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Synthesizing time-series wound prognosis factors from electronic medical records using generative adversarial networks
arXiv - CS - Artificial Intelligence Pub Date : 2021-05-03 , DOI: arxiv-2105.01159
Farnaz H. Foomani, D. M. Anisuzzaman, Jeffrey Niezgoda, Jonathan Niezgoda, William Guns, Sandeep Gopalakrishnan, Zeyun Yu

Wound prognostic models not only provide an estimate of wound healing time to motivate patients to follow up their treatments but also can help clinicians to decide whether to use a standard care or adjuvant therapies and to assist them with designing clinical trials. However, collecting prognosis factors from Electronic Medical Records (EMR) of patients is challenging due to privacy, sensitivity, and confidentiality. In this study, we developed time series medical generative adversarial networks (GANs) to generate synthetic wound prognosis factors using very limited information collected during routine care in a specialized wound care facility. The generated prognosis variables are used in developing a predictive model for chronic wound healing trajectory. Our novel medical GAN can produce both continuous and categorical features from EMR. Moreover, we applied temporal information to our model by considering data collected from the weekly follow-ups of patients. Conditional training strategies were utilized to enhance training and generate classified data in terms of healing or non-healing. The ability of the proposed model to generate realistic EMR data was evaluated by TSTR (test on the synthetic, train on the real), discriminative accuracy, and visualization. We utilized samples generated by our proposed GAN in training a prognosis model to demonstrate its real-life application. Using the generated samples in training predictive models improved the classification accuracy by 6.66-10.01% compared to the previous EMR-GAN. Additionally, the suggested prognosis classifier has achieved the area under the curve (AUC) of 0.975, 0.968, and 0.849 when training the network using data from the first three visits, first two visits, and first visit, respectively. These results indicate a significant improvement in wound healing prediction compared to the previous prognosis models.

中文翻译:

使用生成性对抗网络从电子病历中综合按时间顺序排列的伤口预后因素

伤口预后模型不仅可以估计伤口的愈合时间,以激励患者继续治疗,还可以帮助临床医生决定是否使用标准护理或辅助疗法,并协助他们设计临床试验。然而,由于隐私,敏感性和机密性,从患者的电子病历(EMR)收集预后因素具有挑战性。在这项研究中,我们开发了时间序列医学生成对抗网络(GAN),使用在专门的伤口护理机构进行常规护理期间收集到的非常有限的信息来生成综合性伤口预后因素。所产生的预后变量用于建立慢性伤口愈合轨迹的预测模型。我们新颖的医用GAN可以通过EMR产生连续的和分类的特征。此外,我们通过考虑从患者每周随访中收集的数据,将时间信息应用于我们的模型。有条件的训练策略被用于增强训练并根据愈合或不愈合产生分类数据。通过TSTR(对合成进行测试,对真实进行训练),判别准确性和可视化来评估所提出模型生成实际EMR数据的能力。我们利用建议的GAN生成的样本来训练预后模型,以证明其实际应用。与先前的EMR-GAN相比,在训练预测模型中使用生成的样本可使分类准确性提高6.66-10.01%。此外,建议的预后分类器的曲线下面积(AUC)为0.975、0.968和0。849,分别使用前三次访问,前两次访问和第一次访问的数据训练网络时。这些结果表明与以前的预后模型相比,伤口愈合的预测有显着改善。
更新日期:2021-05-05
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