Use of Data Mining Techniques to Predict Short Term Adverse Events Occurrence in NB-UVB Phototherapy Treatments

The prediction of short term adverse events occurrence in phototherapy treatment is important for the dermatologists who administrate phototherapy to adjust the treatment and standardize the clinical outcomes. Recently, a modeling technique which can detect the potential short term adverse events occurrence in phototherapy treatments is required for clinicians. Based on data mining, this study tends to explore the significant features and the class distribution of training data for the short term adverse events occurrence prediction in NB-UVB phototherapy treatments. The experimental results highlight that acceptable prediction accuracy can be achieved by using the significant features and the performance of the classifiers can be significantly improved by sampling 40% of negative class samples in training data, hyper parameter tuning of classifiers and use of stacked classifiers in creating prediction models.Science Foundation IrelandInsight Research CentreJournal site: http://www.ijmlc.org

Prediction of NB-UVB phototherapy treatment response of psoriasis patients using data mining

IEEE International Conference on Bioinformatics and Biomedicine, (BIBM-BHI 2017), Kansas, MO, USA, November 13-16, 2017NB-UVB Phototherapy is one of the most commontreatments administrated by dermatologists for psoriasis patients.Although in general, the treatment results in improving thecondition, it also can worsen it. If a model can predict thetreatment response before hand, the dermatologists can adjustthe treatment accordingly. In this paper, we use data miningtechniques and conduct four experiments. The best performanceof all four experiments was obtained by the stacked classifiermade of hyper parameter tuned Random Forest, kSVM and ANNbase learners, learned using L1-Regularized Logistic Regressionsuper learner.Science Foundation IrelandInsight Research Centre2017-12-14 JG: NB this is not the same paper as this: https://doi.org/10.1109/ICKEA.2017.816990

The Limb Movement Analysis of Rehabilitation Exercises using Wearable Inertial Sensors

38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Florida, United States of America, 16-20 August 2016Due to no supervision of a therapist in home based exercise programs, inertial sensor based feedback systems which can accurately assess movement repetitions are urgently required. The synchronicity and the degrees of freedom both show that one movement might resemble another movement signal which is mixed in with another not precisely defined movement. Therefore, the data and feature selections are important for movement analysis. This paper explores the data and feature selection for the limb movement analysis of rehabilitation exercises. The results highlight that the classification accuracy is very sensitive to the mount location of the sensors. The results show that the use of 2 or 3 sensor units, the combination of acceleration and gyroscope data, and the feature sets combined by the statistical feature set with another type of feature, can significantly improve the classification accuracy rates. The results illustrate that acceleration data is more effective than gyroscope data for most of the movement analysis

Automatic Classification of Knee Rehabilitation Exercises Using a Single Inertial Sensor: a Case Study

IEEE 15th International Conference on Wearable and Implantable Body Sensor Networks (BSN), 4-7 March 2018, Las Vegas, Nevada, USAInertial measurement units have the ability to accurately record the acceleration and angular velocity of human limb segments during discrete joint movements. These movements are commonly used in exercise rehabilitation programmes following orthopaedic surgery such as total knee replacement. This provides the potential for a biofeedback system with data mining technique for patients undertaking exercises at home without physician supervision. We propose to use machine learning techniques to automatically analyse inertial measurement unit data collected during these exercises, and then assess whether each repetition of the exercise was executed correctly or not. Our approach consists of two main phases: signal segmentation, and segment classification. Accurate pre-processing and feature extraction are paramount topics in order for the technique to work. In this paper, we present a classification method for unsupervised rehabilitation exercises, based on a segmentation process that extracts repetitions from a longer signal activity. The results obtained from experimental datasets of both clinical and healthy subjects, for a set of 4 knee exercises commonly used in rehabilitation, are very promising.Science Foundation Irelan

Leveraging IMU Data for Accurate Exercise Performance Classification and Musculoskeletal Injury Risk Screening

38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Florida, United States of America, 17-20 August 2016Inertial measurement units (IMUs) are becoming increasingly prevalent as a method for low cost and portable biomechanical analysis. However, to date they have not tended to be accepted into routine clinical practice. This is often due to the disconnect between translating the data collected by the sensors into meaningful and actionable information for end users. This paper outlines the work completed by our group in attempting to achieve this. We discuss the conceptual framework involved in our work, the methodological approach taken in analysing sensor signals and discuss possible application models. The work completed by our group indicates that IMU based systems have the potential to bridge the gap between laboratory and clinical movement analysis. Future work will focus on collecting a diverse range of movement data and using more sophisticated data analysis techniques to refine systems.Science Foundation Irelan