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Big Data in Gesundheitswesen und Medizin

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Gesundheit digital

Zusammenfassung

In Medizin und Gesundheitswesen sind immer größere Mengen immer vielfältigerer Daten verfügbar, die zunehmend schneller generiert werden. Dieser allgemeine Trend wird als Big Data bezeichnet. Die Analyse von Big Data mit Methoden des maschinellen Lernens führt zur Entwicklung innovativer Lösungen, die neue medizinische Einsichten generieren und die Qualität und Effizienz im Gesundheitssystem erhöhen können. Prototypische Beispiele existieren im Bereich der Analyse klinischer Texte, der klinischen Entscheidungsunterstützung, der Analyse von Daten aus öffentlichen Datenquellen oder Wearables und in Form der Entwicklung persönlicher Assistenten. Diese Potenziale bringen aber auch neue Herausforderungen im Bereich Datenschutz und in der Transparenz bzw. Nachvollziehbarkeit der Ergebnisse für den medizinischen Experten mit sich.

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Notes

  1. 1.

    In der Statistik und im maschinellen Lernen haben sich unterschiedliche Sprachgebräuche herausgebildet, dort werden Merkmale Variablen genannt und insbesondere das Zielmerkmal als abhängige Variable bezeichnet.

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Authors and Affiliations

  1. Fraunhofer-Institut für Intelligente Analyse- und Informationssysteme (IAIS), Sankt Augustin, Deutschland

    Stefan Rüping & Jil Sander

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  1. Stefan Rüping
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  2. Jil Sander
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Correspondence to Stefan Rüping .

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  1. FB Angewandte Gesundheitswissenschaften, EU FH MED Rostock, Rostock, Germany

    Robin Haring

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Rüping, S., Sander, J. (2019). Big Data in Gesundheitswesen und Medizin. In: Haring, R. (eds) Gesundheit digital. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-57611-3_2

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