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LiV2O4 is a mixed-valent spinel oxide and one of a few transition-metal compounds to host a heavy fermion phase at low temperatures. Although numerous experimental studies have attempted to elucidate how its 3d electrons undergo giant mass renormalization, spectroscopic probes that may provide crucial hints, such as scanning tunneling microscopy (STM), remain to be applied. A prerequisite is atomically flat and pristine surfaces, which, in the case of LiV2O4, are difficult to obtain by the cleavage of small, three-dimensional crystals. We report the epitaxial growth of LiV2O4 thin films with bulklike properties on SrTiO3(111) via pulsed laser deposition and stable STM imaging of the LiV2O4(111) surface. The as-grown films were transferred ex situ to a room-temperature STM, where subsequent annealing with optional sputtering in ultrahigh vacuum enabled compact islands with smooth surfaces and a hexagonal 1 × 1 atomic lattice to be resolved. Our STM measurements provide insights into the growth mechanisms of LiV2O4 on SrTiO3(111) as well as demonstrate the feasibility of performing surface-sensitive measurements of this heavy fermion compound. [ABSTRACT FROM AUTHOR]

Attrition or dropout is the most severe missingness problem in longitudinal cohort study data where some participants do not show up for follow‐up examinations. Dropouts result in biased data and cause the reduction of 1ata set size. Moreover, they limit the power of statistical analysis and the validity of study findings. Visualization can play a strong role in analysing and displaying the missingness patterns. In this work, we present VIVID, a framework for the visual analysis of missing values in cohort study data. VIVID is inspired by discussions with epidemiologists and adds visual components to their current statistics‐based approaches. VIVID provides functions for exploration, imputation and validity check of imputations. The main focus of this paper is multiple imputation to fix the missing data. [ABSTRACT FROM AUTHOR]

A prerequisite of personalized medicine is the identification of groups of people who share specific risk factors towards an outcome. We investigate the potential of subspace clustering for finding such groups in epidemiological data. We propose a workflow that encompasses clusterability assessment before cluster discovery and quality assessment after learning the clusters. Epidemiological usually do not have a ground truth for the verification of clusters found in subspaces. Hence, we introduce quality assessment through juxtaposition of the learned models to “models-of-randomness”, i.e. models that do not reflect a true cluster structure. On the basis of this workflow, we select subspace clustering methods, compare and discuss their performance. We use a dataset with hepatic steatosis as outcome, but our findings apply on arbitrary epidemiological cohort data that have tenths of variables and exhibit class skew.

Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The author(s) declared that they were an editorial board member of Frontiers, at the time of submission. This had no impact on the peer review process and the final decision.

The clinical heterogeneity of chronic tinnitus poses major challenges to patient management and prompts the identification of distinct patient subgroups (or phenotypes) that respond more predictable to a particular treatment. We model heterogeneity in treatment response among phenotypes of tinnitus patients concerning their change in self-reported health burden, psychological characteristics, and tinnitus characteristics. Before and after a 7-day multimodal treatment, 989 tinnitus patients completed 14 assessment questionnaires, from which 64 variables measured general tinnitus characteristics, quality of life, pain experiences, somatic expressions, affective symptoms, tinnitus-related distress, internal resources, and perceived stress. Our approach encompasses mechanisms for patient phenotyping, visualizations of the phenotypes and their change with treatment in a projected space, and the extraction of patient subgroups based on their change with treatment. On average, all four distinct phenotypes identified at the pre-intervention baseline showed improved values for nearly all the considered variables following the intervention. However, a considerable intra-phenotype heterogeneity was noted. Five clusters of change reflected variations in the observed improvements among individuals. These patterns of treatment effects were identified to be associated with baseline phenotypes. Our exploratory approach establishes a groundwork for future studies incorporating control groups to pinpoint patient subgroups that are more likely to benefit from specific treatments. This strategy not only has the potential to advance personalized medicine but can also be extended to a broader spectrum of patients with various chronic conditions., (© 2024. The Author(s).)

Background: Parameters of body composition have prognostic potential in patients with oncologic diseases. The aim of the present study was to analyse the prognostic potential of radiomics-based parameters of the skeletal musculature and adipose tissues in patients with advanced hepatocellular carcinoma (HCC)., Methods: Radiomics features were extracted from a cohort of 297 HCC patients as post hoc sub-study of the SORAMIC randomized controlled trial. Patients were treated with selective internal radiation therapy (SIRT) in combination with sorafenib or with sorafenib alone yielding two groups: (1) sorafenib monotherapy (n = 147) and (2) sorafenib and SIRT (n = 150). The main outcome was 1-year survival. Segmentation of muscle tissue and adipose tissue was used to retrieve 881 features. Correlation analysis and feature cleansing yielded 292 features for each patient group and each tissue type. We combined 9 feature selection methods with 10 feature set compositions to build 90 feature sets. We used 11 classifiers to build 990 models. We subdivided the patient groups into a train and validation cohort and a test cohort, that is, one third of the patient groups., Results: We used the train and validation set to identify the best feature selection and classification model and applied it to the test set for each patient group. Classification yields for patients who underwent sorafenib monotherapy an accuracy of 75.51% and area under the curve (AUC) of 0.7576 (95% confidence interval [CI]: 0.6376-0.8776). For patients who underwent treatment with SIRT and sorafenib, results are accuracy = 78.00% and AUC = 0.8032 (95% CI: 0.6930-0.9134)., Conclusions: Parameters of radiomics-based analysis of the skeletal musculature and adipose tissue predict 1-year survival in patients with advanced HCC. The prognostic value of radiomics-based parameters was higher in patients who were treated with SIRT and sorafenib., (© 2023 The Authors. Journal of Cachexia, Sarcopenia and Muscle published by Wiley Periodicals LLC.)

Background: Tinnitus is a leading cause of disease burden globally. Several therapeutic strategies are recommended in guidelines for the reduction of tinnitus distress; however, little is known about the potentially increased effectiveness of a combination of treatments and personalized treatments for each tinnitus patient., Methods: Within the Unification of Treatments and Interventions for Tinnitus Patients project, a multicenter, randomized clinical trial is conducted with the aim to compare the effectiveness of single treatments and combined treatments on tinnitus distress (UNITI-RCT). Five different tinnitus centers across Europe aim to treat chronic tinnitus patients with either cognitive behavioral therapy, sound therapy, structured counseling, or hearing aids alone, or with a combination of two of these treatments, resulting in four treatment arms with single treatment and six treatment arms with combinational treatment. This statistical analysis plan describes the statistical methods to be deployed in the UNITI-RCT., Discussion: The UNITI-RCT trial will provide important evidence about whether a combination of treatments is superior to a single treatment alone in the management of chronic tinnitus patients. This pre-specified statistical analysis plan details the methodology for the analysis of the UNITI trial results., Trial Registration: ClinicalTrials.gov NCT04663828 . The trial is ongoing. Date of registration: December 11, 2020. All patients that finished their treatment before 19 December 2022 are included in the main RCT analysis., (© 2023. The Author(s).)

The mixed-valent spinel LiV 2 O 4 is known as the first oxide heavy-fermion system. There is a general consensus that a subtle interplay of charge, spin, and orbital degrees of freedom of correlated electrons plays a crucial role in the enhancement of quasi-particle mass, but the specific mechanism has remained yet elusive. A charge-ordering (CO) instability of V 3+ and V 4+ ions that is geometrically frustrated by the V pyrochlore sublattice from forming a long-range CO down to T = 0 K has been proposed as a prime candidate for the mechanism. Here, we uncover the hidden CO instability by applying epitaxial strain on single-crystalline LiV 2 O 4 thin films. We find a crystallization of heavy fermions in a LiV 2 O 4 film on MgO, where a charge-ordered insulator comprising of a stack of V 3+ and V 4+ layers along [001], the historical Verwey-type ordering, is stabilized by the in-plane tensile and out-of-plane compressive strains from the substrate. Our discovery of the [001] Verwey-type CO, together with previous realizations of a distinct [111] CO, evidence the close proximity of the heavy-fermion state to degenerate CO states mirroring the geometrical frustration of the V pyrochlore lattice, which supports the CO instability scenario for the mechanism behind the heavy-fermion formation.

We present the framework GUCCI (Guided Cardiac Cohort Investigation), which provides a guided visual analytics workflow to analyze cohort-based measured blood flow data in the aorta. In the past, many specialized techniques have been developed for the visual exploration of such data sets for a better understanding of the influence of morphological and hemodynamic conditions on cardiovascular diseases. However, there is a lack of dedicated techniques that allow visual comparison of multiple data sets and defined cohorts, which is essential to characterize pathologies. GUCCI offers visual analytics techniques and novel visualization methods to guide the user through the comparison of predefined cohorts, such as healthy volunteers and patients with a pathologically altered aorta. The combination of overview and glyph-based depictions together with statistical cohort-specific information allows investigating differences and similarities of the time-dependent data. Our framework was evaluated in a qualitative user study with three radiologists specialized in cardiac imaging and two experts in medical blood flow visualization. They were able to discover cohort-specific characteristics, which supports the derivation of standard values as well as the assessment of pathology-related severity and the need for treatment.

Background: Chronic tinnitus is a clinically multidimensional phenomenon that entails audiological, psychological and somatosensory components. Previous research has demonstrated age and female gender as potential risk factors, although studies to this regard are heterogeneous. Moreover, whilst recent research has begun to identify clinical "phenotypes," little is known about differences in patient population profiles at geographically separated and specialized treatment centers. Identifying such differences might prevent potential biases in joint randomized controlled trials (RCTs) and allow for population-specific treatment adaptations., Method: Two German tinnitus treatment centers were compared regarding pre-treatment data distributions of their patient population bases. To identify overlapping as well as center-specific factors, juxtaposition-, similarity-, and meta-data-based methods were applied., Results: Between centers, significant differences emerged. One center demonstrated some predictive power of the patients of the other center with regard to questionnaire score after treatment, indicating similarities in treatment response across center populations. Furthermore, adherence to the completion of the questionnaires was found to be an important factor in predicting post-treatment data., Discussion: Differential age and gender distributions per center should be considered as regards RCT design and individualized treatment planning., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Puga, Schleicher, Niemann, Unnikrishnan, Boecking, Brueggemann, Simoes, Langguth, Schlee, Mazurek and Spiliopoulou.)

Objectives: (1) To determine which psychosocial aspects predict tinnitus-related distress in a large self-reported dataset of patients with chronic tinnitus, and (2) to identify underlying constructs by means of factor analysis., Methods: A cohort of 1958 patients of the Charité Tinnitus Center, Berlin completed a large questionnaire battery that comprised sociodemographic data, tinnitus-related distress, general psychological stress experience, emotional symptoms, and somatic complaints. To identify a construct of "tinnitus-related distress", significant predictive items were grouped using factor analysis., Results: For the prediction of tinnitus-related distress (linear regression model with R 2 = 0.7), depressive fatigue symptoms (concentration, sleep, rumination, joy decreased), the experience of emotional strain, somatization tendencies (pain experience, doctor contacts), and age appeared to play a role. The factor analysis revealed five factors: "stress", "pain experience", "fatigue", "autonomy", and low "educational level"., Conclusions: Tinnitus-related distress is predicted by psychological and sociodemographic indices. Relevant factors seem to be depressive exhaustion with somatic expressions such as sleep and concentration problems, somatization, general psychological stress, and reduced activity, in addition to higher age.

Background: As healthcare-related data proliferate, there is need to annotate them expertly for the purposes of personalized medicine. Crowdworking is an alternative to expensive expert labour. Annotation corresponds to diagnosis, so comparing unlabeled records to labeled ones seems more appropriate for crowdworkers without medical expertise. We modeled the comparison of a record to two other records as a triplet annotation task, and we conducted an experiment to investigate to what extend sensor-measured stress, task duration, uncertainty of the annotators and agreement among the annotators could predict annotation correctness., Materials and Methods: We conducted an annotation experiment on health data from a population-based study. The triplet annotation task was to decide whether an individual was more similar to a healthy one or to one with a given disorder. We used hepatic steatosis as example disorder, and described the individuals with 10 pre-selected characteristics related to this disorder. We recorded task duration, electro-dermal activity as stress indicator, and uncertainty as stated by the experiment participants (n = 29 non-experts and three experts) for 30 triplets. We built an Artificial Similarity-Based Annotator (ASBA) and compared its correctness and uncertainty to that of the experiment participants., Results: We found no correlation between correctness and either of stated uncertainty, stress and task duration. Annotator agreement has not been predictive either. Notably, for some tasks, annotators agreed unanimously on an incorrect annotation. When controlling for Triplet ID, we identified significant correlations, indicating that correctness, stress levels and annotation duration depend on the task itself. Average correctness among the experiment participants was slightly lower than achieved by ASBA. Triplet annotation turned to be similarly difficult for experts as for non-experts., Conclusion: Our lab experiment indicates that the task of triplet annotation must be prepared cautiously if delegated to crowdworkers. Neither certainty nor agreement among annotators should be assumed to imply correct annotation, because annotators may misjudge difficult tasks as easy and agree on incorrect annotations. Further research is needed to improve visualizations for complex tasks, to judiciously decide how much information to provide, Out-of-the-lab experiments in crowdworker setting are needed to identify appropriate designs of a human-annotation task, and to assess under what circumstances non-human annotation should be preferred., Competing Interests: The authors have declared that no competing interests exist.

Tinnitus is the perception of a phantom sound and the patient's reaction to it. Although much progress has been made, tinnitus remains a scientific and clinical enigma of high prevalence and high economic burden, with an estimated prevalence of 10%-20% among the adult population. The EU is funding a new collaborative project entitled "Unification of Treatments and Interventions for Tinnitus Patients" (UNITI, grant no. 848261) under its Horizon 2020 framework. The main goal of the UNITI project is to set the ground for a predictive computational model based on existing and longitudinal data attempting to address the question of which treatment or combination of treatments is optimal for a specific patient group based on certain parameters. Clinical, epidemiological, genetic and audiological data, including signals reflecting ear-brain communication, as well as patients' medical history, will be analyzed making use of existing databases. Predictive factors for different patient groups will be extracted and their prognostic relevance validated through a Randomized Clinical Trial (RCT) in which different patient groups will undergo a combination of tinnitus therapies targeting both auditory and central nervous systems. From a scientific point of view, the UNITI project can be summarized into the following research goals: (1) Analysis of existing data: Results of existing clinical studies will be analyzed to identify subgroups of patients with specific treatment responses and to identify systematic differences between the patient groups at the participating clinical centers. (2) Genetic and blood biomarker analysis: High throughput Whole Exome Sequencing (WES) will be performed in well-characterized chronic tinnitus cases, together with Proximity Extension Assays (PEA) for the identification of blood biomarkers for tinnitus. (3) RCT: A total of 500 patients will be recruited at five clinical centers across Europe comparing single treatments against combinational treatments. The four main treatments are Cognitive Behavioral Therapy (CBT), hearing aids, sound stimulation, and structured counseling. The consortium will also make use of e/m-health applications for the treatment and assessment of tinnitus. (4) Decision Support System: An innovative Decision Support System will be implemented, integrating all available parameters (epidemiological, clinical, audiometry, genetics, socioeconomic and medical history) to suggest specific examinations and the optimal intervention strategy based on the collected data. (5) Financial estimation analysis: A cost-effectiveness analysis for the respective interventions will be calculated to investigate the economic effects of the interventions based on quality-adjusted life years. In this paper, we will present the UNITI project, the scientific questions that it aims to address, the research consortium, and the organizational structure., (© 2021 Elsevier B.V. All rights reserved.)

Chronic tinnitus is a complex, multi-factorial symptom that requires careful assessment and management. Evidence-based therapeutic approaches involve audiological and psychological treatment components. However, not everyone benefits from treatment. The identification and characterisation of patient subgroups (or "phenotypes") may provide clinically relevant information. Due to the large number of assessment tools, data-driven methods appear to be promising. The acceptance of these empirical results can be further strengthened by a comprehensive visualisation. In this study, we used cluster analysis to identify distinct tinnitus phenotypes based on self-report questionnaire data and implemented a visualisation tool to explore phenotype idiosyncrasies. 1228 patients with chronic tinnitus from the Charité Tinnitus Center in Berlin were included. At baseline, each participant completed 14 questionnaires measuring tinnitus distress, -loudness, frequency and location, depressivity, perceived stress, quality of life, physical and mental health, pain perception, somatic symptom expression and coping attitudes. Four distinct patient phenotypes emerged from clustering: avoidant group (56.8%), psychosomatic group (14.1%), somatic group (15.2%), and distress group (13.9%). Radial bar- and line charts allowed for visual inspection and juxtaposition of major phenotype characteristics. The phenotypes differed in terms of clinical information including psychological symptoms, quality of life, coping attitudes, stress, tinnitus-related distress and pain, as well as socio-demographics. Our findings suggest that identifiable patient subgroups and their visualisation may allow for stratified treatment strategies and research designs.

Purpose: Medical case-based reasoning solves problems by applying experience gained from the outcome of previous treatments of the same kind. Particularly for complex treatment decisions, for example, incidentally found intracranial aneurysms (IAs), it can support the medical expert. IAs bear the risk of rupture and may lead to subarachnoidal hemorrhages. Treatment needs to be considered carefully, since it may entail unnecessary complications for IAs with low rupture risk. With a rupture risk prediction based on previous cases, the treatment decision can be supported., Methods: We present an interactive visual exploration tool for the case-based reasoning of IAs. In presence of a new aneurysm of interest, our application provides visual analytics techniques to identify the most similar cases with respect to morphology. The clinical expert can obtain the treatment, including the treatment outcome, for these cases and transfer it to the aneurysm of interest. Our application comprises a heatmap visualization, an adapted scatterplot matrix and fully or partially directed graphs with a circle- or force-directed layout to guide the interactive selection process. To fit the demands of clinical applications, we further integrated an interactive identification of outlier cases as well as an interactive attribute selection for the similarity calculation. A questionnaire evaluation with six trained physicians was used., Result: Our application allows for case-based reasoning of IAs based on a reference data set. Three classifiers summarize the rupture state of the most similar cases. Medical experts positively evaluated the application., Conclusion: Our case-based reasoning application combined with visual analytic techniques allows for representation of similar IAs to support the clinician. The graphical representation was rated very useful and provides visual information of the similarity of the k most similar cases.

Whilst some studies have identified gender-specific differences, there is no consensus about gender-specific determinants for prevalence rates or concomitant symptoms of chronic tinnitus such as depression or anxiety. However, gender-associated differences in psychological response profiles and coping strategies may differentially affect tinnitus chronification and treatment success rates. Thus, understanding gender-associated differences may facilitate a more detailed identification of symptom profiles, heighten treatment response rates, and help to create access for vulnerable populations that are potentially less visible in clinical settings. Our research questions are: RQ1: how do male and female tinnitus patients differ regarding tinnitus-related distress, depression severity, and treatment response, RQ2: to what extent are answers to questionnaires administered at baseline associated with gender, and RQ3: which baseline questionnaire items are associated with tinnitus distress, depression, and treatment response, while relating to one gender only? In this work, we present a data analysis workflow to investigate gender-specific differences in N = 1,628 patients with chronic tinnitus (828 female, 800 male) who completed a 7-day multimodal treatment encompassing cognitive behavioral therapy (CBT), physiotherapy, auditory attention training, and information counseling components. For this purpose, we extracted 181 variables from 7 self-report questionnaires on socio-demographics, tinnitus-related distress, tinnitus frequency, loudness, localization, and quality as well as physical and mental health status. Our workflow comprises (i) training machine learning models, (ii) a comprehensive evaluation including hyperparameter optimization, and (iii) post-learning steps to identify predictive variables. We found that female patients reported higher levels of tinnitus-related distress, depression and response to treatment (RQ1). Female patients indicated higher levels of tension, stress, and psychological coping strategies rates. By contrast, male patients reported higher levels of bodily pain associated with chronic tinnitus whilst judging their overall health as better (RQ2). Variables measuring depression, sleep problems, tinnitus frequency, and loudness were associated with tinnitus-related distress in both genders and indicators of mental health and subjective stress were found to be associated with depression in both genders (RQ3). Our results suggest that gender-associated differences in symptomatology and treatment response profiles suggest clinical and conceptual needs for differential diagnostics, case conceptualization and treatment pathways., (Copyright © 2020 Niemann, Boecking, Brueggemann, Mazurek and Spiliopoulou.)

Background: Microcirculatory defects in diabetes are linked with neuropathy and the onset of diabetic foot syndrome. In this study we quantify pressure- and posture-dependent changes of plantar temperatures as a surrogate of tissue perfusion in healthy volunteers versus diabetes patients diagnosed with neuropathy in the absence of macroangiopathy., Methods: Healthy volunteers (n = 31) as well as patients with diabetes diagnosed with severe polyneuropathy (n = 30) were enrolled in a clinical study to test for plantar temperature changes in the feet during extended episodes of standing. These lasted between 5 and 20 min each over 95 min, in between the participants were asked to take a seated position for 5 min and release the pressure from the feet. Major macroangiopathy was excluded before study enrolment. Custom-made insoles harbored temperature and pressure sensors positioned at eight preselected positions for recording., Findings: In both subgroups a significant plantar temperature downshift occurred within 10 min of standing, which was especially detected during the initial 45 min of the study protocol. Comparisons between healthy volunteers and patients with diabetes revealed no differences in the magnitude of temperature downshifts during stance episodes. Pressure sensor recordings revealed that healthy volunteers intermittently released pressure during the longer stance episodes due to discomfort, whereas the patients with diabetes and polyneuropathy did not., Interpretation: Our findings demonstrate a tight plantar temperature regulation following pressure exposure. In patients with diabetes and peripheral sensoric neuropathy the temperature drop is similar to healthy volunteers. Potentially, prolonged stance periods resulting in less perfused plantar tissue may remain unrecognized with polyneuropathy, whereas discomfort develops in healthy controls., Funding: The study was supported by EFRE Förderung der Europäischen Union und Landesmittel des Ministeriums für Wirtschaft, Wissenschaft und Digitalisierung Sachsen-Anhalt (Vorhabennummer: ZS/2016/05/78,615 and ZS/2018/12/95,325). JK and PRM were supported by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) - project ID 97,850,925 - SFB854, AM by the Chinese Scholarship Council (CSC)., Competing Interests: Declaration of Competing Interest SK and PRM filed a patent on sensor-equipped insoles: WO2012084814A1. TS is CEO at Thorsis Technologies GmbH., (Copyright © 2020 The Author(s). Published by Elsevier B.V. All rights reserved.)

Tinnitus is a complex condition that is associated with major psychological and economic impairments - partly through various comorbidities such as depression. Understanding the interaction between tinnitus and depression may thus improve either symptom cluster's prevention, diagnosis and treatment. In this study, we developed and validated a machine learning model to predict depression severity after outpatient therapy (T1) based on variables obtained before therapy (T0). 1,490 patients with chronic tinnitus (comorbid major depressive disorder: 52.2%) who completed a 7-day multimodal treatment encompassing tinnitus-specific components, cognitive behavioural therapy, physiotherapy and informational counselling were included. 185 variables were extracted from self-report questionnaires and socio-demographic data acquired at T0. We used 11 classification methods to train models that reliably separate between subclinical and clinical depression at T1 as measured by the general depression questionnaire. To ensure highly predictive and robust classifiers, we tuned algorithm hyperparameters in a 10-fold cross-validation scheme. To reduce model complexity and improve interpretability, we wrapped model training around an incremental feature selection mechanism that retained features that contributed to model prediction. We identified a LASSO model that included all 185 features to yield highest predictive performance (AUC = 0.87 ± 0.04). Through our feature selection wrapper, we identified a LASSO model with good trade-off between predictive performance and interpretability that used only 6 features (AUC = 0.85 ± 0.05). Thus, predictive machine learning models can lead to a better understanding of depression in tinnitus patients, and contribute to the selection of suitable therapeutic strategies and concise and valid questionnaire design for patients with chronic tinnitus with or without comorbid major depressive disorder.

Background: Chronic tinnitus is a complex condition that can be associated with considerable distress. Whilst cognitive-behavioral treatment (CBT) approaches have been shown to be effective, not all patients benefit from psychological or psychologically anchored multimodal therapies. Determinants of tinnitus-related distress thus provide valuable information about tinnitus characterization and therapy planning., Objective: The study aimed to develop machine learning models that use variables (or "features") obtained before treatment to characterize patients' tinnitus-related distress status after treatment. Whilst initially all available variables were considered for model training, the final model was required to achieve highest predictive performance using only a small number of features., Methods: 1,416 tinnitus patients (decompensated tinnitus: 32%) who completed a 7-day multimodal treatment encompassing tinnitus-specific components, CBT, physiotherapy and informational counseling were included in the analysis. At baseline, patients were assessed using 205 features from 10 questionnaires comprising sociodemographic and clinical information. A data-driven workflow was developed consisting of (a) an initial exploratory correlation analysis, (b) supervised machine learning to predict tinnitus-related distress after treatment (T1) using baseline data only (T0), and (c) post-hoc analysis of the best model to facilitate model inspection and understanding. Classification methods were embedded in a feature elimination wrapper that iteratively learned on features found to be important for the model in the preceding iteration, in order to keep the performance stable while successively reducing the model complexity. 10-fold cross-validation with area under the curve (AUC) as performance measure was implemented for model generalization error estimation., Results: The best machine learning classifier (gradient boosted trees) can predict tinnitus-related distress in T1 with AUC = 0.890 using 26 features. Subjectively perceived tinnitus-related impairment, depressivity, sleep problems, physical health-related impairments in quality of life, time spent to complete questionnaires and educational level exhibited a high attribution towards model prediction., Conclusions: Machine learning can reliably identify baseline features recorded prior to treatment commencement that characterize tinnitus-related distress after treatment. The identification of key features can contribute to an improved understanding of multifactorial contributors to tinnitus-related distress and thereon based multimodal treatment strategies., Competing Interests: The authors have declared that no competing interests exist.

In diabetic patients, excessive peak plantar pressure has been identified as major risk factor for ulceration. Analyzing plantar pressure distributions potentially improves the identification of patients with a high risk for foot ulceration development. The goal of this study was to classify regional plantar pressure distributions. By means of a sensor-equipped insole, pressure recordings of healthy controls (n = 18) and diabetics with severe polyneuropathy (n = 25) were captured across eight foot regions. The study involved a controlled experimental protocol with multiple sessions, where a session contained several cycles of pressure exposure. Clustering was used to identify subgroups of study participants that are characterized by similar pressure distributions. For both analyzed groups, the number of clusters to best describe the pressure profiles was four. When both groups were combined, analysis again led to four distinct clusters. While three clusters did not separate between healthy and diabetic volunteers the fourth cluster was only represented by diabetics. Here the pressure distribution pattern is characterized by a focal point of pressure application on the forefoot and low pressure on the lateral region. Our data suggest that pressure clustering is a feasible means to identify inappropriate biomechanical plantar stress.

Epidemiological studies comprise heterogeneous data about a subject group to define disease-specific risk factors. These data contain information (features) about a subject's lifestyle, medical status as well as medical image data. Statistical regression analysis is used to evaluate these features and to identify feature combinations indicating a disease (the target feature). We propose an analysis approach of epidemiological data sets by incorporating all features in an exhaustive regression-based analysis. This approach combines all independent features w.r.t. a target feature. It provides a visualization that reveals insights into the data by highlighting relationships. The 3D Regression Heat Map, a novel 3D visual encoding, acts as an overview of the whole data set. It shows all combinations of two to three independent features with a specific target disease. Slicing through the 3D Regression Heat Map allows for the detailed analysis of the underlying relationships. Expert knowledge about disease-specific hypotheses can be included into the analysis by adjusting the regression model formulas. Furthermore, the influences of features can be assessed using a difference view comparing different calculation results. We applied our 3D Regression Heat Map method to a hepatic steatosis data set to reproduce results from a data mining-driven analysis. A qualitative analysis was conducted on a breast density data set. We were able to derive new hypotheses about relations between breast density and breast lesions with breast cancer. With the 3D Regression Heat Map, we present a visual overview of epidemiological data that allows for the first time an interactive regression-based analysis of large feature sets with respect to a disease.