and high-frequency anatomical features. This dual-domain strategy allows fine details to be captured early in training, thereby mitigating spectral bias, accelerating convergence, and improving the preservation of high-frequency anatomical structures. To further enhance diagnostic fidelity, we replace the conventional mean squared error (MSE) loss with an edge-aware L1 loss function that better preserves critical anatomical textures. Additionally, an entropy-based criterion tracks variations in image uncertainty over iterations to determine the optimal stopping point, effectively preventing overfitting without the need for external validation data. Experimental results demonstrate that our model achieves an average improvement of 10.7 % in PSNR and 17.9 % in SSIM compared to DIP, reaching peak performance in just 60 iterations, faster than the 1360 and 2990 iterations required by DIP and DIP-AITV, respectively. These findings highlight the efficiency and effectiveness of our method for medical image denoising.</div></div>","PeriodicalId":10578,"journal":{"name":"Computers in biology and medicine","volume":"193 ","pages":"Article 110374"},"PeriodicalIF":7.0,"publicationDate":"2025-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144116246","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The impact of different material types on ergonomics in lower extremity exoskeleton construction","authors":"İsmail Çalıkuşu ,&nbsp;Ugur Fidan","doi":"10.1016/j.compbiomed.2025.110403","DOIUrl":"10.1016/j.compbiomed.2025.110403","url":null,"abstract":"<div><div>This study examines the effects of materials such as A Glass Fiber, Aluminum Alloy, Stainless Steel, S Glass Fiber, C Graphite, Hexcel, and Thornel on biomechanical performance in the design of lower extremity exoskeletons. Exoskeleton models created using Computer-Aided Modeling software were integrated into the AnyBody Modeling System and combined with a full-body human model to conduct walking simulations. In these simulations, femur and tibia segments were also incorporated into the model to analyze the impacts of the exoskeleton on human movement dynamics in detail. The results reveal that material selection significantly influences joint reaction forces and moments, ground reaction forces, and contact forces. Flexible materials were found to provide greater comfort to the user but demonstrated lower durability performance. Conversely, more durable materials improved overall efficiency by reducing load transfer. These findings emphasize that material selection in exoskeleton design plays a critical role not only in durability and performance but also in meeting ergonomic requirements. This research offers a valuable foundation for developing exoskeleton designs tailored to different user groups and highlights the need to customize material selection to optimize biomechanical performance. The study serves as a guide for enhancing the compatibility of exoskeletons with human movement dynamics and improving user comfort.</div></div>","PeriodicalId":10578,"journal":{"name":"Computers in biology and medicine","volume":"193 ","pages":"Article 110403"},"PeriodicalIF":7.0,"publicationDate":"2025-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144116247","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"CancerNet: A comprehensive deep learning framework for precise and intelligible cancer identification","authors":"S.M. Nuruzzaman Nobel ,&nbsp;Shirin Sultana ,&nbsp;Md All Moon Tasir ,&nbsp;M.F. Mridha ,&nbsp;Zeyar Aung","doi":"10.1016/j.compbiomed.2025.110339","DOIUrl":"10.1016/j.compbiomed.2025.110339","url":null,"abstract":"<div><div>The medical community continually seeks innovative solutions to address healthcare challenges, particularly in cancer detection. A promising approach involves the use of Artificial Intelligence (AI) techniques, specifically Deep Learning (DL) models. This research introduces CancerNet, incorporating convolutional, involutional, and transformer components to extract hierarchical features and capture long-range dependencies from medical imaging data across the channel and spatial domains. CancerNet was trained and evaluated on an extensive dataset of histopathological images (HI) of tumor tissues and validated on the DeepHisto dataset, which comprises whole slide images (WSI) of various subtypes of glioma. CancerNet surpasses other comparative models and, achieves a higher accuracy on both datasets. CancerNet exhibits robustness across various imaging conditions, thereby ensuring reliable performance in various clinical scenarios. By integrating Explainable AI (XAI) techniques, CancerNet enhances transparency in its decision-making process, improves understanding and fosters trust in clinical adoption. CancerNet achieved an accuracy of 98.77% on the Histopathological Image dataset and 97.83% on the DeepHisto validation dataset, proving to be more effective than previous. Furthermore, transparency in AI models is crucial as it enhances healthcare professionals ability to understand and trust the model’s decision-making process, facilitating their adoption in clinical settings.</div></div>","PeriodicalId":10578,"journal":{"name":"Computers in biology and medicine","volume":"193 ","pages":"Article 110339"},"PeriodicalIF":7.0,"publicationDate":"2025-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144106737","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A rule-based clinical decision support system for detection of acute kidney injury after pediatric cardiac surgery","authors":"Janice Wachenbrunner ,&nbsp;Marcel Mast ,&nbsp;Julia Böhnke ,&nbsp;Nicole Rübsamen ,&nbsp;Louisa Bode ,&nbsp;André Karch ,&nbsp;Henning Rathert ,&nbsp;Alexander Horke ,&nbsp;Philipp Beerbaum ,&nbsp;Michael Marschollek ,&nbsp;Thomas Jack ,&nbsp;Martin Böhne","doi":"10.1016/j.compbiomed.2025.110382","DOIUrl":"10.1016/j.compbiomed.2025.110382","url":null,"abstract":"<div><h3>Background</h3><div>Acute kidney injury (AKI) is common in children with congenital heart disease following open-heart surgery with cardiopulmonary bypass (CPB). Early AKI detection in critically ill children requires clinician expertise to compile various data from different sources within a stressful and time-sensitive environment. However, as electronic health records provide data in a machine-readable format, this process could be supported by computerized systems. Therefore, we developed a time-aware, rule-based clinical decision support system (CDSS) to detect, stage, and track temporal AKI progression in children.</div></div><div><h3>Methods</h3><div>We integrated retrospective clinical routine data from n = 290 randomly selected cases (n = 263 patients, aged 0–17 years) who underwent cardiac surgery with CPB into a dataset. We adapted <em>Kidney Disease: Improving Global Outcome</em> (KDIGO) criteria, including serum creatinine, urine output, and estimated glomerular filtration rate, and translated them into computable rules for the CDSS. As a reference standard, patients were manually assessed by blinded clinical experts.</div></div><div><h3>Results</h3><div>The AKI incidence, according to the reference standard, was n = 146 cases for stage 1, n = 58 for stage 2, and n = 20 for stage 3. The CDSS achieved sensitivities of 92.2 % (95 % CI: 86.8–95.5 %) for AKI stage 1, 88.1 % (95 % CI: 77.2–94.2 %) for stage 2, and 95 % (95 % CI: 70.5–99.3 %) for stage 3. The specificities were 97.0 % (95 % CI: 94.4–98.4 %), 98.5 % (95 % CI: 96.5–99.4 %), and 99.3 % (95 % CI: 97.3–99.8 %), respectively.</div></div><div><h3>Conclusions</h3><div>We demonstrated that a CDSS is able to perform a complex AKI detection and staging process, including 11 criteria across three stages. For accurate automated AKI detection, standardized machine-readable data of high data quality are required. CDSS with high diagnostic accuracy, like presented, can support clinical management and be used for surveillance and quality management. The prototypical use for surveillance and further studies, such as the development of prediction models, should demonstrate the system's benefits in the future.</div></div>","PeriodicalId":10578,"journal":{"name":"Computers in biology and medicine","volume":"193 ","pages":"Article 110382"},"PeriodicalIF":7.0,"publicationDate":"2025-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144107111","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Transformation trees — Documentation of multimodal image registration","authors":"Agnieszka Anna Tomaka,&nbsp;Dariusz Pojda,&nbsp;Michał Tarnawski,&nbsp;Leszek Luchowski","doi":"10.1016/j.compbiomed.2025.110311","DOIUrl":"10.1016/j.compbiomed.2025.110311","url":null,"abstract":"<div><div>Multimodal image registration plays a key role in creating digital patient models by combining data from different imaging techniques into a single coordinate system. This process often involves multiple sequential and interconnected transformations, which must be well-documented to ensure transparency and reproducibility. In this paper, we propose the use of transformation trees as a method for structured recording and management of these transformations. This approach has been implemented in the dpVision software and uses a dedicated .dpw file format to store hierarchical relationships between images, transformations, and motion data. Transformation trees allow precise tracking of all image processing steps, reduce the need to store multiple copies of the same data, and enable the indirect registration of images that do not share common reference points. This improves the reproducibility of the analyses and facilitates later processing and integration of images from different sources. The practical application of this method is demonstrated with examples from orthodontics, including the integration of 3D face scans, intraoral scans, and CBCT images, as well as the documentation of mandibular motion. Beyond orthodontics, this method can be applied in other fields that require systematic management of image registration processes, such as maxillofacial surgery, oncology, and biomechanical analysis. Maintaining long-term data consistency is essential for both scientific research and clinical practice. It enables easier comparison of results in longitudinal studies, improves retrospective analysis, and supports the development of artificial intelligence algorithms by providing standardized and well-documented datasets. The proposed approach enhances data organization, allows for efficient analysis, and facilitates the reuse of information in future studies and diagnostic procedures.</div></div>","PeriodicalId":10578,"journal":{"name":"Computers in biology and medicine","volume":"193 ","pages":"Article 110311"},"PeriodicalIF":7.0,"publicationDate":"2025-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144107112","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Mutational and expression analysis of classical protein tyrosine phosphatase genes in pancreatic ductal adenocarcinoma","authors":"Maryam Naeem,&nbsp;Khursheed Ahmed,&nbsp;Aneesa Sultan","doi":"10.1016/j.compbiomed.2025.110319","DOIUrl":"10.1016/j.compbiomed.2025.110319","url":null,"abstract":"<div><h3>Background</h3><div>Pancreatic cancer is a highly lethal and aggressive malignancy with a minimal five-year survival rate (5 %) and a high mortality rate. The most common and fast-growing type of pancreatic cancer is PDAC, which constitutes 90 % of all cases.</div></div><div><h3>Objective</h3><div>Numerous signaling pathways are disrupted in PDAC. We explored the mutational status and expression profiling of classical protein tyrosine phosphatase (PTP) genes, which are vital regulators of multiple significant signaling pathways.</div></div><div><h3>Method</h3><div>Through whole exome sequencing, we identified potentially pathogenic non-synonymous variants that were subsequently analyzed <em>in-silico</em>. To validate these findings, quantitative real-time PCR was performed on blood samples from PDAC patients to assess the expression of deleterious genes.</div></div><div><h3>Results</h3><div>All the potential pathogenic variants were localized within the phosphatase domain 1, fibronectin type III domain, and the FERM domain of classical PTPs regions crucial for the proper functioning of the respective proteins. Among the analyzed genes, PTPN3, PTPN12, PTPRK, and PTPRZ1 were found statistically significant (<em>p</em> &lt; 0.05), highlighting their potential as novel prognostic biomarkers and therapeutic targets for PDAC.</div></div><div><h3>Conclusion</h3><div>These findings hold particular relevance for the Pakistani population, offering valuable insights into the genetic landscape of this aggressive cancer.</div></div>","PeriodicalId":10578,"journal":{"name":"Computers in biology and medicine","volume":"193 ","pages":"Article 110319"},"PeriodicalIF":7.0,"publicationDate":"2025-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144106713","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A cluster attention-based multiple instance learning network for enhancing histopathological image interpretation","authors":"Seokhwan Ko ,&nbsp;Yu Ando ,&nbsp;Moonsik Kim ,&nbsp;Nora Jee-Young Park ,&nbsp;Hyungsoo Han ,&nbsp;Ji Young Park ,&nbsp;Junghwan Cho","doi":"10.1016/j.compbiomed.2025.110353","DOIUrl":"10.1016/j.compbiomed.2025.110353","url":null,"abstract":"<div><h3>Background:</h3><div>Histopathological diagnosis involves examining abnormal architectural patterns and cellular-level changes. Whole slide images (WSIs) provide comprehensive digital representations of tissue samples, enabling detailed analysis and interpretation. Annotating the giga-pixel images remains labor-intensive, requiring experts to label abnormal patterns and cellular changes. To address this, Multiple Instance Learning (MIL), a promising weakly supervised approach, enables models to learn from limited annotations while preserving key histopathological features.</div></div><div><h3>Method:</h3><div>However, existing MIL-based methods may overlook potential semantic features, limiting their effectiveness. To overcome this limitation, we propose a novel Cluster-Aware Attention-based MIL (CAAMIL) architecture. This approach employs an advanced attention-based module integrated with a clustering method to enhance the interpretability of heterogeneous features. Our approach clusters architectural or cytologic features, making the groups interpretable at the cluster level and reflective of histopathological grades or prognostic indicators.</div></div><div><h3>Results:</h3><div>We demonstrated the efficacy of our model in both slide-level and patch-level classification as well as in interpreting tumor and mutation predictions. Experimental results show that our model achieves an AUC score of 0.96 for tumor detection at slide-level and 0.85 at patch-level, outperforming other state-of-the-art MIL-based methods.</div></div><div><h3>Conclusion:</h3><div>Our proposed CAAMIL architecture overcomes the limitations of existing MIL methods by effectively clustering features and providing interpretable results. The high accuracy and interpretability of our model make it a promising tool for histopathological diagnosis and tumor detection.</div></div>","PeriodicalId":10578,"journal":{"name":"Computers in biology and medicine","volume":"193 ","pages":"Article 110353"},"PeriodicalIF":7.0,"publicationDate":"2025-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144106716","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Agitation-sedation models for critical care: Insights into endogenous agitation reduction and stimulus","authors":"Ryan O’Sullivan,&nbsp;Isaac Flett,&nbsp;Chris Pretty,&nbsp;J. Geoffrey Chase","doi":"10.1016/j.compbiomed.2025.110323","DOIUrl":"10.1016/j.compbiomed.2025.110323","url":null,"abstract":"<div><h3>Background:</h3><div>Sedation and agitation management are core treatments in the intensive care unit. This study uses pharmacokinetic–pharmacodynamic (PKPD) models to capture the endogenous agitation response. The identification and validation of these models allow for a better understanding of agitation-sedation dynamics and improves the clinical implementation.</div></div><div><h3>Methods:</h3><div>A cohort of healthy volunteers (N=25) was exposed to a controlled psychological stimulus, with agitation levels quantitatively measured using heart rate-derived metrics. Endogenous agitation reduction (EAR) coefficients were determined from the post-stimulus decay. Using these parameters and a priori information about the experienced stimulus, the model was validated against the measured agitation data.</div></div><div><h3>Results:</h3><div>The model demonstrated a good fit between measured and modelled agitation. EAR parameters were identified with 45% of the cohort ranging between 0.003–0.004 <span><math><msup><mrow><mi>s</mi></mrow><mrow><mo>−</mo><mn>1</mn></mrow></msup></math></span>. Using a population value for EAR still resulted in a good fit to measured data. Minimal differences were observed between female and male participants.</div></div><div><h3>Conclusion:</h3><div>This study provides further development of PKPD models of agitation-sedation dynamics. The identified EAR parameter can be used in future studies and in the clinical application of these models.</div></div>","PeriodicalId":10578,"journal":{"name":"Computers in biology and medicine","volume":"193 ","pages":"Article 110323"},"PeriodicalIF":7.0,"publicationDate":"2025-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144107110","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}