Faculty of Sciences, University of Novi Sad, Trg Dositeja Obradovića 4, Novi Sad, Serbia.
Faculty of Computer and Information Science, University of Ljubljana, Večna pot 113, Ljubljana, Slovenia.
J Med Syst. 2018 Oct 27;42(12):243. doi: 10.1007/s10916-018-1071-x.
Machine learning and data mining approaches are being successfully applied to different fields of life sciences for the past 20 years. Medicine is one of the most suitable application domains for these techniques since they help model diagnostic information based on causal and/or statistical data and therefore reveal hidden dependencies between symptoms and illnesses. In this paper we give a detailed overview of the recent machine learning research and its applications for predicting cognitive diseases, especially the Alzheimer's disease, mild cognitive impairment and the Parkinson's disease. We survey different state-of-the-art methodological approaches, data sources and public data, and provide their comparative analysis. We conclude by identifying the open problems within the field that include an early detection of the cognitive diseases and inclusion of machine learning tools into diagnostic practice and therapy planning.
机器学习和数据挖掘方法在过去的 20 年中成功应用于生命科学的不同领域。由于这些技术可以帮助基于因果和/或统计数据来构建诊断信息，从而揭示症状和疾病之间的隐藏关系，因此医学是这些技术最适合的应用领域之一。在本文中，我们详细概述了最近用于预测认知疾病（特别是阿尔茨海默病、轻度认知障碍和帕金森病）的机器学习研究及其应用。我们调查了不同的最新方法、数据源和公共数据，并对其进行了比较分析。最后，我们确定了该领域存在的一些尚未解决的问题，包括认知疾病的早期检测，以及将机器学习工具纳入诊断实践和治疗计划。
