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Prediction of at-risk patients for readmission allows for targeted interventions that reduce morbidity and mortality.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods and results<\/jats:title>\n                    <jats:p>We presented a process mining\/deep learning approach for the prediction of unplanned 30-day readmission of ICU patients with HF. A patient\u2019s health records can be understood as a sequence of observations called event logs; used to discover a process model. Time information was extracted using the DREAM (Decay Replay Mining) algorithm. Demographic information and severity scores upon admission were then combined with the time information and fed to a neural network (NN) model to further enhance the prediction efficiency. Additionally, several machine learning (ML) algorithms were developed to be used as the baseline models for the comparison of the results.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>By using the Medical Information Mart for Intensive Care III (MIMIC-III) dataset of 3411 ICU patients with HF, our proposed model yielded an area under the receiver operating characteristics (AUROC) of 0.930, 95% confidence interval of [0.898\u20130.960], the precision of 0.886, sensitivity of 0.805, accuracy of 0.841, and F-score of 0.800 which were far better than the results of the best baseline model and the existing literature.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>The proposed approach was capable of modeling the time-related variables and incorporating the medical history of patients from prior hospital visits for prediction. Thus, our approach significantly improved the outcome prediction compared to that of other ML-based models and health calculators.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1186\/s12911-022-01857-y","type":"journal-article","created":{"date-parts":[[2022,5,2]],"date-time":"2022-05-02T15:02:43Z","timestamp":1651503763000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":43,"title":["Prediction of unplanned 30-day readmission for ICU patients with heart failure"],"prefix":"10.1186","volume":"22","author":[{"given":"M.","family":"Pishgar","sequence":"first","affiliation":[]},{"given":"J.","family":"Theis","sequence":"additional","affiliation":[]},{"given":"M.","family":"Del Rios","sequence":"additional","affiliation":[]},{"given":"A.","family":"Ardati","sequence":"additional","affiliation":[]},{"given":"H.","family":"Anahideh","sequence":"additional","affiliation":[]},{"given":"H.","family":"Darabi","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,5,2]]},"reference":[{"key":"1857_CR1","first-page":"e38","volume":"133","author":"DBE Mozaffarian","year":"2016","unstructured":"Mozaffarian DBE, Go A, Arnett D, Blaha M, Cushman M, et al. 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This database is a public de-identified database thus informed consent and approval of the Institutional Review Board was waived.\u00a0All methods were performed in accordance with the relevant guidelines and regulations.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The authors declare that they have no competing interests.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"117"}}