Objectives: Heart failure (HF) is a common disease with a high hospital readmission rate. This study considered class imbalance and missing data, which are two common issues in medical data. The current study’s main goal was to compare the performance of six machine learning (ML) methods for predicting hospital readmission in HF patients.Methods: In this retrospective cohort study, information of 1,856 HF patients was analyzed. These patients were hospitalized in Farshchian Heart Center in Hamadan Province in Western Iran, from October 2015 to July 2019. The support vector machine (SVM), least-square SVM (LS-SVM), bagging, random forest (RF), AdaBoost, and naïve Bayes (NB) methods were used to predict hospital readmission. These methods’ performance was evaluated using sensitivity, specificity, positive predictive value, negative predictive value, and accuracy. Two imputation methods were also used to deal with missing data.Results: Of the 1,856 HF patients, 29.9% had at least one hospital readmission. Among the ML methods, LS-SVM performed the worst, with accuracy in the range of 0.57–0.60, while RF performed the best, with the highest accuracy (range, 0.90–0.91). Other ML methods showed relatively good performance, with accuracy exceeding 0.84 in the test datasets. Furthermore, the performance of the SVM and LS-SVM methods in terms of accuracy was higher with the multiple imputation method than with the median imputation method.Conclusions: This study showed that RF performed better, in terms of accuracy, than other methods for predicting hospital readmission in HF patients.
Gingival fibroblasts have critical roles in oral wound healing. Photobiomodulation (PBM) has been shown to promote mucosal healing and is now recommended for managing oncotherapy‐associated oral mucositis. This study examined the effects of the emission mode of a 940 nm diode laser on the viability and migration of human gingival fibroblasts. Cells were cultured in a routine growth media and treated with PBM (average power 0.1 W cm−2, average fluence 3 J cm−2, every 12 h for six sessions) in one continuous wave and two pulsing settings with 20% and 50% duty cycles. Cell viability was assessed using MTT, and digital imaging quantified cell migration. After 48 and 72 h, all treatment groups had significantly higher viability (n = 6, P < 0.05) compared with the control. The highest viability was seen in the pulsed (20% duty cycle) group at the 72‐h time point. PBM improved fibroblast migration in all PBM‐treated groups, but differences were not statistically significant (n = 2, P > 0.05). PBM treatments can promote cell viability in both continuous and pulsed modes. Further studies are needed to elucidate the optimal setting for PBM‐evoked responses for its rationalized use in promoting specific phases of oral wound healing.
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