Synthesizing time-series wound prognosis factors from electronic medical records using generative adversarial networks

• Published in: Journal of biomedical informatics Vol. 125; p. 103972
• Main Authors: Foomani, Farnaz H., Anisuzzaman, D.M., Niezgoda, Jeffrey, Niezgoda, Jonathan, Guns, William, Gopalakrishnan, Sandeep, Yu, Zeyun
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 01-01-2022
• Subjects: Confidentiality; Electronic Health Records; Electronic medical record; Generative adversarial network; Humans; Privacy; Prognosis; Time Factors; Wound prognosis model; PU; EMR-TCWGAN; EMR; AUC; DFU; AU; RF; GAN; Generative adversarial network; Wound prognosis model; VLU; Prog-CNN; Electronic medical record
• ISSN: 1532-0464; 1532-0480
• DOI: 10.1016/j.jbi.2021.103972

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•Collecting wound prognosis factors from Electronic Medical Records of patients is time consuming and challenging.•Time-series generative adversarial networks can be used to generate synthetic wound prognosis factors.•Generated samples can be used in training a prognosis model to provide an estimate of wound healing status.•Data from three weeks of follow-up contains enough information to provide a strong prediction of healing potentials.
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 0.875, 0.810, and 0.647 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.