[Psychosomatic health inventory: Basic psychosomatic documentation for quality assurance inpatient care]. / Gesundheitsinventar Psychosomatik: Psychosomatische Basisdokumentation zur Qualitätssicherung im stationären Alltag.

Objectives: As part of the quality assurance of inpatient treatment, the severity of the disease and the course of therapy must be mapped. However, there is a high degree of heterogeneity in the implementation of basic diagnostics in psychosomatic facilities.There is a lack of scientifically based standardisation in determining the quality of outcomes. Methods: With the help of scientifically established test instruments, a resource-saving basic documentation instrument was developed. Many existing psychometric instruments were checked for test quality, costs and computer-supported application. Results: The Psychosomatic Health Inventory (gi-ps) consists of three basic modules with a total of 63 items: sociodemography, screening and psychosomatic health status.The latter is represented bymeans of construct-based recording on eight scales. Its collection at admission and discharge allows the presentation of the quality of outcomes.The development of a proprietary software solution with LimeSurvey enables the computer-based collection, evaluation, and storage of data. A list of test inventories for confirming diagnoses and predictors has been compiled, which are recommended for use in clinical routine. Discussion: With the gi-ps, a modular basic documentation instrument including the software solution is available to all interested institutions free of charge.

Quantum Computing Approaches for Vector Quantization-Current Perspectives and Developments.

In the field of machine learning, vector quantization is a category of low-complexity approaches that are nonetheless powerful for data representation and clustering or classification tasks. Vector quantization is based on the idea of representing a data or a class distribution using a small set of prototypes, and hence, it belongs to interpretable models in machine learning. Further, the low complexity of vector quantizers makes them interesting for the application of quantum concepts for their implementation. This is especially true for current and upcoming generations of quantum devices, which only allow the execution of simple and restricted algorithms. Motivated by different adaptation and optimization paradigms for vector quantizers, we provide an overview of respective existing quantum algorithms and routines to realize vector quantization concepts, maybe only partially, on quantum devices. Thus, the reader can infer the current state-of-the-art when considering quantum computing approaches for vector quantization.

Virxicon: a lexicon of viral sequences.

MOTIVATION: Viruses are the most abundant biological entities and constitute a large reservoir of genetic diversity. In recent years, knowledge about them has increased significantly as a result of dynamic development in life sciences and rapid technological progress. This knowledge is scattered across various data repositories, making a comprehensive analysis of viral data difficult. RESULTS: In response to the need for gathering a comprehensive knowledge of viruses and viral sequences, we developed Virxicon, a lexicon of all experimentally acquired sequences for RNA and DNA viruses. The ability to quickly obtain data for entire viral groups, searching sequences by levels of taxonomic hierarchy-according to the Baltimore classification and ICTV taxonomy-and tracking the distribution of viral data and its growth over time are unique features of our database compared to the other tools. AVAILABILITYAND IMPLEMENTATION: Virxicon is a publicly available resource, updated weekly. It has an intuitive web interface and can be freely accessed at http://virxicon.cs.put.poznan.pl/.

AI-Based Multi Sensor Fusion for Smart Decision Making: A Bi-Functional System for Single Sensor Evaluation in a Classification Task.

Sensor fusion has gained a great deal of attention in recent years. It is used as an application tool in many different fields, especially the semiconductor, automotive, and medical industries. However, this field of research, regardless of the field of application, still presents different challenges concerning the choice of the sensors to be combined and the fusion architecture to be developed. To decrease application costs and engineering efforts, it is very important to analyze the sensors' data beforehand once the application target is defined. This pre-analysis is a basic step to establish a working environment with fewer misclassification cases and high safety. One promising approach to do so is to analyze the system using deep neural networks. The disadvantages of this approach are mainly the required huge storage capacity, the big training effort, and that these networks are difficult to interpret. In this paper, we focus on developing a smart and interpretable bi-functional artificial intelligence (AI) system, which has to discriminate the combined data regarding predefined classes. Furthermore, the system can evaluate the single source signals used in the classification task. The evaluation here covers each sensor contribution and robustness. More precisely, we train a smart and interpretable prototype-based neural network, which learns automatically to weight the influence of the sensors for the classification decision. Moreover, the prototype-based classifier is equipped with a reject option to measure classification certainty. To validate our approach's efficiency, we refer to different industrial sensor fusion applications.

The Resolved Mutual Information Function as a Structural Fingerprint of Biomolecular Sequences for Interpretable Machine Learning Classifiers.

In the present article we propose the application of variants of the mutual information function as characteristic fingerprints of biomolecular sequences for classification analysis. In particular, we consider the resolved mutual information functions based on Shannon-, Rényi-, and Tsallis-entropy. In combination with interpretable machine learning classifier models based on generalized learning vector quantization, a powerful methodology for sequence classification is achieved which allows substantial knowledge extraction in addition to the high classification ability due to the model-inherent robustness. Any potential (slightly) inferior performance of the used classifier is compensated by the additional knowledge provided by interpretable models. This knowledge may assist the user in the analysis and understanding of the used data and considered task. After theoretical justification of the concepts, we demonstrate the approach for various example data sets covering different areas in biomolecular sequence analysis.

Subspace corrected relevance learning with application in neuroimaging.

In machine learning, data often comes from different sources, but combining them can introduce extraneous variation that affects both generalization and interpretability. For example, we investigate the classification of neurodegenerative diseases using FDG-PET data collected from multiple neuroimaging centers. However, data collected at different centers introduces unwanted variation due to differences in scanners, scanning protocols, and processing methods. To address this issue, we propose a two-step approach to limit the influence of center-dependent variation on the classification of healthy controls and early vs. late-stage Parkinson's disease patients. First, we train a Generalized Matrix Learning Vector Quantization (GMLVQ) model on healthy control data to identify a "relevance space" that distinguishes between centers. Second, we use this space to construct a correction matrix that restricts a second GMLVQ system's training on the diagnostic problem. We evaluate the effectiveness of this approach on the real-world multi-center datasets and simulated artificial dataset. Our results demonstrate that the approach produces machine learning systems with reduced bias - being more specific due to eliminating information related to center differences during the training process - and more informative relevance profiles that can be interpreted by medical experts. This method can be adapted to similar problems outside the neuroimaging domain, as long as an appropriate "relevance space" can be identified to construct the correction matrix.

Alignment-Free Sequence Comparison: A Systematic Survey From a Machine Learning Perspective.

The encounter of large amounts of biological sequence data generated during the last decades and the algorithmic and hardware improvements have offered the possibility to apply machine learning techniques in bioinformatics. While the machine learning community is aware of the necessity to rigorously distinguish data transformation from data comparison and adopt reasonable combinations thereof, this awareness is often lacking in the field of comparative sequence analysis. With realization of the disadvantages of alignments for sequence comparison, some typical applications use more and more so-called alignment-free approaches. In light of this development, we present a conceptual framework for alignment-free sequence comparison, which highlights the delineation of: 1) the sequence data transformation comprising of adequate mathematical sequence coding and feature generation, from 2) the subsequent (dis-)similarity evaluation of the transformed data by means of problem-specific but mathematically consistent proximity measures. We consider coding to be an information-loss free data transformation in order to get an appropriate representation, whereas feature generation is inevitably information-lossy with the intention to extract just the task-relevant information. This distinction sheds light on the plethora of methods available and assists in identifying suitable methods in machine learning and data analysis to compare the sequences under these premises.

Learning vector quantization as an interpretable classifier for the detection of SARS-CoV-2 types based on their RNA sequences.

We present an approach to discriminate SARS-CoV-2 virus types based on their RNA sequence descriptions avoiding a sequence alignment. For that purpose, sequences are preprocessed by feature extraction and the resulting feature vectors are analyzed by prototype-based classification to remain interpretable. In particular, we propose to use variants of learning vector quantization (LVQ) based on dissimilarity measures for RNA sequence data. The respective matrix LVQ provides additional knowledge about the classification decisions like discriminant feature correlations and, additionally, can be equipped with easy to realize reject options for uncertain data. Those options provide self-controlled evidence, i.e., the model refuses to make a classification decision if the model evidence for the presented data is not sufficient. This model is first trained using a GISAID dataset with given virus types detected according to the molecular differences in coronavirus populations by phylogenetic tree clustering. In a second step, we apply the trained model to another but unlabeled SARS-CoV-2 virus dataset. For these data, we can either assign a virus type to the sequences or reject atypical samples. Those rejected sequences allow to speculate about new virus types with respect to nucleotide base mutations in the viral sequences. Moreover, this rejection analysis improves model robustness. Last but not least, the presented approach has lower computational complexity compared to methods based on (multiple) sequence alignment. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s00521-021-06018-2.

Divergence-based vector quantization.

Supervised and unsupervised vector quantization methods for classification and clustering traditionally use dissimilarities, frequently taken as Euclidean distances. In this article, we investigate the applicability of divergences instead, focusing on online learning. We deduce the mathematical fundamentals for its utilization in gradient-based online vector quantization algorithms. It bears on the generalized derivatives of the divergences known as Fréchet derivatives in functional analysis, which reduces in finite-dimensional problems to partial derivatives in a natural way. We demonstrate the application of this methodology for widely applied supervised and unsupervised online vector quantization schemes, including self-organizing maps, neural gas, and learning vector quantization. Additionally, principles for hyperparameter optimization and relevance learning for parameterized divergences in the case of supervised vector quantization are given to achieve improved classification accuracy.

Classification of mass-spectrometric data in clinical proteomics using learning vector quantization methods.

In the present contribution we propose two recently developed classification algorithms for the analysis of mass-spectrometric data-the supervised neural gas and the fuzzy-labeled self-organizing map. The algorithms are inherently regularizing, which is recommended, for these spectral data because of its high dimensionality and the sparseness for specific problems. The algorithms are both prototype-based such that the principle of characteristic representants is realized. This leads to an easy interpretation of the generated classifcation model. Further, the fuzzy-labeled self-organizing map is able to process uncertainty in data, and classification results can be obtained as fuzzy decisions. Moreover, this fuzzy classification together with the property of topographic mapping offers the possibility of class similarity detection, which can be used for class visualization. We demonstrate the power of both methods for two exemplary examples: the classification of bacteria (listeria types) and neoplastic and non-neoplastic cell populations in breast cancer tissue sections.

Instability and discontinuous change in the experience of therapeutic interaction: an extended single-case study of psychodynamic therapy processes.

The authors developed a concept that applies self-organization theory to psychodynamic principles. According to this concept, episodes of temporary destabilization represent a precondition for abrupt changes within the therapeutic process. The authors examined six courses of therapy (patients diagnosed with depression and personality disorder). After each therapy session, patients rated their experience of the therapeutic interaction. A measure of instability was used to identify episodes of destabilization with respect to patients' interaction experience throughout the process. Episodes of pronounced destabilization occurred in the four courses of therapy that showed better therapy outcomes. These episodes were characterized by temporary strong deteriorations in interaction experience (negative peaks). Three of the four courses showed subsequent discontinuous improvements to a higher level of interaction. Results indicate that the systematic inclusion of a measure of instability is worthwhile in investigations of discontinuous changes. This method allows the theoretical assumptions of the psychodynamic approach to be tested.

Atherosclerosis, myocardial infarction and primary hemostasis: Impact of platelets, von Willebrand factor and soluble glycoprotein VI.

INTRODUCTION: Little is known about peril constellations in primary hemostasis contributing to an acute myocardial infarction (MI) in patients with already manifest atherosclerosis. The study aimed to establish a predicting model based on six biomarkers of primary hemostasis: platelet count, mean platelet volume, hematocrit, soluble glycoprotein VI, fibrinogen and von Willebrand factor ratio. MATERIALS AND METHODS: The biomarkers were measured in 1.491 patients with manifest atherosclerosis of the Leipzig (LIFE) heart study. Three groups were divided: patients with coronary artery disease (900 patients) and patients with atherosclerosis and either ST-elevated MI (404 patients) or Non-ST-elevated MI (187 patients). Correlations were analyzed by non-linear analysis with Self Organizing Maps. Classification and discriminant analysis was performed using Learning Vector Quantization. RESULTS AND CONCLUSIONS: The combination of hemostatic biomarkers is regarded as valuable tool for identifying patients with atherosclerosis at risk for MI. Nevertheless, our study contradicts this belief. The biomarkers did not allow to establish a predicting model usable in daily patient care. Good specificity and sensitivity for the detection of MI was only reached in models including acute phase parameters (specificity 0,9036, sensitivity 0,7937 in men; 0,8977 and 0,8133 in women). In detail, hematocrit and soluble glycoprotein VI were significantly different between the groups. Significant dissimilarities were also found for fibrinogen (in men) and von Willebrand factor ratio. In contrast, the most promising parameters mean platelet volume and platelet count showed no difference, which is an important contribution to the controversy concerning them as new risk and therapy targets for MI.

Application of an interpretable classification model on Early Folding Residues during protein folding.

BACKGROUND: Machine learning strategies are prominent tools for data analysis. Especially in life sciences, they have become increasingly important to handle the growing datasets collected by the scientific community. Meanwhile, algorithms improve in performance, but also gain complexity, and tend to neglect interpretability and comprehensiveness of the resulting models. RESULTS: Generalized Matrix Learning Vector Quantization (GMLVQ) is a supervised, prototype-based machine learning method and provides comprehensive visualization capabilities not present in other classifiers which allow for a fine-grained interpretation of the data. In contrast to commonly used machine learning strategies, GMLVQ is well-suited for imbalanced classification problems which are frequent in life sciences. We present a Weka plug-in implementing GMLVQ. The feasibility of GMLVQ is demonstrated on a dataset of Early Folding Residues (EFR) that have been shown to initiate and guide the protein folding process. Using 27 features, an area under the receiver operating characteristic of 76.6% was achieved which is comparable to other state-of-the-art classifiers. The obtained model is accessible at https://biosciences.hs-mittweida.de/efpred/. CONCLUSIONS: The application on EFR prediction demonstrates how an easy interpretation of classification models can promote the comprehension of biological mechanisms. The results shed light on the special features of EFR which were reported as most influential for the classification: EFR are embedded in ordered secondary structure elements and they participate in networks of hydrophobic residues. Visualization capabilities of GMLVQ are presented as we demonstrate how to interpret the results.

[From chaos to insight--the character of the dynamics of therapeutic changes with a linguistic analysis of verbatim transcripts]. / Vom Chaos zur Einsicht--Die Charakterisierung der Dynamik therapeutischer Veränderungsprozesse mittels textanalytischer Untersuchung von Verbatimprotokollen.

The objective of this study is to determine and to analyze so-called key sessions in the frameworks of Therapeutic Cycles Model introduced by Mergenthaler and the Energy Model proposed by Caspar. For this purpose, different measures for key session identification are used based on linguistic text variables. The investigation is done for 10 high-frequency, psychodynamic, inpatient, individual therapies consisting of overall 206 therapeutic sessions, all of which were completely videotaped and transcribed. The text analysis was performed using the automated text analysis tool provided by Mergenthaler, which measures the construct of Emotional Tone as a linguistic manifestation of the emotional event and the construct of Abstraction as a linguistic manifestation of cognitive-reflective processes in speech and texts. Feeding these variables into both models, results reveal their coherence: Therapeutic change may occur, whenever an emotional and cognitive-reflective processing of the internal conflicts begins after destabilisation of coherent patterns of behaviour and experiencing. The discussion suggests a more detailed specification of the definition of key sessions in the Therapeutic Cycles Model by Mergenthaler.

Explicit magnification control of self-organizing maps for "forbidden" data.

In this paper, we examine the scope of validity of the explicit self-organizing map (SOM) magnification control scheme of Bauer et al. (1996) on data for which the theory does not guarantee success, namely data that are n-dimensional, n > or =2, and whose components in the different dimensions are not statistically independent. The Bauer et al. algorithm is very attractive for the possibility of faithful representation of the probability density function (pdf) of a data manifold, or for discovery of rare events, among other properties. Since theoretically unsupported data of higher dimensionality and higher complexity would benefit most from the power of explicit magnification control, we conduct systematic simulations on "forbidden" data. For the unsupported n=2 cases that we investigate, the simulations show that even though the magnification exponent alpha achieved achieved by magnification control is not the same as the desired alpha desired, alpha achieved systematically follows alpha desired with a slowly increasing positive offset. We show that for simple synthetic higher dimensional data information, theoretically optimum pdf matching (alpha achieved = 1) can be achieved, and that negative magnification has the desired effect of improving the detectability of rare classes. In addition, we further study theoretically unsupported cases with real data.

Batch and median neural gas.

Neural Gas (NG) constitutes a very robust clustering algorithm given Euclidean data which does not suffer from the problem of local minima like simple vector quantization, or topological restrictions like the self-organizing map. Based on the cost function of NG, we introduce a batch variant of NG which shows much faster convergence and which can be interpreted as an optimization of the cost function by the Newton method. This formulation has the additional benefit that, based on the notion of the generalized median in analogy to Median SOM, a variant for non-vectorial proximity data can be introduced. We prove convergence of batch and median versions of NG, SOM, and k-means in a unified formulation, and we investigate the behavior of the algorithms in several experiments.

Prototype-based models in machine learning.

An overview is given of prototype-based models in machine learning. In this framework, observations, i.e., data, are stored in terms of typical representatives. Together with a suitable measure of similarity, the systems can be employed in the context of unsupervised and supervised analysis of potentially high-dimensional, complex datasets. We discuss basic schemes of competitive vector quantization as well as the so-called neural gas approach and Kohonen's topology-preserving self-organizing map. Supervised learning in prototype systems is exemplified in terms of learning vector quantization. Most frequently, the familiar Euclidean distance serves as a dissimilarity measure. We present extensions of the framework to nonstandard measures and give an introduction to the use of adaptive distances in relevance learning.

Serotonin and dopamine transporter imaging in patients with obsessive-compulsive disorder.

In obsessive-compulsive disorder (OCD), the success of pharmacological treatment with serotonin re-uptake inhibitors and atypical antipsychotic drugs suggests that both the central serotonergic and dopaminergic systems are involved in the pathophysiology of the disorder. We applied [123I]-2beta-carbomethoxy-3beta-(4-idiophenyl)tropane (beta-CIT) and a brain-dedicated high-resolution single photon emission computed tomography (SPECT) system to quantify dopamine transporter (DAT) and serotonin transporter (SERT) availability. By comparing 15 drug-naïve patients with OCD and 10 controls, we found a significantly reduced availability (corrected for age) of striatal DAT and of thalamic/hypothalamic, midbrain and brainstem SERT in OCD patients. Severity of OCD symptoms showed a significant negative correlation with thalamic/hypothalamic SERT availability, corrected for age and duration of symptoms. Our data provide evidence for imbalanced monoaminergic neurotransmitter modulation in OCD. Further studies with more selective DAT and SERT radiotracers are needed.

Correlation between automated writing movements and striatal dopaminergic innervation in patients with Wilson's disease.

Handwriting defects are an early sign of motor impairment in patients with Wilson's disease. The basal ganglia being the primary site of copper accumulation in the brain suggests a correlation with lesions in the nigrostiatal dopaminergic system. We have analysed and correlated striatal dopaminergic innervation using [(123)I]beta-CIT-SPECT and automated handwriting movements in 37 patients with Wilson's disease. There was a significant correlation of putaminal dopaminergic innervation with fine motor ability (p < 0,05 for NIV [number of inversion in velocity], NIA [number of inversion in acceleration], frequency). These data suggest that loss of dorsolateral striatal dopaminergic innervation has a pathophysiological function for decreased automated motor control in Wilson's disease. Furthermore analysis of automated handwriting movements could be useful for therapy monitoring and evaluation of striatal dopaminergic innervation.

Generalized relevance learning vector quantization.

We propose a new scheme for enlarging generalized learning vector quantization (GLVQ) with weighting factors for the input dimensions. The factors allow an appropriate scaling of the input dimensions according to their relevance. They are adapted automatically during training according to the specific classification task whereby training can be interpreted as stochastic gradient descent on an appropriate error function. This method leads to a more powerful classifier and to an adaptive metric with little extra cost compared to standard GLVQ. Moreover, the size of the weighting factors indicates the relevance of the input dimensions. This proposes a scheme for automatically pruning irrelevant input dimensions. The algorithm is verified on artificial data sets and the iris data from the UCI repository. Afterwards, the method is compared to several well known algorithms which determine the intrinsic data dimension on real world satellite image data.