Abstract
Adverse drug reactions considerably impact patient outcomes and healthcare costs in cancer therapy. Using artificial intelligence to predict adverse drug reactions in real time could revolutionize oncology treatment. This study aims to assess the performance of artificial intelligence models in predicting adverse drug reactions in patients with cancer. This is the first systematic review and meta-analysis. Scopus, PubMed, IEEE Xplore, and ACM Digital Library databases were searched for studies in English, French, and Arabic from January 1, 2018, to August 20, 2023. The inclusion criteria were: (1) peer-reviewed research articles; (2) use of artificial intelligence algorithms (machine learning, deep learning, knowledge graphs); (3) study aimed to predict adverse drug reactions (cardiotoxicity, neutropenia, nephrotoxicity, hepatotoxicity); (4) study was on cancer patients. The data were extracted and evaluated by three reviewers for study quality. Of the 332 screened articles, 17 studies (5%) involving 93,248 oncology patients from 17 countries were included in the systematic review, of which ten studies synthesized the meta-analysis. A random-effects model was created to pool the sensitivity, specificity, and AUC of the included studies. The pooled results were 0.82 (95% CI:0.69, 0.9), 0.84 (95% CI:0.75, 0.9), and 0.83 (95% CI:0.77, 0.87) for sensitivity, specificity, and AUC, respectively, of ADR predictive models. Biomarkers proved their effectiveness in predicting ADRs, yet they were adopted by only half of the reviewed studies. The use of AI in cancer treatment shows great potential, with models demonstrating high specificity and sensitivity in predicting ADRs. However, standardized research and multicenter studies are needed to improve the quality of evidence. AI can enhance cancer patient care by bridging the gap between data-driven insights and clinical expertise.
Abstract (translated)
翻译：不良药物反应对癌症治疗的患者结局和医疗费用产生严重影响。利用人工智能在实时预测患者癌症中的不良反应可能彻底颠覆癌症治疗。这项研究旨在评估人工智能模型预测癌症患者中不良反应的性能。这是第一篇系统综述和meta分析。在2018年1月至2023年8月期间，用英语、法语和阿拉伯语从PubMed、IEEE Xplore和ACM Digital Library数据库中搜索研究。纳入标准包括： （1）同行评审的研究文章； （2）应用人工智能算法（机器学习、深度学习、知识图谱）； （3）旨在预测不良反应（心血管毒性、中性粒减少、肾毒性、肝脏毒性）； （4）研究对象为癌症患者。 数据由三位审稿人评估研究质量。在332篇筛选出的文章中，有17篇（5％）研究（93,248名癌症患者来自17个国家的）纳入系统综述，其中10篇研究进行了元分析。使用随机效应模型对纳入研究的敏感性、特异性、AUC进行了加权平均。加权平均结果分别为： - 敏感性：0.82（95％CI：0.69，0.9）； - 特异性：0.84（95％CI：0.75，0.9）； - AUC：0.83（95％CI：0.77，0.87）。 生物标志物在预测ADR方面证明了自己的有效性，然而只有半数被回顾的研究采用了这些生物标志物。人工智能在癌症治疗中显示出巨大的潜力，模型在预测ADR方面的特异性和敏感性均很高。然而，需要标准化研究和多中心研究来提高证据的质量。人工智能可以通过缩小数据驱动见解和临床专业知识之间的差距来提高癌症患者的护理。
URL
https://arxiv.org/abs/2404.05762
