Visual Integration of Data and Model Space in Ensemble Learning

Ensembles of classifier models typically deliver superior performance and can outperform single classifier models given a dataset and classification task at hand. However, the gain in performance comes together with the lack in comprehensibility, posing a challenge to understand how each model affects the classification outputs and where the errors come from. We propose a tight visual integration of the data and the model space for exploring and combining classifier models. We introduce a workflow that builds upon the visual integration and enables the effective exploration of classification outputs and models. We then present a use case in which we start with an ensemble automatically selected by a standard ensemble selection algorithm, and show how we can manipulate models and alternative combinations.Comment: 8 pages, 7 picture

Spherical similarity explorer for comparative case analysis

Comparative Case Analysis (CCA) is an important tool for criminal investigation and crime theory extraction. It analyzes the commonalities and differences between a collection of crime reports in order to understand crime patterns and identify abnormal cases. A big challenge of CCA is the data processing and exploration. Traditional manual approach can no longer cope with the increasing volume and complexity of the data. In this paper we introduce a novel visual analytics system, Spherical Similarity Explorer (SSE) that automates the data processing process and provides interactive visualizations to support the data exploration. We illustrate the use of the system with uses cases that involve real world application data and evaluate the system with criminal intelligence analysts

Human papillomavirus vaccination of girls in the German model region Saarland: Insurance data-based analysis and identification of starting points for improving vaccination rates

In Germany, the incidence of cervical cancer, a disease caused by human papillomaviruses (HPV), is higher than in neighboring European countries. HPV vaccination has been recommended for girls since 2007. However, it continues to be significantly less well received than other childhood vaccines, so its potential for cancer prevention is not fully realized. To find new starting points for improving vaccination rates, we analyzed pseudonymized routine billing data from statutory health insurers in the PRÄZIS study (prevention of cervical carcinoma and its precursors in women in Saarland) in the federal state Saarland serving as a model region. We show that lowering the HPV vaccination age to 9 years led to more completed HPV vaccinations already in 2015. Since then, HPV vaccination rates and the proportion of 9- to 11-year-old girls among HPV-vaccinated females have steadily increased. However, HPV vaccination rates among 15-year-old girls in Saarland remained well below 50% in 2019. Pediatricians vaccinated the most girls overall, with a particularly high proportion at the recommended vaccination age of 9–14 years, while gynecologists provided more HPV catch-up vaccinations among 15-17-year-old girls, and general practitioners compensated for HPV vaccination in Saarland communities with fewer pediatricians or gynecologists. We also provide evidence for a significant association between attendance at the children´s medical check-ups “U11” or “J1” and HPV vaccination. In particular, participation in HPV vaccination is high on the day of U11. However, obstacles are that U11 is currently not financed by all statutory health insurers and there is a lack of invitation procedures for both U11 and J1, resulting in significantly lower participation rates than for the earlier U8 or U9 screenings, which are conducted exclusively with invitations and reminders. Based on our data, we propose to restructure U11 and J1 screening in Germany, with mandatory funding for U11 and organized invitations for HPV vaccination at U11 or J1 for both boys and girls

Delphi Initiative for Early-Onset Colorectal Cancer (DIRECt) International Management Guidelines

Background & aims: Patients with early-onset colorectal cancer (eoCRC) are managed according to guidelines that are not age-specific. A multidisciplinary international group (DIRECt), composed of 69 experts, was convened to develop the first evidence-based consensus recommendations for eoCRC. Methods: After reviewing the published literature, a Delphi methodology was used to draft and respond to clinically relevant questions. Each statement underwent 3 rounds of voting and reached a consensus level of agreement of ≥80%. Results: The DIRECt group produced 31 statements in 7 areas of interest: diagnosis, risk factors, genetics, pathology-oncology, endoscopy, therapy, and supportive care. There was strong consensus that all individuals younger than 50 should undergo CRC risk stratification and prompt symptom assessment. All newly diagnosed eoCRC patients should receive germline genetic testing, ideally before surgery. On the basis of current evidence, endoscopic, surgical, and oncologic treatment of eoCRC should not differ from later-onset CRC, except for individuals with pathogenic or likely pathogenic germline variants. The evidence on chemotherapy is not sufficient to recommend changes to established therapeutic protocols. Fertility preservation and sexual health are important to address in eoCRC survivors. The DIRECt group highlighted areas with knowledge gaps that should be prioritized in future research efforts, including age at first screening for the general population, use of fecal immunochemical tests, chemotherapy, endoscopic therapy, and post-treatment surveillance for eoCRC patients. Conclusions: The DIRECt group produced the first consensus recommendations on eoCRC. All statements should be considered together with the accompanying comments and literature reviews. We highlighted areas where research should be prioritized. These guidelines represent a useful tool for clinicians caring for patients with eoCRC

Making machine intelligence less scary for criminal analysts: reflections on designing a visual comparative case analysis tool

A fundamental task in Criminal Intelligence Analysis is to analyze the similarity of crime cases, called CCA, to identify common crime patterns and to reason about unsolved crimes. Typically, the data is complex and high dimensional and the use of complex analytical processes would be appropriate. State-of-the-art CCA tools lack flexibility in interactive data exploration and fall short of computational transparency in terms of revealing alternative methods and results. In this paper, we report on the design of the Concept Explorer, a flexible, transparent and interactive CCA system. During this design process, we observed that most criminal analysts are not able to understand the underlying complex technical processes, which decrease the users' trust in the results and hence a reluctance to use the tool}. Our CCA solution implements a computational pipeline together with a visual platform that allows the analysts to interact with each stage of the analysis process and to validate the result. The proposed Visual Analytics workflow iteratively supports the interpretation of the results of clustering with the respective feature relations, the development of alternative models, as well as cluster verification. The visualizations offer an understandable and usable way for the analyst to provide feedback to the system and to observe the impact of their interactions. Expert feedback confirmed that our user-centred design decisions made this computational complexity less scary to criminal analysts

Transparency in Interactive Feature-based Machine Learning : Challenges and Solutions

Machine learning is ubiquitous in everyday life; techniques from the area of automated data analysis are used in various application scenarios, ranging from recommendations for movies over routes to drive to automated analysis of data in critical domains. To make appropriate use of such techniques, a calibration between human trust and trustworthiness of the machine learning techniques is required. If the calibration does not take place, research shows that disuse and misuse of machine learning techniques may happen. In this thesis, we elaborate on the problem of providing transparency in feature-based machine learning. In particular, we outline a number of challenges and present solutions for transparency. The solutions are based on interactive visual interfaces operating on feature-level. First, we elaborate on the connection between trust and transparency and outline the fundamental framework that builds the ground for this thesis and introduce different audiences of transparency. In the following, we present interactive, visualization and visual analytics-based solutions for specific aspects of transparency. First, the solution for the task of error analysis in supervised learning is presented. The proposed visual analytics system contains a number of coordinated views that facilitate sensemaking and reasoning of the influence of single features or groups of features in the machine learning process. The second solution is a visualization technique tailored to the interactive, visual exploration of ambiguous feature sets that arise in certain machine learning scenarios. Statistical and semantical information is combined to present a clear picture of the targeted type of ambiguities that can be interactively modified, eventually leading to a more specific feature set with fewer ambiguities. Afterward we illustrate how the concept of transparency and observable behavior can be of use in a real-world scenario. We contribute an interactive, visualization-driven system to explore a spatial clustering, giving the human control of the feature set, feature weights, and associated hyperparameters. To observe different behaviors of the spatial clustering, an interactive visualization is provided that allows the comparison of different feature combinations and hyperparameters. In the same application domain, we contribute a visual analytics system that enables analysts to interactively visualize the output of a machine learning system in context with additional data that have a common, spatial context. The system bridges the gap between the analysts utilizing a machine learning system and users of the results, which in the targeted scenario are two different user groups. Our solutions show that both groups profit from insights in the feature set of the machine learning. The thesis concludes with a reflection regarding further research directions and a summary of the results.publishe

Predictive Policing in Germany

The subject of predictive policing has been very virulent in Germany since 2014. Currently different approaches are running in six federal states. Despite the many approaches, there is still no scientific report that grants a corresponding overview. The reason for the lack of such an overview lies in the fact that less official reports of the different approaches have been published.1 In this article, we introduce the German idea of predictive policing and make a brief reference to the problem of the comparability of different solutions. After that we survey the existing solutions chosen by the federal states and their police authorities.publishe