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DOI: 10.1055/a-1234-6247
Big Data‚ KI und Maschinenlernen auf dem Weg zur Precision-Psychiatry – wie verändern sie den therapeutischen Alltag?
Big Data, AI and Machine Learning for Precision Psychiatry: How are they changing the clinical practice?![](https://www.thieme-connect.de/media/fdn/202012/lookinside/thumbnails/10.1055a-1234-6247-1.jpg)
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-SystemeKey words
Psychiatry - Machine Learning - Artificial Intelligence - Big Data - Clinical Decision Support SystemPublication History
Received: 16 April 2020
Accepted: 31 July 2020
Article published online:
30 September 2020
© 2020. Thieme. All rights reserved.
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Literatur
- 1 Chekroud AM. Bigger data, harder questions—opportunities throughout mental health care. JAMA Psychiatry 2017; 74: 1183-1184 . Im Internet: https://doi.org/10.1001/jamapsychiatry.2017.3333
- 2 Collins FS, Varmus H. A new initiative on precision medicine. N Engl J Med 2015; 372: 793-795
- 3 Ardila D, Kiraly AP, Bharadwaj S. et al. End-to-end lung cancer screening with three-dimensional deep learning on low-dose chest computed tomography. Nat Med 2019; 25: 954-961 . Im Internet: http://dx.doi.org/10.1038/s41591-019-0447-x
- 4 Tomašev N, Glorot X, Rae JW. et al. A clinically applicable approach to continuous prediction of future acute kidney injury. Nature 2019; 572: 116-119 . Im Internet: http://dx.doi.org/10.1038/s41586-019-1390-1
- 5 Fernandes BS, Williams LM, Steiner J. et al. The new field of „precision psychiatry“. BMC Med 2017; 15: 1-7
- 6 Star neuroscientist Tom Insel leaves the Google-spawned Verily for … a startup? Wired. 2017 ; Im Internet: https://www.wired.com/2017/05/star-neuroscientist-tom-insel-leaves-google-spawned-verily-startup/
- 7 Fusar-Poli P, Hijazi Z, Stahl D. et al. The science of prognosis in psychiatry: a review. JAMA Psychiatry 2018; 75: 1280-1288
- 8 Paulus MP. Evidence-based pragmatic psychiatry-a call to action. JAMA Psychiatry 2017; 74: 1185-1186
- 9 Monteith S, Glenn T, Geddes J. et al. Big data are coming to psychiatry: A general introduction. Int J Bipolar Disord. 2015 3:. 1–11
- 10 Torous J, Staples P, Onnela JP. Realizing the potential of mobile mental health: new methods for new data in psychiatry. Curr Psychiatry Rep. 2015 17: . 1–7
- 11 Grünerbl A, Muaremi A, Osmani V. et al. Smartphone-based recognition of states and state changes in bipolar disorder patients. IEEE J Biomed Heal Informatics 2015; 19: 140-148
- 12 Torous J, Walker R. Leveraging digital health and machine learning toward reducing suicide - from panacea to practical tool. JAMA Psychiatry 2019; 76: 999-1000
- 13 Surmann M, Bock EM, Krey E. et al. Einstellungen gegenüber eHealth-Angeboten in Psychiatrie und Psychotherapie. Nervenarzt 2017; 88: 1036-1043
- 14 Deutscher Bundestag. Bundestag stimmt Digitale-Ver-sorgung-Gesetz zu. 2019 Im Internet: https://www.bundestag.de/dokumente/textarchiv/2019/kw45-de-digitale-versorgung-gesetz-664900
- 15 Simon GE. Big data from health records in mental health care: hardly clairvoyant but already useful. JAMA Psychiatry 2019; 76: 349-350
- 16 Nunes A, Schnack HG, Ching CRK. et al. Using structural MRI to identify bipolar disorders – 13 site machine learning study in 3020 individuals from the ENIGMA bipolar disorders working group. Mol Psychiatry 2018
- 17 Clark SR, Cearns M, Schubert KO. et al. Multimodal modeling for personalized psychiatry. In: Personalized Psychiatry. Academic Press, 2019: 521-536
- 18 Hahn T, Nierenberg AA, Whitfield-Gabrieli S. Predictive analytics in mental health: Applications, guidelines, challenges and perspectives. Mol Psychiatry 2017; 22: 37-43
- 19 Smith SM, Nichols TE. Statistical challenges in “Big Data” human neuroimaging. Neuron 2018; 97: 263-268
- 20 Winter NR, Cearns M, Clark SR. et al. From Multivariate Methods to an AI Ecosystem. submitted
- 21 Wardenaar KJ, de Jonge P. Diagnostic heterogeneity in psychiatry: towards an empirical solution. BMC Med 2013; 11: 2-4
- 22 Cearns M, Hahn T, Clark S. et al. Machine learning probability calibration for high-risk clinical decision-making. Aust N Z J Psychiatry 2020; 54: 123-126
- 23 Joyce DW, Geddes J. When deploying predictive algorithms, are summary performance measures sufficient? JAMA Psychiatry 2020; 320: 27-28
- 24 Cheng CA, Boots B. Variational inference for Gaussian process models with linear complexity. Adv Neural Inf Process Syst 2017; 2017-Decem: 5185-5195
- 25 Marquand AF, Kia SM, Zabihi M. et al. Conceptualizing mental disorders as deviations from normative functioning. Mol Psychiatry 2019; 24: 1415-1424
- 26 Scarpazza C, Baecker L, Vieira S. et al. Chapter 3 – Applications of machine learning to brain disorders. In: Mechelli A. , Vieira SBT-ML. (eds.). Academic Press; 2020. : Machine Learning – Methods and Applications to Brain Disorders, Cambridge, Massachusetts, USA 45-65
- 27 Janssen RJ, Mourão-Miranda J, Schnack HG. Making individual prognoses in psychiatry using neuroimaging and machine learning. Biol Psychiatry Cogn Neurosci Neuroimaging 2018; 3: 798-808
- 28 Jollans L, Whelan R. The clinical added value of imaging: a perspective from outcome prediction. Biol Psychiatry Cogn Neurosci Neuroimaging. 2016; 1: 423-432 . Im Internet: http://dx.doi.org/10.1016/j.bpsc.2016.04.005
- 29 Dwyer DB, Falkai P, Koutsouleris N. Machine learning approaches for clinical psychology and psychiatry. Annu Rev Clin Psychol 2018; 14: 91-118
- 30 Chekroud AM, Zotti RJ, Shehzad Z. et al. Cross-trial prediction of treatment outcome in depression: A machine learning approach. Lancet Psychiatry 2016; 3: 243-250
- 31 Fusar-Poli P, Rutigliano G, Stahl D. et al. Development and validation of a clinically based risk calculator for the transdiagnostic prediction of psychosis. JAMA Psychiatry 2017; 74: 493-500
- 32 Barak-Corren Y, Castro VM, Javitt S. et al. Predicting suicidal behavior from longitudinal electronic health records. Am J Psychiatry 2017; 174: 154-162
- 33 Hahn T, Ebner-Priemer U, Meyer-Lindenberg A. Transparent artificial intelligence – a conceptual framework for evaluating ai-based clinical decision support systems. OSF Prepr 2019
- 34 Moons KGM, Altman DG, Reitsma JB. et al. Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): explanation and elaboration. Ann Intern Med 2015; 162: W1-W73
- 35 Shortliffe EH, Sepúlveda MJ. Clinical decision support in the era of artificial intelligence. JAMA - J Am Med Assoc 2018; 320: 2199-2200
- 36 Sutton RT, Pincock D, Baumgart DC. et al. An overview of clinical decision support systems: Benefits, risks, and strategies for success. Npj Digit Med 2020; 3: 1-10
- 37 Rubeis G, Steger F. Internet- und mobilgestützte Interventionen bei psychischen Störungen. Nervenarzt 2019; 90: 497-502
- 38 Fitzpatrick KK, Darcy A, Vierhile M. Delivering cognitive behavior therapy to young adults with symptoms of depression and anxiety using a fully automated conversational agent (Woebot): a randomized controlled trial. JMIR Ment Heal 2017; 4: e19
- 39 Mengoni SE, Irvine K, Thakur D. et al. Feasibility study of a randomised controlled trial to investigate the effectiveness of using a humanoid robot to improve the social skills of children with autism spectrum disorder (Kaspar RCT): A study protocol. BMJ Open 2017; 7: 1-10
- 40 Doraiswamy PM, Blease C, Bodner K. Artificial intelligence and the future of psychiatry: Insights from a global physician survey. Artif Intell Med 2020; 102: 101753