Dihydrocodeine/Agonists for Alcohol Dependents

Objective: Alcohol addiction too often remains insufficiently treated. It shows the same profile as severe chronic diseases, but no comparable, effective basic treatment has been established up to now. Especially patients with repeated relapses, despite all therapeutic approaches, and patients who are not able to attain an essential abstinence to alcohol, need a basic medication. It seems necessary to acknowledge that parts of them need any agonistic substance, for years, possibly lifelong. For >14 years, we have prescribed such substances with own addictive character for these patients. Methods: We present a documented best possible practice, no designed study. Since 1997, we prescribed Dihydrocodeine (DHC) to 102 heavily alcohol addicted patients, later, also Buprenorphine, Clomethiazole (>6 weeks), Baclofen, and in one case Amphetamine, each on individual indication. This paper focuses on the data with DHC, especially. The Clomethiazole-data has been submitted to a German journal. The number of treatments with the other substances is still low. Results: The 102 patients with the DHC treatment had 1367 medically assisted detoxifications and specialized therapies before! The 4 years-retention rate was 26.4%, including 2.8% successfully terminated treatments. In our 12-steps scale on clinical impression, we noticed a significant improvement from mean 3.7 to 8.4 after 2 years. The demand for medically assisted detoxifications in the 2 years remaining patients was reduced by 65.5%. Mean GGT improved from 206.6 U/l at baseline to 66.8 U/l after 2 years. Experiences with the other substances are similar but different in details. Conclusion: Similar to the Italian studies with GHB and Baclofen, we present a new approach, not only with new substances, but also with a new setting and much more trusting attitude. We observe a huge improvement, reaching an almost optimal, stable, long term status in around 1/4 of the patients already. Many further optimizations are possible

A Generic Machine Learning Framework for Fully-Unsupervised Anomaly Detection with Contaminated Data

Anomaly detection (AD) tasks have been solved using machine learning algorithms in various domains and applications. The great majority of these algorithms use normal data to train a residual-based model, and assign anomaly scores to unseen samples based on their dissimilarity with the learned normal regime. The underlying assumption of these approaches is that anomaly-free data is available for training. This is, however, often not the case in real-world operational settings, where the training data may be contaminated with a certain fraction of abnormal samples. Training with contaminated data, in turn, inevitably leads to a deteriorated AD performance of the residual-based algorithms. In this paper we introduce a framework for a fully unsupervised refinement of contaminated training data for AD tasks. The framework is generic and can be applied to any residual-based machine learning model. We demonstrate the application of the framework to two public datasets of multivariate time series machine data from different application fields. We show its clear superiority over the naive approach of training with contaminated data without refinement. Moreover, we compare it to the ideal, unrealistic reference in which anomaly-free data would be available for training. Since the approach exploits information from the anomalies, and not only from the normal regime, it is comparable and often outperforms the ideal baseline as well

Intellectual and motor performance, quality of life and psychosocial adjustment in children with cystinosis

Cystinosis is a rare multisystemic progressive disorder mandating lifelong medical treatment. Knowledge on the intellectual and motor functioning, health-related quality of life and psychosocial adjustment in children with cystinosis is limited. We have investigated nine patients (four after renal transplantation) at a median age of 9.7years (range 5.3-19.9years). Intellectual performance (IP) was analysed with the Wechsler Intelligence Scale for Children-III (seven children) and the Kaufman Assessment Battery for Children (two children). Motor performance (MP) was evaluated using the Zurich Neuromotor Assessment Test, and quality of life (QOL) was studied by means of the Netherlands Organization for Applied Scientific Research Academical Medical Center Child Quality of Life Questionnaire. Psychosocial adjustment was assessed by the Child Behavior Checklist. The overall intelligence quotient (IQ) of our patient cohort (median 92, range 71-105) was significantly lower than that of the healthy controls (p = 0.04), with two patients having an IQ < 85. Verbal IQ (93, range 76-118) was significantly higher than performance IQ (90, range 68-97; p = 0.03). The MP was significantly below the norm for pure motor, pegboard and static balance, as well as for movement quality. The patients' QOL was normal for six of seven dimensions (exception being positive emotions), whereas parents reported significant impairment in positive emotions, autonomy, social and cognitive functions. Significant disturbance was noted in terms of psychosocial adjustment. Based on the results from our small patient cohort, we conclude that intellectual and motor performance, health-related QOL and psychosocial adjustment are significantly impaired in children and adolescents with cystinosi

A generic machine learning framework for fully-unsupervised anomaly detection with contaminated data

Anomaly detection (AD) tasks have been solved using machine learning algorithms in various domains and applications. The great majority of these algorithms use normal data to train a residual-based model, and assign anomaly scores to unseen samples based on their dissimilarity with the learned normal regime. The underlying assumption of these approaches is that anomaly-free data is available for training. This is, however, often not the case in real-world operational settings, where the training data may be contaminated with a certain fraction of abnormal samples. Training with contaminated data, in turn, inevitably leads to a deteriorated AD performance of the residual-based algorithms. In this paper we introduce a framework for a fully unsupervised refinement of contaminated training data for AD tasks. The framework is generic and can be applied to any residual-based machine learning model. We demonstrate the application of the framework to two public datasets of multivariate time series machine data from different application fields. We show its clear superiority over the naive approach of training with contaminated data without refinement. Moreover, we compare it to the ideal, unrealistic reference in which anomaly-free data would be available for training. Since the approach exploits information from the anomalies, and not only from the normal regime, it is comparable and often outperforms the ideal baseline as well

Cross-turbine training of convolutional neural networks for SCADA-based fault detection in wind turbines

Machine learning algorithms for early fault detection of wind turbines using 10-minute SCADA data are attracting attention in the wind energy community due to their cost-effectiveness. It has been recently shown that convolutional neural networks (CNNs) can significantly improve the performance of such algorithms. One practical aspect in the deployment of these algorithms is that they require a large amount of historical SCADA data for training. These are not always available, for example in the case of newly installed turbines. Here we suggest a cross-turbine training scheme for CNNs: we train a CNN model on a turbine with abundant data and use the trained network to detect faults in a different wind turbine for which only little data are available. We show that this scheme is able to considerably improve the fault detection performance compared to the scarce data training. Moreover, it is shown to detect faults with an accuracy and robustness which are very similar to the single-turbine scheme, in which training and detection are both done on the same turbine with a large and representative training set. We demonstrate this for two different fault types: abrupt and slowly evolving faults and perform a sensitivity analysis in order to compare the performance of the two training schemes. We show that the cross-turbine scheme works successfully also when training on turbines from another farm and with different measured variables than the target turbine