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胰腺癌细胞数据的高级数据处理，通过整合本体论和机器学习技术，创建整体健康记录。

Advanced Data Processing of Pancreatic Cancer Data Integrating Ontologies and Machine Learning Techniques to Create Holistic Health Records.

机构信息

Department of Digital Systems, University of Piraeus, 18534 Piraeus, Greece.

Facultad de Informática, Universidad Politécnica de Madrid, 28040 Madrid, Spain.

出版信息

Sensors (Basel). 2024 Mar 7;24(6):1739. doi: 10.3390/s24061739.

DOI:10.3390/s24061739
PMID:38544003
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10974291/
Abstract

The modern healthcare landscape is overwhelmed by data derived from heterogeneous IoT data sources and Electronic Health Record (EHR) systems. Based on the advancements in data science and Machine Learning (ML), an improved ability to integrate and process the so-called primary and secondary data fosters the provision of real-time and personalized decisions. In that direction, an innovative mechanism for processing and integrating health-related data is introduced in this article. It describes the details of the mechanism and its internal subcomponents and workflows, together with the results from its utilization, validation, and evaluation in a real-world scenario. It also highlights the potential derived from the integration of primary and secondary data into Holistic Health Records (HHRs) and from the utilization of advanced ML-based and Semantic Web techniques to improve the quality, reliability, and interoperability of the examined data. The viability of this approach is evaluated through heterogeneous healthcare datasets pertaining to personalized risk identification and monitoring related to pancreatic cancer. The key outcomes and innovations of this mechanism are the introduction of the HHRs, which facilitate the capturing of all health determinants in a harmonized way, and a holistic data ingestion mechanism for advanced data processing and analysis.

摘要

现代医疗保健领域被来自异构物联网数据源和电子健康记录 (EHR) 系统的数据所淹没。基于数据科学和机器学习 (ML) 的进步，能够更好地整合和处理所谓的主要和次要数据，从而促进实时和个性化决策的提供。在这方面，本文引入了一种用于处理和整合与健康相关数据的创新机制。它描述了该机制及其内部子组件和工作流程的细节，以及在实际场景中利用、验证和评估该机制的结果。它还强调了将主要和次要数据集成到整体健康记录 (HHR) 中以及利用基于先进机器学习和语义 Web 的技术来提高所检查数据的质量、可靠性和互操作性所带来的潜力。通过与个人化风险识别和监测胰腺癌相关的异质医疗保健数据集来评估这种方法的可行性。该机制的关键成果和创新是引入 HHR，以协调一致的方式捕获所有健康决定因素，以及用于高级数据处理和分析的整体数据摄取机制。

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