Big data and machine learning in health
Big data and machine learning in health
复制标题
健康领域的大数据和机器学习
DOI：
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发表时间：
2020
期刊：
影响因子：
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通讯作者：
R. Cruz
中科院分区：
文献类型：
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作者：
D. Carvalho;R. Cruz
简介大数据的定义是，一旦组织和分析，就可以为降低成本、规避风险和优化服务而做出价值、决策、预测和发现模式的数据量。机器学习(ML)是人工智能的一个领域，其特征是一种机器学习方法，它使用从数据分析中学习的算法，允许计算机发现模式、得出结论和做出预测。这些工具可用于人类知识的不同领域，特别是在每天产生大量信息的卫生部门，允许创建学习和获得理解的算法，以帮助各种临床实践。
目的本文旨在分析大数据和机器学习在提供整体医疗保健方面的优势。
方法：应用计算机检索电子数据库PubMed/MEDLINE和Google Scholar中的科学文献，检索词：大数据“、”机器学习“。
结果在肿瘤学(皮肤癌、乳腺癌、肺癌、白血病)领域，ML和大数据有助于不同病理及其演变的早期诊断，以及优化治疗。在眼科(糖尿病视网膜病变和先天性白内障)方面，已显示出快速诊断和适当治疗的高效性，这对防止疾病的进展至关重要。在帕金森氏症和心血管疾病的案例中，测试的算法取得了非常好的结果。在制药行业，这些计算机和数字工具有助于优化临床试验，对肿瘤进行基因组测序，然后识别和开发特定的药物来对抗它。
结论MIL和大数据的进展是臭名昭著的，发展机会是巨大的，可以彻底改变诊断、治疗和医疗保健等任务。
Introduction Big data is defined as the amount of data that once organized and analysed, can make a value, make decisions, make predictions and discover patterns in order to reduce costs, avoid risks and optimize services. Machine Learning (ML) is a field of artificial intelligence and is characterized as a method of machine learning, which uses algorithms that learn from data analysis, allowing computers to find patterns, draw conclusions and make predictions. These tools can be used in different areas of human knowledge, particularly in the health sector which are generated daily a huge amount of information, allowing the creation of algorithms that learn and gain understanding to assist in various clinical practices.
Objectives The purpose of this paper is to analyse the benefits of Big Data and Machine Learning in providing overall health care.
Methodology We conducted a review of the scientific literature published in the electronic databases PubMed/MEDLINE and Google Scholar, according to specific criteria, using keywords: Big Data”, “Machine Learning".
Results In the field of oncology (skin cancer, breast, lung, leukaemia) ML and Big Data have contributed to early diagnosis of different pathologies and their evolution, as well as optimizing therapies. In ophthalmology (diabetic retinopathy and congenital cataract) has shown high efficacy in rapid diagnosis and appropriate treatment crucial to prevent the progress of the disease. The tested algorithms achieved very favourable results in cases of Parkinson’s and cardiovascular diseases. In the pharmaceutical industry these computer and digital tools have contributed to the optimization of clinical trials, genome sequencing of tumours to then identify and develop specific drugs to fight it.
Conclusion Advances of MIL and Big data are notorious and development opportunities are immense and can come to revolutionize tasks such as diagnosis, treatment and health care in general.