  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.
