[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120301-en":3,"doc-seo-120301-105":30,"detail-sidebar-cat-0-en-105":90},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},120301,1374391975076,"Riley","https://ap-avatar.wpscdn.com/avatar/14000253ca4ec9f6853?x-image-process=image/resize,m_fixed,w_180,h_180&k=1783305029341752051",7,"Healthcare","Application of Machine Learning Algorithms in Medical Data Processing - Conference Paper","Machine learning applies advanced computation and inference to extract actionable insights from medical data and to support clinician decision making. Using inputs such as medical images, biomarkers, and patient records, methods including convolutional neural networks and classification or regression models enable the creation of personalized models tailored to individual patients. The approach supports clinical practice through decision-support workflows, improving diagnostic speed while aiming for accuracy and repeatability across diverse medical tasks.","2nd International Conference on Chemo and Bioinformatics, September 28-29, 2023. Kragujevac, Serbia  \nApplication of Machine Learning Algorithms in Medical Data Processing Tijana Geroski1,2,*, Nenad Filipović1,2  \n1 University of Kragujevac, Faculty of Engineering, Sestre Janjić 6, 34000 Kragujevac, Serbia  \n2 Bioengineering Research and Development Center (BioIRC), Prvoslava Stojanovića 6, 34000 Kragujevac, Serbia  \ne-mails: [tijanas@kg.ac.rs](tijanas@kg.ac.rs), [fica@kg.ac.rs](fica@kg.ac.rs)  \n* Corresponding author  \nDOI: 10.46793/ICCBI23 .379G  \nAbstract: Machine learning (ML) leverages sophisticated computation and inference to generate insights, enables the system to reason and learn, and empowers clinician decision making. Starting from data (medical images, biomarkers, patients’ data) and using powerful tools such as convolutional neural networks, classification and regression models, etc., it aims at creating personalized models, adapted to each patient, which can be applied in real clinical practice as a decision support system to doctors.  \nKeywords: image processing, deep learning, data mining, medical expert systems  \n1. Introduction  \nAdvances in computational power paired with massive amounts of data generated in healthcare systems make many clinical problems ripe for Machine Learning (ML) applications. Machine Learning has been successfully applied in the automation of the process of analysis of medical data, shortening the time for diagnosis, as well as ensuring high accuracy and repeatability of results. Algorithms can be applied to automatically diagnose diseases based on MRI/CT/X-ray images, predict patient survival rates more accurately, estimate treatment effects on patients using data from randomized trials and automate the task of labeling medical datasets using natural language processing. Algorithms in medicine have so far demonstrated several potential benefits to both physicians and patients.  \n2. Application of Machine Learning in Medical Data Processing  \n[www.iccbikg2023.kg.ac.rs](www.iccbikg2023.kg.ac.rs| |379)[| |379](www.iccbikg2023.kg.ac.rs| |379)  \nML has found application in several fields of medicine. One example is the stratification of patients with carotid artery disease by analysing clinical and personalized data, plaque and cerebral image processing and novel biomarkers [1] . Convolutional neural network U-net was used in plaque components segmentation (semantic segmentation)  \n(Figure 1) .  \na) b) c)  \nFigure 1. Original ultrasound image (a), extracted carotid artery (b), annotated plaque (c) .  \nAnother interesting area of ML application is the analysis of patient-specific data and the development of patient-specific models for monitoring and assessment of patient conditions with familiar cardiomyopathy [2] . Ultrasound images are processed in order to segment the Left ventricle and reconstruct a 3D model of the heart (Figure 2) .  \nFigure 2. Segmentation of Left Ventricle in apical view images performed by U-net.  \nOther areas include the integration of different machine learning algorithms into one multiscale platform to investigate cancer, cardiovascular, bone disorders and tissue engineering [3], prediction of coating thickness to increase the lifespan of biomaterial susceptible to corrosion [4] or even contribute to developing drug-eluting devices to combat the burden of peripheral artery disease (PAD) [5] . Machine Learning also plays its role in the development of personalized models for COVID-19 prediction in patients or epidemiological models for monitoring of number of people infected with COVID-19 [6] .  \n3. Conclusions  \nThe astonishing capacity of machine learning to analyse massive quantities of data, make sense of images, and discover patterns that even the most expert human eye  \nmisses, has inspired hope that technology may improve medicine. Finally, ML holds the promise of “making health care human again” by bringing the physician closer to the patient by creating ","cbCaij2FprcuCUVB","https://ap.wps.com/l/cbCaij2FprcuCUVB","pdf",466135,1,3,"English","en",105,"# Introduction\n# Application of Machine Learning in Medical Data Processing\n## Image segmentation and modeling\n## Multiscale platforms and disease prediction\n# Conclusions\n# Acknowledgment\n# References","[{\"question\":\"What medical data sources does machine learning use for medical processing in this work?\",\"answer\":\"The document highlights medical images, biomarkers, and patients’ data as the starting inputs for machine learning models.\"},{\"question\":\"How does machine learning support clinical decision making?\",\"answer\":\"It generates insights from data and enables personalized models that can be used in real clinical practice as decision support for doctors.\"},{\"question\":\"What are common applications mentioned for machine learning in medical data processing?\",\"answer\":\"Examples include disease diagnosis from MRI/CT/X-ray images, patient stratification, image segmentation (e.g., carotid plaque and left ventricle), prediction of survival and treatment effects, and monitoring or modeling for conditions such as COVID-19 and peripheral artery disease.\"}]","Application of Machine Learning Algorithms in Medical Data Processing - 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