Enhancing Decision Tree based Interpretation of Deep Neural Networks through L1-Orthogonal Regularization

One obstacle that so far prevents the introduction of machine learning models primarily in critical areas is the lack of explainability. In this work, a practicable approach of gaining explainability of deep artificial neural networks (NN) using an interpretable surrogate model based on decision trees is presented. Simply fitting a decision tree to a trained NN usually leads to unsatisfactory results in terms of accuracy and fidelity. Using L1-orthogonal regularization during training, however, preserves the accuracy of the NN, while it can be closely approximated by small decision trees. Tests with different data sets confirm that L1-orthogonal regularization yields models of lower complexity and at the same time higher fidelity compared to other regularizers.Comment: 8 pages, 18th IEEE International Conference on Machine Learning and Applications (ICMLA) 201

Hendra Virus Outbreak with Novel Clinical Features, Australia

To determine the epidemiologic and clinical features of a 2008 outbreak of Hendra virus infection in a veterinary clinic in Australia, we investigated the equine case-series. Four of 5 infected horses died, as did 1 of 2 infected staff members. Clinical manifestation in horses was predominantly neurologic. Preclinical transmission appears likely

Response to Beretich and Beretich

no abstract availabl

Neuronale Netze: Ein Blick in die Black Box: Beitrag auf der Internetseite Informatik aktuell (https://www.informatik-aktuell.de), 14.01.2020

Künstliche Intelligenz und allem voran Deep Learning ist momentan in aller Munde. Hierbei dominiert größtenteils die Diskussion um die gesellschaftlichen Auswirkungen, welche meist zwischen Utopie und Horrorszenarien schwankt. In dieser teils sehr aufgeregten Debatte kann schnell untergehen, was eigentlich hinter den allgegenwärtigen Begriffen KI und Deep Learning steckt. Wie sind künstliche neuronale Netze aufgebaut und wie funktionieren sie? Darüber hinaus lohnt sich ein Blick auf eine der Herausforderungen neuronaler Netze: deren "Black-Box"-Charakter. Wieso sind neuronale Netze eine Black Box, für welche Anwendungen ist dies eher kritisch und welche Lösungsansätze existieren bereits

Extraktion von Erklärungen zu Produktionsprozessen aus künstlichen Neuronalen Netzen

Ein Hindernis das bisher, vorwiegend in kritischen Anwendungdomänen, die Einführung maschineller Lernmodelle verhindert, ist deren mangelnde Erklärbarkeit. In diesem Beitrag wird ein praktikabler Ansatz zur Gewinnung von Erklärbarkeit von tiefen künstlichen neuronalen Netzen am Beispiel eines Anwendungsfalls aus dem Tiefdruck vorgestellt. Im vorliegenden Fall wird Erklärbarkeit mit Hilfe eines interpretierbaren Stellvertretermodells auf der Basis von Entscheidungsbäumen erreicht. Der Einsatz einer L1-orthogonalen Regularisierung während des Trainings des neuronalen Netzes führt dazu, dass aus dem Netz extrahierte Entscheidungsbäume eine geringe Größe und mit hoher Wiedergabetreue haben und somit gut verständlich sind

Serum 25(OH)D concentrations and atopic diseases at age 10: results from the GINIplus and LISAplus birth cohort studies

Background: Vitamin D is well recognized for its role in skeletal health and its involvement in the modulation of the immune system. In the literature, controversial results are reported for atopic diseases. Thus, we investigated the association between vitamin D status and the prevalence of atopic diseases. Methods: Serum 25-hydroxy-vitamin D (25(OH) D) concentrations were measured in a sample of 2815 10-years old children from two German birth cohort studies. Self-reported physician-diagnosed eczema, hay fever or allergic rhinitis, and asthma were used as outcome variables as well as specific IgE positivity against common allergens. We applied logistic regression models, deriving adjusted odds ratio estimates (aOR) and 95% confidence intervals (CI). Results: For asthma and hay fever or allergic rhinitis, no associations existed with serum 25(OH) D concentrations. We observed a significant positive relationship between serum 25(OH) D levels and eczema at age 10 (aOR = 1.09, CI = 1.01-1.17, per 10 nmol/l increase in serum 25(OH) D levels) and for the lifetime prevalence of eczema (aOR = 1.05, CI = 1.01-1.09). Specific IgE positivity for food allergens (aOR = 1.07, CI = 1.02-1.11) and aeroallergens (aOR = 1.05, CI = 1.01-1.08) at age 10, as well as lifetime prevalence, was significantly related to the vitamin D status. Conclusion: In this study we found no indication that higher blood 25(OH) D levels are associated with decreased risk for any of the atopic outcomes in children. However, we observed a positive association of serum 25(OH) D concentrations with eczema and detectable specific IgE. Due to the given limitations of our study, the clinical relevance of these findings needs further clarification

The evolving role of medical geneticists in the era of gene therapy: an urgency to prepare

: By 2030, it is estimated that at least 30 non-oncology gene therapies will be approved in the United States alone. These therapies could be used to treat up to 50,000 patients annually and have the potential to result in major shifts in disease management pathways. Medical geneticists have well-established roles in the direct management of many rare genetic diseases and often provide support in the diagnosis and care of patients with such diseases. Because an increasing number of gene therapies are likely to become available over the next decade, there is a need to better define the role of medical geneticists within current and future gene therapy pathways and prepare for their expected role within the context of this new treatment paradigm. This commentary examines the current and potential future roles of medical geneticists in gene therapy and identifies specific needs that must be addressed for medical geneticists to assume an expanded leadership role in this area