Personalized Medicine Using Neuroimmunological Biomarkers in Depressive Disorders

Major depressive disorder (MDD) is associated with increased suicidal risk and reduced productivity at work. Neuroimmunology, the study of the immune system and nervous system, provides further insight into the pathogenesis and outcome of MDD. Cytokines are the main modulators of neuroimmunology, and their levels are somewhat entangled in depressive disorders as they affect depressive symptoms and are affected by antidepressant treatment. The use of cytokine-derived medication as a treatment option for MDD is currently a topic of interest. Although not very promising, cytokines are also considered as possible prognostic or diagnostic markers for depression. The machine learning approach is a powerful tool for pattern recognition and has been used in psychiatry for finding useful patterns in data that have translational meaning and can be incorporated in daily clinical practice. This review focuses on the current knowledge of neuroimmunology and depression and the possible use of machine learning to widen our understanding of the topic.

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A machine learning approach using EEG data to predict response to SSRI treatment for major depressive disorder

Clinical Neurophysiology ◽

10.1016/j.clinph.2013.04.010 ◽

2013 ◽

Vol 124 (10) ◽

pp. 1975-1985 ◽

Cited By ~ 63

Author(s):

Ahmad Khodayari-Rostamabad ◽

James P. Reilly ◽

Gary M. Hasey ◽

Hubert de Bruin ◽

Duncan J. MacCrimmon

Keyword(s):

Machine Learning ◽

Major Depressive Disorder ◽

Depressive Disorder ◽

Learning Approach ◽

Major Depressive ◽

Eeg Data ◽

Machine Learning Approach ◽

Ssri Treatment

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GWAS-based machine learning approach to predict duloxetine response in major depressive disorder

Journal of Psychiatric Research ◽

10.1016/j.jpsychires.2017.12.009 ◽

2018 ◽

Vol 99 ◽

pp. 62-68 ◽

Cited By ~ 21

Author(s):

Malgorzata Maciukiewicz ◽

Victoria S. Marshe ◽

Anne-Christin Hauschild ◽

Jane A. Foster ◽

Susan Rotzinger ◽

...

Keyword(s):

Machine Learning ◽

Major Depressive Disorder ◽

Depressive Disorder ◽

Learning Approach ◽

Major Depressive ◽

Machine Learning Approach

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Destination Image of DMO and UGC on Instagram: A Machine-Learning Approach

Information and Communication Technologies in Tourism 2022 ◽

10.1007/978-3-030-94751-4_31 ◽

2022 ◽

pp. 343-355

Author(s):

Roman Egger ◽

Oguzcan Gumus ◽

Elza Kaiumova ◽

Richard Mükisch ◽

Veronika Surkic

Keyword(s):

Machine Learning ◽

Social Media ◽

Destination Image ◽

User Generated Content ◽

Learning Approach ◽

Projected Image ◽

Machine Learning Approach ◽

Destination Brand ◽

Practical Implications ◽

Insight Into

AbstractSocial media plays a key role in shaping the image of a destination. Although recent research has investigated factors influencing online users’ perception towards destination image, limited studies encompass and compare social media content shared by tourists and destination management organisations (DMOs) at the same time. This paper aims to determine whether the projected image of DMOs corresponds with the destination image perceived by tourists. By taking the Austrian Alpine resort Saalbach-Hinterglemm as a case, a netnographic approach was applied to analyse the visual and textual posts of DMO and user-generated content (UGC) on Instagram using machine learning. The findings reveal themes that are not covered in the posts published by marketers but do appear in UGC. This study adds to the existing literature by providing a deeper insight into destination image formation and uses a qualitative approach to assess destination brand image. It further highlights practical implications for the industry regarding DMOs’ social media marketing strategy.

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Predicting the Mechanical Properties of Zeolite Frameworks by Machine Learning

10.26434/chemrxiv.5349151.v1 ◽

2017 ◽

Author(s):

Jack D. Evans ◽

François-xavier Coudert

Keyword(s):

Machine Learning ◽

Elastic Moduli ◽

Elastic Response ◽

Local Geometry ◽

Learning Approach ◽

Shear Moduli ◽

Machine Learning Approach ◽

Characteristic Features ◽

Field Approaches ◽

Insight Into

We show here that machine learning is a powerful new tool for predicting the elastic response of zeolites. We built our machine learning approach relying on geometric features only, which are related to local geometry, structure and porosity of a zeolite, to predict bulk and shear moduli of zeolites with an accuracy exceeding that of force field approaches. The development of this model has illustrated clear correlations between characteristic features of a zeolite and elastic moduli providing exceptional insight into the mechanics of zeolitic frameworks. Finally, we employ this methodology to predict the elastic response of 590 448 hypothetical zeolites, and the results of this massive database provide clear evidence to stability trends in porous materials.

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