
No statistically significant difference (difference 0.03, 95% confidence interval −0.0 to 0.07) was found between average C statistics of models developed using regression methods (0.71, 0.68 to 0.73) and machine learning (0.74, 0.71 to 0.77). Twenty six studies used logistic or Cox regression models, and the rest used machine learning methods. Using natural language processing, three models were able to extract relevant psychosocial features, which substantially improved their predictions. Using EMR data enabled final predictive models to use a wide variety of clinical measures such as laboratory results and vital signs however, use of socioeconomic features or functional status was rare. Fifteen models used calibration techniques to further refine the model. Seventeen of 41 studies reported C statistics of 0.75 or greater. Twenty five models used a split sample validation technique. The total sample size for each model ranged between 349 and 1 195 640. Except for two studies from the UK and Israel, all were from the US. Seventeen models predicted risk of readmission for all patients and 24 developed predictions for patient specific populations, with 13 of those being developed for patients with heart conditions. Results Of 4442 citations reviewed, 41 studies met the inclusion criteria.



Akbar K Waljee, associate professor, co-director, staff physician and researcher 8 9 11.Karandeep Singh, assistant professor 7 10,.

