{"doi":"10.1007/978-3-030-92328-0_29","title":"A Less Traditional Approach to Biomedical Signal Processing for Sepsis Prediction","abstract":null,"journal":"IFMBE Proceedings","year":2022,"id":679811,"datarank":0.20794415416798362,"base_score":1.3862943611198906,"endowment":1.3862943611198906,"self_citation_contribution":0.20794415416798362,"citation_network_contribution":0.0,"self_endowment_contribution":0.20794415416798362,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":3,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":null,"is_data_producer":false,"deposit_databanks":null,"is_oa":false,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":null,"fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":1776231,"name":"Victor Iapăscurtă","orcid":null,"position":0,"is_corresponding":false}],"reference_count":0,"raw_metadata":{"has_enrichment":true,"resolved":true,"title":"A Less Traditional Approach to Biomedical Signal Processing for Sepsis Prediction","abstract":"Most of the data generated by monitors in a clinical setting represent time series data which can be visualized and subsequently used for decision making. This usually is the simplest part. A more challenging aspect is using this data for more complex task like machine learning with the same goal – computer assisted decisions. Within this challenge raw biomedical signal data need to be preprocessed before being passed to the machine learning algorithm. This can be done by a multitude of methods. A number of such methods comes from the field of Algorithmic Complexity and although of a promising nature, these particular methods are poorly explored yet. The current research presents an example of applying the Block Decomposition Method to data routinely generated by patients in a modern Intensive Care Unit. The final goal of a larger research, the actual research being part of, is building a system for early sepsis prediction.","is_dataset_classified":null,"base_score":1.3862943611198906,"endowment":1.3862943611198906,"datacite_reuse_total":0,"file_count":0,"downloads":0,"views":0,"has_version_chain":false,"is_dataset":false,"is_oa":false,"pmid":"21097893","pmcid":null,"openalex_id":"https://openalex.org/W4205092988","authors":[],"funders":[],"total_grants":0,"fwci":0.5061,"citation_percentile":0.60674266,"influential_citations":0,"citation_trend":[{"year":2022,"count":1},{"year":2023,"count":1},{"year":2024,"count":1}],"oa_status":"gold","license":"other-oa","oa_locations":[{"url":"https://ibn.idsi.md/vizualizare_articol/142461","host_type":""},{"url":"https://ibn.idsi.md/vizualizare_articol/142461","host_type":""},{"url":"https://link.springer.com/content/pdf/10.1007/978-3-030-92328-0_29","host_type":"publisher"},{"url":"https://doi.org/10.1007/978-3-030-92328-0_29","host_type":"book series"},{"url":"https://ibn.idsi.md/vizualizare_articol/148992","host_type":""}],"fields_of_study":["Machine Learning in Healthcare","Sepsis Diagnosis and Treatment","Phonocardiography and Auscultation Techniques"],"mesh_terms":[],"keywords":["Computer science","Raw data","Field (mathematics)","Task (project management)","Machine learning","Artificial intelligence","SIGNAL (programming language)","Data science","Decomposition","Data mining","Systems engineering","Engineering"],"sdg_mappings":[{"sdg_number":0,"sdg_label":"Peace, Justice and strong institutions"}],"linked_datasets":[],"clinical_trials":[],"software_tools":[],"database_accessions":[],"source":"live","citation_network_status":"fetched"},"created_at":"2026-08-17T13:50:46.745801Z","pmid":null,"pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}