Manchester Medical School, The University of Manchester, Manchester, UK
UCL Centre for Artificial Intelligence, University College London, London, UK.
Postgrad Med J. 2022 Jul;98(1161):551-558. doi: 10.1136/postgradmedj-2020-139352. Epub 2021 Mar 10.
Cardiovascular disease (CVD) is one of the leading causes of death across the world. CVD can lead to angina, heart attacks, heart failure, strokes, and eventually, death; among many other serious conditions. The early intervention with those at a higher risk of developing CVD, typically with statin treatment, leads to better health outcomes. For this reason, clinical prediction models (CPMs) have been developed to identify those at a high risk of developing CVD so that treatment can begin at an earlier stage. Currently, CPMs are built around statistical analysis of factors linked to developing CVD, such as body mass index and family history. The emerging field of machine learning (ML) in healthcare, using computer algorithms that learn from a dataset without explicit programming, has the potential to outperform the CPMs available today. ML has already shown exciting progress in the detection of skin malignancies, bone fractures and many other medical conditions. In this review, we will analyse and explain the CPMs currently in use with comparisons to their developing ML counterparts. We have found that although the newest non-ML CPMs are effective, ML-based approaches consistently outperform them. However, improvements to the literature need to be made before ML should be implemented over current CPMs.
心血管疾病（CVD）是全球主要死因之一。CVD 可导致心绞痛、心脏病发作、心力衰竭、中风，最终导致死亡；还有许多其他严重疾病。对那些患 CVD 风险较高的人进行早期干预，通常采用他汀类药物治疗，可以改善健康结果。出于这个原因，已经开发出临床预测模型（CPM）来识别那些患 CVD 风险较高的人，以便可以在早期开始治疗。目前，CPM 是围绕与 CVD 发展相关的因素的统计分析构建的，例如体重指数和家族史。医疗保健领域的机器学习（ML）新兴领域使用从没有显式编程的数据集中学习的计算机算法，有可能超越目前可用的 CPM。ML 已经在皮肤恶性肿瘤、骨折和许多其他疾病的检测方面取得了令人兴奋的进展。在这篇综述中，我们将分析和解释当前使用的 CPM，并将其与正在开发的 ML 对应物进行比较。我们发现，尽管最新的非 ML CPM 有效，但基于 ML 的方法始终优于它们。然而，在实施 ML 替代当前 CPM 之前，需要对文献进行改进。
