Cahan Eli M, Hernandez-Boussard Tina, Thadaney-Israni Sonoo, Rubin Daniel L
1New York University School of Medicine, New York, NY USA.
2Department of Pediatric Orthopaedics, Stanford University, Palo Alto, CA USA.
NPJ Digit Med. 2019 Aug 19;2:78. doi: 10.1038/s41746-019-0157-2. eCollection 2019.
Technologies leveraging big data, including predictive algorithms and machine learning, are playing an increasingly important role in the delivery of healthcare. However, evidence indicates that such algorithms have the potential to worsen disparities currently intrinsic to the contemporary healthcare system, including racial biases. Blame for these deficiencies has often been placed on the algorithm-but the underlying training data bears greater responsibility for these errors, as biased outputs are inexorably produced by biased inputs. The utility, equity, and generalizability of predictive models depend on population-representative training data with robust feature sets. So while the conventional paradigm of big data is deductive in nature-clinical decision support-a future model harnesses the potential of big data for inductive reasoning. This may be conceptualized as clinical decision questioning, intended to liberate the human predictive process from preconceived lenses in data solicitation and/or interpretation. Efficacy, representativeness and generalizability are all heightened in this schema. Thus, the possible risks of biased big data arising from the inputs themselves must be acknowledged and addressed. Awareness of data deficiencies, structures for data inclusiveness, strategies for data sanitation, and mechanisms for data correction can help realize the potential of big data for a personalized medicine era. Applied deliberately, these considerations could help mitigate risks of perpetuation of health inequity amidst widespread adoption of novel applications of big data.
利用大数据的技术，包括预测算法和机器学习，在医疗保健服务中发挥着越来越重要的作用。然而，有证据表明，此类算法有可能加剧当代医疗系统目前固有的不平等现象，包括种族偏见。这些缺陷往往被归咎于算法，但基础训练数据对这些错误负有更大责任，因为有偏差的输入必然会产生有偏差的输出。预测模型的效用、公平性和通用性取决于具有强大特征集的具有人群代表性的训练数据。因此，虽然大数据的传统范式本质上是演绎性的——临床决策支持——但未来的模型将利用大数据进行归纳推理的潜力。这可以被概念化为临床决策质疑，旨在使人类预测过程在数据收集和/或解释中摆脱先入为主的观念。在这种模式下，有效性、代表性和通用性都得到了提高。因此，必须认识并解决由输入本身产生的有偏差大数据的潜在风险。对数据缺陷的认识、数据包容性的结构、数据清理的策略以及数据校正的机制，有助于实现大数据在个性化医疗时代的潜力。谨慎应用这些考量因素，有助于在广泛采用大数据新应用的过程中，降低健康不平等持续存在的风险。
