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2019 | OriginalPaper | Buchkapitel

2. Big Data in Gesundheitswesen und Medizin

verfasst von : Stefan Rüping, Jil Sander

Verlag: Springer Berlin Heidelberg

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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.

Vorheriges Kapitel E-Health: Begriff, Umsetzungsbarrieren, Nachhaltigkeit und Nutzen

Nächstes Kapitel Künstliche Intelligenz im Gesundheitswesen

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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Titel: Big Data in Gesundheitswesen und Medizin
verfasst von: Stefan Rüping
Jil Sander
Verlag: Springer Berlin Heidelberg
Buch: Gesundheit digital
Print ISBN: 978-3-662-57610-6

Electronic ISBN: 978-3-662-57611-3

Copyright-Jahr: 2019
DOI: https://doi.org/10.1007/978-3-662-57611-3_2

Springerpflege

Zusammenfassung

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