从电子病历中加速结果驱动的风险因素识别的稳健框架

Prithwish Chakraborty, Faisal Farooq
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引用次数: 8

摘要

包含数百万患者生命的纵向信息的电子健康记录(EHR)正越来越多地被医疗保健领域的组织所利用。对电子病历数据的研究使现实世界的应用成为可能,如了解疾病进展、结果分析和比较有效性研究。然而,通常每项研究都是独立委托的,数据是通过调查收集的,或者是经过一个漫长而痛苦的过程专门购买的。接下来是一个艰巨的分析、模型构建和见解生成的重复循环。这个过程可能需要1 - 3年。在本文中,我们提出了一个强大的端到端基于机器学习的SaaS系统,用于对非常大的EHR数据集进行分析。该框架包括一个专有的EHR数据集,涵盖美国约5500万患者的生活和超过200亿个数据点。据我们所知,这个框架是业界分析这种规模医疗记录的最大框架,具有如此高的效率和易用性。我们开发了一个端到端的机器学习框架,其中包含精心选择的组件,以支持大规模的电子病历分析,并适合进一步的下游临床分析。具体来说,它由具有临床核的脊化生存支持向量机(SSVM)和基于卡方距离的特征选择组成,通过利用EHR中的弱相关性来发现相关的危险因素。我们在多个实际用例上的结果表明,该框架在没有专家监督的情况下有效地识别了相关因素。该框架是稳定的,可对结果进行概括,并且还有助于对已知专家特征进行更好的边界预测。重要的是,所使用的机器学习方法是可解释的,这对于目标用户群接受我们的系统至关重要。随着系统投入使用,与行业标准的12-36个月相比,所有这些研究都在3-4周内完成。因此,我们的系统可以加速分析和发现,由于减少投资和更快的研究周转,从而获得更好的投资回报率。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Robust Framework for Accelerated Outcome-driven Risk Factor Identification from EHR
Electronic Health Records (EHR) containing longitudinal information about millions of patient lives are increasingly being utilized by organizations across the healthcare spectrum. Studies on EHR data have enabled real world applications like understanding of disease progression, outcomes analysis, and comparative effectiveness research. However, often every study is independently commissioned, data is gathered by surveys or specifically purchased per study by a long and often painful process. This is followed by an arduous repetitive cycle of analysis, model building, and generation of insights. This process can take anywhere between 1 - 3 years. In this paper, we present a robust end-to-end machine learning based SaaS system to perform analysis on a very large EHR dataset. The framework consists of a proprietary EHR datamart spanning ~55 million patient lives in USA and over ~20 billion data points. To the best of our knowledge, this framework is the largest in the industry to analyze medical records at this scale, with such efficacy and ease. We developed an end-to-end ML framework with carefully chosen components to support EHR analysis at scale and suitable for further downstream clinical analysis. Specifically, it consists of a ridge regularized Survival Support Vector Machine (SSVM) with a clinical kernel, coupled with Chi-square distance-based feature selection, to uncover relevant risk factors by exploiting the weak correlations in EHR. Our results on multiple real use cases indicate that the framework identifies relevant factors effectively without expert supervision. The framework is stable, generalizable over outcomes, and also found to contribute to better out-of-bound prediction over known expert features. Importantly, the ML methodologies used are interpretable which is critical for acceptance of our system in the targeted user base. With the system being operational, all of these studies were completed within a time frame of 3-4 weeks compared to the industry standard 12-36 months. As such our system can accelerate analysis and discovery, result in better ROI due to reduced investments as well as quicker turn around of studies.
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