[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118448-en":3,"doc-seo-118448-105":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},118448,2336464648746,"Skyler","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","Semifinal Results of a Research Project Involving Algorithmic Complexity Estimation and Machine Learning - Paper","Recent advances connect algorithmic complexity with machine learning to analyze continuous biomedical signals. This study reports semifinal findings on estimating Kolmogorov-Chaitin Complexity (KCC) using the Block Decomposition Method and using KCC as a core feature for models predicting sepsis and epileptic seizures. Trained models based on multiple classifiers demonstrate strong performance metrics, with high AUC values indicating reliable predictive capacity. The work supports algorithmic complexity measures as useful signal descriptors for biomedical machine learning.","BIOMEDICAL ENGINEERING 13th IC ECCO  \nSemifinal Results of a Research Project Involving Algorithmic Complexity Estimation and Machine Learning  \nVictor Iapascurta 1,2  \n1 Technical University of Moldova, [victor.iapascurta@doctorat.utm.md](victor.iapascurta@doctorat.utm.md), ORCID: 0000-0002-4540-7045 www. utm 2 N.Testemitanu University of Medicine and Pharmacy, [www.usmf.md](www.usmf.md)  \nKeywords: Kolmogorov-Chaitin complexity, machine learning, sepsis, epilepsy  \nAbstract. In recent years, the intersection of algorithmic complexity and machine learning has opened new avenues for analyzing continuous biomedical data. This paper presents the semifinal results of a research project focused on estimating Kolmogorov-Chaitin Complexity (KCC) using the Block Decomposition Method. KCC serves as a core feature for machine learning models aimed at predicting sepsis and epileptic seizures. The results highlight the efficacy of these models, with promising performance metrics, and underscore the utility of algorithmic complexity measures in enhancing machine learning models for biomedical applications. By leveraging the inherent complexity in biomedical signals, these models achieve superior predictive performance.  \nMethodology. Kolmogorov-Chaitin Complexity (KCC) [1] is a measure of the randomness or information content of a data sequence. Estimating KCC for continuous biomedical signals involves the Block Decomposition Method, which breaks down data into manageable blocks to approximate complexity.  \nThe KCC values derived from biomedical data are used as primary features in various machine learning models. The objective is to predict medical conditions—specifically, sepsis with a 4-hour horizon and epileptic seizures.  \nData and Experimentation. Real-world biomedical data were sourced from international competitions, including time-series data relevant to sepsis  \nBIOMEDICAL ENGINEERING 13th IC ECCO  \nand epilepsy. These datasets provided a robust foundation for training and testing machine learning models.  \nSeveral models, including neural networks, gradient boosting machines, generalized linear models, and others, were trained using the KCC features.  \nResults  \nTable 1. The machine learning models with the highest performance [2] .  \n\n| Data set | ML model | Performance by AUC |\n| --- | --- | --- |\n| Epileptic EEG set | Word2Vec | 96.8% |\n| Sepsis set | Gradient Boosting Machine | 95.3% |\n\nNote: AUC – area under the ROC curve  \nDiscussion. The results underscore the utility of algorithmic complexity measures in enhancing machine learning models for biomedical applications. By leveraging the inherent complexity in biomedical signals, these models achieve superior predictive performance.  \nConclusion. This research project demonstrates the effectiveness of combining algorithmic complexity estimation with machine learning to predict sepsis and epileptic seizures. With AUC scores of 95.3% and 96.8%, respectively, the models show significant promise for real-world medical applications. Further refinement and validation could enhance their utility in clinical practice.  \nReferences  \n[1] H. Zenil, A review of methods for estimating algorithmic complexity: options, challenges, and new directions. Entropy 22(6),(2020) .  \n[2] V. Iapascurta, V. Estimation of Kolmogorov-Chaitin Complexity on Continuous Biomedical Data for Machine Learning Purposes. In: Intelligent and Fuzzy Systems. INFUS 2024. Lecture Notes in Networks and Systems, vol 1090. Springer, Cham. pp. 60-67, 2024.","cbCailQ6rdL7czeg","https://ap.wps.com/l/cbCailQ6rdL7czeg","pdf",91507,1,2,"English","en",105,"# Methodology\n## Kolmogorov-Chaitin Complexity and Block Decomposition\n## Prediction targets and feature usage\n# Data and Experimentation\n## Datasets from international competitions\n## Trained machine learning models\n# Results\n## Best-performing models and AUC metrics\n# Discussion\n# Conclusion\n# References","[{\"question\":\"What problem does the research project address?\",\"answer\":\"It estimates Kolmogorov-Chaitin Complexity (KCC) from continuous biomedical data and uses KCC features for machine learning to predict sepsis and epileptic seizures.\"},{\"question\":\"How is KCC estimated from biomedical time-series signals?\",\"answer\":\"KCC is approximated using the Block Decomposition Method, which splits continuous signals into manageable blocks to estimate complexity.\"},{\"question\":\"What performance results were reported for the best models?\",\"answer\":\"For the epileptic EEG dataset, Word2Vec achieved 96.8% AUC, and for the sepsis dataset, Gradient Boosting Machine achieved 95.3% AUC.\"}]","Semifinal Results of a Research Project Involving Algorithmic Complexity Estimation and Machine Learning - 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