Big Data‚ KI und Maschinenlernen auf dem Weg zur Precision-Psychiatry – wie verändern sie den therapeutischen Alltag?

Zusammenfassung

Derzeit sehen wir verstärkt Ansätze in der psychiatrischen Forschung, die sich mit prognostischen Modellen und einer individualisierten Diagnosestellung und Therapieauswahl beschäftigen. Vor diesem Hintergrund strebt die Precision-Psychiatry, wie auch andere Teildisziplinen der Medizin, eine präzisere Diagnostik und individualisierte Therapie durch Big Data an. Die elektronische Patientenakte, Datenerfassung durch Smartphones und technische Fortschritte in der Genotypisierung und Bildgebung ermöglichen eine detaillierte klinische und neurobiologische Beschreibung einer Vielzahl von Patienten. Damit diese Daten tatsächlich zu einem Paradigmenwechsel in der Behandlung psychischer Störungen führen, braucht es eine Personalisierung der Psychiatrie durch Maschinelles Lernen (ML) und Künstliche Intelligenz (KI). Neben der Digitalisierung der Klinik müssen wir daher eine KI-Infrastruktur etablieren, in der maßgeschneiderte KI- und ML-Lösungen entwickelt und nach hohen Validierungsstandards evaluiert werden können. Zusätzlich müssen Modellvorhersagen und detaillierte Patienteninformationen in KI-basierte Clinical-Decision-Support-Systeme (CDSS) integriert werden. Nur so können Big Data, Maschinelles Lernen und Künstliche Intelligenz den Behandler im therapeutischen Alltag aktiv und effizient unterstützen und eine personalisierte Behandlung erreichen.

Abstract

Currently, we are witnessing an increasing interest in predictive models and personalized diagnosis and treatment choice in psychiatric research. Against this background, the emerging field of Precision Psychiatry is trying to establish precise diagnostics and personalized therapy through Big Data. Electronic Health Records (EHR), smartphone-based data collection and advances in genotyping and imaging allow for a detailed clinical and neurobiological characterization of numerous patients. In order to revolutionize the treatment of psychiatric disorders, a personalization of psychiatry through machine learning (ML) and artificial intelligence (AI) is needed. We must therefore establish an AI ecosystem to develop and strictly validate custom-tailored AI and ML solutions. Furthermore, personalized predictions and detailed patient information must be integrated in AI-based Clinical Decision Support systems. Only in this way can Big Data, ML and AI support the clinician most effectively and help personalize treatment in psychiatry.

Schlüsselwörter

Psychiatrie - Maschinelles Lernen - Künstliche Intelligenz - Big Data - Decision-Support-Systeme

Key words

Psychiatry - Machine Learning - Artificial Intelligence - Big Data - Clinical Decision Support System

Publication History

Received: 16 April 2020

Accepted: 31 July 2020

Article published online:
30 September 2020

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