Predicting ACL Injury Using Machine Learning on Data From an Extensive Screening Test Battery of 880 Female Elite Athletes

BACKGROUND

Injury risk prediction is an emerging field in which more research is needed to recognize the best practices for accurate injury risk assessment. Important issues related to predictive machine learning need to be considered, for example, to avoid overinterpreting the observed prediction performance.

PURPOSE

To carefully investigate the predictive potential of multiple predictive machine learning methods on a large set of risk factor data for anterior cruciate ligament (ACL) injury; the proposed approach takes into account the effect of chance and random variations in prediction performance.

STUDY DESIGN

Case-control study; Level of evidence, 3.

METHODS

The authors used 3-dimensional motion analysis and physical data collected from 791 female elite handball and soccer players. Four common classifiers were used to predict ACL injuries (n = 60). Area under the receiver operating characteristic curve (AUC-ROC) averaged across 100 cross-validation runs (mean AUC-ROC) was used as a performance metric. Results were confirmed with repeated permutation tests (paired Wilcoxon signed-rank-test; P < .05). Additionally, the effect of the most common class imbalance handling techniques was evaluated.

RESULTS

For the best classifier (linear support vector machine), the mean AUC-ROC was 0.63. Regardless of the classifier, the results were significantly better than chance, confirming the predictive ability of the data and methods used. AUC-ROC values varied substantially across repetitions and methods (0.51-0.69). Class imbalance handling did not improve the results.

CONCLUSION

The authors' approach and data showed statistically significant predictive ability, indicating that there exists information in this prospective data set that may be valuable for understanding injury causation. However, the predictive ability remained low from the perspective of clinical assessment, suggesting that included variables cannot be used for ACL prediction in practice.

Review on predicting pairwise relationships between human microbes, drugs and diseases: from biological data to computational models

In recent years, with the rapid development of techniques in bioinformatics and life science, a considerable quantity of biomedical data has been accumulated, based on which researchers have developed various computational approaches to discover potential associations between human microbes, drugs and diseases. This paper provides a comprehensive overview of recent advances in prediction of potential correlations between microbes, drugs and diseases from biological data to computational models. Firstly, we introduced the widely used datasets relevant to the identification of potential relationships between microbes, drugs and diseases in detail. And then, we divided a series of a lot of representative computing models into five major categories including network, matrix factorization, matrix completion, regularization and artificial neural network for in-depth discussion and comparison. Finally, we analysed possible challenges and opportunities in this research area, and at the same time we outlined some suggestions for further improvement of predictive performances as well.

Research progress in prediction of postpartum depression

Postpartum depression is a kind of mental disorder caused by the comprehensive effects of economics, psychosociology, biology, obstetrics and other characteristic factors in the process of female pregnancy. The pathogenesis of postpartum depression is complicated and has not been clarified. With the process pregnancy, the influence of psychosocial and biology factors are also in dynamic change. The postpartum depression predictor among psychosocial factors are antenatal depression, life events and social supports. Among biological factors, the predictors are hypothalamic-pituitary-thyroid axis and serum lipids. Timely and effective prediction can identify the high-risk population and risk factors for postpartum depression.

A Machine Learning Approach for Human Activity Recognition

Human Activity Recognition (HAR) is becoming a significant issue in modern times and directly impact the field of mobile health. Therefore, it is essential the designing of systems which are capable of recognizing properly the activities conducted by the individuals. In this work, we developed a system using the Internet of Things (IoT) and machine learning technologies in order to monitor and assist individuals in their daily life. We compared the data collected using a mobile application and a wearable device with built-in sensors (accelerometer and gyroscope) with the data of a publicly available dataset. By this way, we were able to validate our results and also investigate the functionality and applicability of the wearable device that we choose for the Human Activity Recognition problem. The classification results for the different types of activities presented using our dataset (99%) outperforms the results from the publicly database (97%).

Predicting methylphenidate response in attention deficit hyperactivity disorder: a preliminary study

Methylphenidate (MPH) is established as the main pharmacological treatment for patients with attention deficit hyperactivity disorder (ADHD). Whilst MPH is generally a highly effective treatment, not all patients respond, and some experience adverse reactions. Currently, there is no reliable method to predict how patients will respond, other than by exposure to a trial of medication. In this preliminary study, we sought to investigate whether an accurate predictor of clinical response to methylphenidate could be developed for individual patients, using sociodemographic, clinical and neuropsychological measures. Of the 43 boys with ADHD included in this proof-of-concept study, 30 were classed as responders and 13 as non-responders to MPH, with no significant differences in age nor verbal intelligence quotient (IQ) between the groups. Here we report the application of a multivariate analysis approach to the prediction of clinical response to MPH, which achieved an accuracy of 77% (p = 0.005). The most important variables to the classifier were performance on a 'go/no go' task and comorbid conduct disorder. This preliminary study suggested that further investigation is merited. Achieving a highly significant accuracy of 77% for the prediction of MPH response is an encouraging step towards finding a reliable and clinically useful method that could minimise the number of children needlessly being exposed to MPH.