Alanazi Hamdan O, Abdullah Abdul Hanan, Qureshi Kashif Naseer
Faculty of Computing, Universiti Teknologi Malaysia, Johor Bahru, Malaysia.
Department of Medical Science Technology, Faculty of Applied Medical Science, Majmaah University, Al Majmaah, Kingdom of Saudi Arabia.
J Med Syst. 2017 Apr;41(4):69. doi: 10.1007/s10916-017-0715-6. Epub 2017 Mar 11.
Recently, Artificial Intelligence (AI) has been used widely in medicine and health care sector. In machine learning, the classification or prediction is a major field of AI. Today, the study of existing predictive models based on machine learning methods is extremely active. Doctors need accurate predictions for the outcomes of their patients' diseases. In addition, for accurate predictions, timing is another significant factor that influences treatment decisions. In this paper, existing predictive models in medicine and health care have critically reviewed. Furthermore, the most famous machine learning methods have explained, and the confusion between a statistical approach and machine learning has clarified. A review of related literature reveals that the predictions of existing predictive models differ even when the same dataset is used. Therefore, existing predictive models are essential, and current methods must be improved.
最近，人工智能（AI）已在医学和医疗保健领域得到广泛应用。在机器学习中，分类或预测是人工智能的一个主要领域。如今，基于机器学习方法的现有预测模型的研究极为活跃。医生需要对患者疾病的预后进行准确预测。此外，为了进行准确预测，时机是影响治疗决策的另一个重要因素。在本文中，对医学和医疗保健领域的现有预测模型进行了批判性回顾。此外，还解释了最著名的机器学习方法，并澄清了统计方法与机器学习之间的混淆。对相关文献的综述表明，即使使用相同的数据集，现有预测模型的预测结果也存在差异。因此，现有预测模型至关重要，当前的方法必须加以改进。
