The Comprehensive Machine Learning Analytics for Heart Failure.

Background: Early detection of heart failure is the basis for better medical treatment and prognosis. Over the last decades, both prevalence and incidence rates of heart failure have increased worldwide, resulting in a significant global public health issue. However, an early diagnosis is not an easy task because symptoms of heart failure are usually non-specific. Therefore, this study aims to develop a risk prediction model for incident heart failure through a machine learning-based predictive model. Although African Americans have a higher risk of incident heart failure among all populations, few studies have developed a heart failure risk prediction model for African Americans. Methods: This research implemented the Least Absolute Shrinkage and Selection Operator (LASSO) logistic regression, support vector machine, random forest, and Extreme Gradient Boosting (XGBoost) to establish the Jackson Heart Study's predictive model. In the analysis of real data, missing data are problematic when building a predictive model. Here, we evaluate predictors' inclusion with various missing rates and different missing imputation strategies to discover the optimal analytics. Results: According to hundreds of models that we examined, the best predictive model was the XGBoost that included variables with a missing rate of less than 30 percent, and we imputed missing values by non-parametric random forest imputation. The optimal XGBoost machine demonstrated an Area Under Curve (AUC) of 0.8409 to predict heart failure for the Jackson Heart Study. Conclusion: This research identifies variations of diabetes medication as the most crucial risk factor for heart failure compared to the complete cases approach that failed to discover this phenomenon.

Case-control study on endobutton plate or clavicular hook plate for the repair of acromioclavicular joint dislocations / 中国骨伤

<p><b>OBJECTIVE</b>To compare the treatment results between Endobutton plate and clavicular hook plate for the treatment of acromioclavicular joint dislocations.</p><p><b>METHODS</b>From January 2009 to September 2010, 30 patients with acromioclavicular joint dislocations were treated with two different fixations: Endobutton plate (15 patients, including 12 males and 3 females, with a mean age of 38.5 +/- 8.2 years) and clavicular hook plate (15 patients, including 14 males and 1 female, with the mean age of 33.8 +/- 5.9 years). The mean operative time and blood loss were compared between the two groups. Joint function was assessed by the Karlsson standard.</p><p><b>RESULTS</b>The mean operative time of the Endobutton group and the hook plate group were (61 +/- 8.6) min and (40 +/- 5.6) min. The average blood loss were (93 +/- 8.4) ml and (100 +/- 12.6) ml. There was no statistical difference between the two groups in the average blood loss and the mean operative time. According to the Karlsson standard, 13 patients got an excellent result, 2 good in the Endobutton group; and 7 patients got an excellent result and 7 good in hook plate group. The therapeutic effect in Endobutton group was better than that of hook plate group.</p><p><b>CONCLUSION</b>In the treatment of acromioclavicular dislocation, fixation with Endobutton plate is believed to be better than hook plate fixation. It is an effective method for the repair of acromioclavicular joint dislocations in a short term. But its long term effects still need further follow up.</p>