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Unplanned hospital readmissions soon after a person is discharged indicate the poor performance of the healthcare. Previous attempts of readmission prediction pose it as a binary classification problem and largely ignore the previous history. This study proposes a novel neural network architecture called Sequential Readmission Predictor with Multitasking (SRPM), to enhance the existing readmission prediction models. We retain the previous admission history of a patient by learning a latent representation for the patient, which could be used for every new admission by the same patient. Our proposed model uses a multitask neural network model that simultaneously models it as a binary classification problem and as a regression problem that predicts the exact days of readmission. By doing so, the error information from the regression task augments the classification task. The results show a promising improvement of up to 6.59% in AUCROC and 19% in F1 score over four benchmark methods.



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