TY - JOUR A1 - Düking, Peter A1 - Fuss, Franz Konstantin A1 - Holmberg, Hans-Christer A1 - Sperlich, Billy T1 - Recommendations for assessment of the reliability, sensitivity, and validity of data provided by wearable sensors designed for monitoring physical activity JF - JMIR Mhealth and Uhealth N2 - Although it is becoming increasingly popular to monitor parameters related to training, recovery, and health with wearable sensor technology (wearables), scientific evaluation of the reliability, sensitivity, and validity of such data is limited and, where available, has involved a wide variety of approaches. To improve the trustworthiness of data collected by wearables and facilitate comparisons, we have outlined recommendations for standardized evaluation. We discuss the wearable devices themselves, as well as experimental and statistical considerations. Adherence to these recommendations should be beneficial not only for the individual, but also for regulatory organizations and insurance companies. KW - internet of things KW - activity tracker KW - data mining KW - load management KW - physical activity KW - smartwatch Y1 - 2018 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-176202 VL - 6 IS - 4 ER - TY - JOUR A1 - Breuer, René A1 - Mattheisen, Manuel A1 - Frank, Josef A1 - Krumm, Bertram A1 - Treutlein, Jens A1 - Kassem, Layla A1 - Strohmaier, Jana A1 - Herms, Stefan A1 - Mühleisen, Thomas W. A1 - Degenhardt, Franziska A1 - Cichon, Sven A1 - Nöthen, Markus M. A1 - Karypis, George A1 - Kelsoe, John A1 - Greenwood, Tiffany A1 - Nievergelt, Caroline A1 - Shilling, Paul A1 - Shekhtman, Tatyana A1 - Edenberg, Howard A1 - Craig, David A1 - Szelinger, Szabolcs A1 - Nurnberger, John A1 - Gershon, Elliot A1 - Alliey-Rodriguez, Ney A1 - Zandi, Peter A1 - Goes, Fernando A1 - Schork, Nicholas A1 - Smith, Erin A1 - Koller, Daniel A1 - Zhang, Peng A1 - Badner, Judith A1 - Berrettini, Wade A1 - Bloss, Cinnamon A1 - Byerley, William A1 - Coryell, William A1 - Foroud, Tatiana A1 - Guo, Yirin A1 - Hipolito, Maria A1 - Keating, Brendan A1 - Lawson, William A1 - Liu, Chunyu A1 - Mahon, Pamela A1 - McInnis, Melvin A1 - Murray, Sarah A1 - Nwulia, Evaristus A1 - Potash, James A1 - Rice, John A1 - Scheftner, William A1 - Zöllner, Sebastian A1 - McMahon, Francis J. A1 - Rietschel, Marcella A1 - Schulze, Thomas G. T1 - Detecting significant genotype–phenotype association rules in bipolar disorder: market research meets complex genetics JF - International Journal of Bipolar Disorders N2 - Background Disentangling the etiology of common, complex diseases is a major challenge in genetic research. For bipolar disorder (BD), several genome-wide association studies (GWAS) have been performed. Similar to other complex disorders, major breakthroughs in explaining the high heritability of BD through GWAS have remained elusive. To overcome this dilemma, genetic research into BD, has embraced a variety of strategies such as the formation of large consortia to increase sample size and sequencing approaches. Here we advocate a complementary approach making use of already existing GWAS data: a novel data mining procedure to identify yet undetected genotype–phenotype relationships. We adapted association rule mining, a data mining technique traditionally used in retail market research, to identify frequent and characteristic genotype patterns showing strong associations to phenotype clusters. We applied this strategy to three independent GWAS datasets from 2835 phenotypically characterized patients with BD. In a discovery step, 20,882 candidate association rules were extracted. Results Two of these rules—one associated with eating disorder and the other with anxiety—remained significant in an independent dataset after robust correction for multiple testing. Both showed considerable effect sizes (odds ratio ~ 3.4 and 3.0, respectively) and support previously reported molecular biological findings. Conclusion Our approach detected novel specific genotype–phenotype relationships in BD that were missed by standard analyses like GWAS. While we developed and applied our method within the context of BD gene discovery, it may facilitate identifying highly specific genotype–phenotype relationships in subsets of genome-wide data sets of other complex phenotype with similar epidemiological properties and challenges to gene discovery efforts. KW - bipolar disorder KW - subphenotypes KW - rule discovery KW - data mining KW - genotype-phenotype patterns Y1 - 2018 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-220509 VL - 6 ER - TY - THES A1 - Kaempgen, Benedikt T1 - Deskriptives Data-Mining für Entscheidungsträger: Eine Mehrfachfallstudie T1 - Descriptive data mining for decision-makers: a multiple case study N2 - Das Potenzial der Wissensentdeckung in Daten wird häufig nicht ausgenutzt, was hauptsächlich auf Barrieren zwischen dem Entwicklerteam und dem Endnutzer des Data-Mining zurückzuführen ist. In dieser Arbeit wird ein transparenter Ansatz zum Beschreiben und Erklären von Daten für Entscheidungsträger vorgestellt. In Entscheidungsträger-zentrierten Aufgaben werden die Projektanforderungen definiert und die Ergebnisse zu einer Geschichte zusammengestellt. Eine Anforderung besteht dabei aus einem tabellarischen Bericht und ggf. Mustern in seinem Inhalt, jeweils verständlich für einen Entscheidungsträger. Die technischen Aufgaben bestehen aus einer Datenprüfung, der Integration der Daten in einem Data-Warehouse sowie dem Generieren von Berichten und dem Entdecken von Mustern wie in den Anforderungen beschrieben. Mehrere Data-Mining-Projekte können durch Wissensmanagement sowie eine geeignete Infrastruktur voneinander profitieren. Der Ansatz wurde in zwei Projekten unter Verwendung von ausschließlich Open-Source-Software angewendet. N2 - Despite high potential of data mining in business and science many projects fail due to barriers between the developer team and the end user. In this work a more transparent approach to describing and explaining data to a decision-maker is presented. In decision-maker-centric tasks project requirements are defined and finally the results composed to a story. A requirement is made of a tabular report and possibly patterns in its data, each understandable to a decision-maker. The technical tasks consist of a data assay, the integration of data within a data warehouse and, as required, the creation of reports and the discovery of patterns. Multiple data mining projects benefit from each other through knowledge management and a common infrastructure. The approach has been applied to two projects exclusively using open source systems. KW - Data Mining KW - Entscheidungsträger KW - Fallstudie KW - Methodologie KW - Endnutzer KW - Business Intelligence KW - Open Source KW - data mining KW - case study KW - process model KW - end user KW - open source Y1 - 2009 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-46343 ER -