نوع مقاله : مقاله پژوهشی

نویسندگان

1 گروه مهندسی کامپیوتر، واحد رشت، دانشگاه آزاد اسلامی، رشت، ایران.

2 گروه روانشناسی، واحد رشت، دانشگاه آزاد اسلامی، رشت، ایران.

چکیده

هدف: روند تشخیص اختلال روانی در رویکرد‌های سنتی، متکی بر پرسشنامه، مصاحبه و بررسی‌های بالینی است؛ درحالی‌که ابزارهای غربالگری خودکار می‌توانند مسیر کوتاه‌تری را طی کنند و به‌عنوان استراتژی‌های ارزیابی نوین، سیستم‌های پشتیبان تصمیم و راهبردهای پیشگیری برای کمک به افراد مستعد توسعه یابند. با توجه به تمایل افراد به اشتراک‌گذاری افکار و احساسات در سکو‌های اجتماعی، داده‌های میکروبلاگینگ حاوی اطلاعات ارزشمندی هستند که می‌توانند برای شناسایی حالات روانی مورد تحلیل قرار گیرند. هدف از این پژوهش تشریح سازوکار تحلیل داده در زمینه مورد بحث است.
روش‌شناسی پژوهش: در این مقاله، در ابتدا مفاهیمی مانند سلامت روان الکترونیک و سکوهای میکروبلاگینگ معرفی شده و با ارائه توضیحاتی در خصوص علم داده و تحلیل داده اجتماعی، ارتباط مفاهیم با یکدیگر مورد بحث قرار می‌گیرد. در ادامه در قالب بخشی جداگانه، پیش‌بینی اختلال در شبکه‌های اجتماعی شرح داده می‌شود. در نهایت با بررسی سوابق تحقیق و مسائل باز، به چگونگی جمع‌آوری داده، پیش‌پردازش و روند استفاده از ویژگی‌های متفاوت به کمک ابزارهای تحلیل گوناگون می‌پردازیم.
یافته‌ها: این پژوهش با پیاده‌سازی نمونه‌ای کاربردی از تجزیه‌وتحلیل داده اجتماعی روی داده‌های دنیای واقعی نشان می‌دهد، ویژگی‌های استخراج شده از نمایه کاربر، تأثیر قابل توجهی در پیش‌بینی علائم افسردگی دارند و حتی می‌توان با اطلاعات استخراج شده از نمایه عمومی کاربر، وضعیت روانی را با دقتی مناسب پیش‌بینی نمود.
اصالت/ارزش افزوده علمی: در این پژوهش چگونگی تحلیل خودکار داده اجتماعی با هدف شناسایی اختلال روانی شرح داده شده و در پیاده‌سازی مشخص می‌شود که علائم تقریباً در تمام ویژگی‌های مورد مطالعه قابل پیگیری هستند.

کلیدواژه‌ها

موضوعات

عنوان مقاله [English]

Developing Clinical Decision Support Systems in Psychiatry Using Microblogging Data

نویسندگان [English]

  • Ramin Safa 1
  • Peyman Bayat 1
  • Leila Moghtader 2

1 Department of Computer Engineering, Rasht Branch, Islamic Azad University, Rasht, Iran.

2 Department of Psychology, Rasht Branch, Islamic Azad University, Rasht, Iran.

چکیده [English]

Purpose: While diagnosing mental disorders in traditional approaches relies on questionnaires, interviews, and clinical trials, automated screening tools can take a shorter path. These tools can be developed as innovative evaluation techniques, decision support systems, and prevention strategies to help susceptible individuals. Due to the tendency of people to share thoughts and feelings on social platforms, microblogging data contains valuable information that can be analyzed to identify users’ mental states. This study describes a roadmap for data analysis in the field in question.
Methodology: In the first part of this paper, concepts such as electronic mental health and microblogging platforms are introduced. And their conceptual relationship is discussed by providing explanations about data science and social data analysis. Next, the prediction of disorder in social platforms is described separately. Finally, by reviewing related works and open issues, we explain how data collection, pre-processing, and analysis are done using different features of real-world data.
Findings: By experimental analysis, this study shows that the extracted features from the users’ profiles have a significant effect on predicting mental health symptoms, and even with the information extracted from the users’ public profiles, the mental state can be predicted with reasonable accuracy.
Originality/Value: In this study, automatic analysis of social data to investigate the psychological signals is described, and in the implementation section, it is founded that the symptoms can be followed in almost all the studied features.

کلیدواژه‌ها [English]

  • Social network analysis
  • Electronic mental health
  • Clinical decision support systems
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