Exploration of Adolescent Depression Risk Prediction Based on Census Surveys and General Life Issues

• URL: http://arxiv.org/abs/2401.03171v1
• Date: Sat, 6 Jan 2024 09:14:25 GMT
• Title: Exploration of Adolescent Depression Risk Prediction Based on Census Surveys and General Life Issues
• Authors: Qiang Li, Yufeng Wu, Zhan Xu, Hefeng Zhou
• Abstract summary: The prevalence of depression among adolescents is steadily increasing. Traditional diagnostic methods, which rely on scales or interviews, prove particularly inadequate for detecting depression in young people. We introduce a method for managing severely imbalanced high-dimensional data and an adaptive predictive approach tailored to data structure characteristics.
• Score: 7.774933303698165
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In contemporary society, the escalating pressures of life and work have propelled psychological disorders to the forefront of modern health concerns, an issue that has been further accentuated by the COVID-19 pandemic. The prevalence of depression among adolescents is steadily increasing, and traditional diagnostic methods, which rely on scales or interviews, prove particularly inadequate for detecting depression in young people. Addressing these challenges, numerous AI-based methods for assisting in the diagnosis of mental health issues have emerged. However, most of these methods center around fundamental issues with scales or use multimodal approaches like facial expression recognition. Diagnosis of depression risk based on everyday habits and behaviors has been limited to small-scale qualitative studies. Our research leverages adolescent census data to predict depression risk, focusing on children's experiences with depression and their daily life situations. We introduced a method for managing severely imbalanced high-dimensional data and an adaptive predictive approach tailored to data structure characteristics. Furthermore, we proposed a cloud-based architecture for automatic online learning and data updates. This study utilized publicly available NSCH youth census data from 2020 to 2022, encompassing nearly 150,000 data entries. We conducted basic data analyses and predictive experiments, demonstrating significant performance improvements over standard machine learning and deep learning algorithms. This affirmed our data processing method's broad applicability in handling imbalanced medical data. Diverging from typical predictive method research, our study presents a comprehensive architectural solution, considering a wider array of user needs.

Related papers

• A Survey of Models for Cognitive Diagnosis: New Developments and Future Directions [66.40362209055023]
  This paper aims to provide a survey of current models for cognitive diagnosis, with more attention on new developments using machine learning-based methods. By comparing the model structures, parameter estimation algorithms, model evaluation methods and applications, we provide a relatively comprehensive review of the recent trends in cognitive diagnosis models.
  arXiv  Detail & Related papers  (2024-07-07T18:02:00Z)
• Assessing ML Classification Algorithms and NLP Techniques for Depression Detection: An Experimental Case Study [0.6524460254566905]
  Depression has affected millions of people worldwide and has become one of the most common mental disorders. Recent research has evidenced that machine learning (ML) and Natural Language Processing (NLP) tools and techniques have significantly been used to diagnose depression. However, there are still several challenges in the assessment of depression detection approaches in which other conditions such as post-traumatic stress disorder (PTSD) are present.
  arXiv  Detail & Related papers  (2024-04-03T19:45:40Z)
• EmoScan: Automatic Screening of Depression Symptoms in Romanized Sinhala Tweets [0.0]
  This work explores the utilization of Romanized Sinhala social media data to identify individuals at risk of depression. A machine learning-based framework is presented for the automatic screening of depression symptoms by analyzing language patterns, sentiment, and behavioural cues.
  arXiv  Detail & Related papers  (2024-03-28T10:31:09Z)
• Harnessing the Power of Hugging Face Transformers for Predicting Mental Health Disorders in Social Networks [0.0]
  This study explores how user-generated data can be used to predict mental disorder symptoms. Our study compares four different BERT models of Hugging Face with standard machine learning techniques. New models outperform the previous approach with an accuracy rate of up to 97%.
  arXiv  Detail & Related papers  (2023-06-29T12:25:19Z)
• Incomplete Multimodal Learning for Complex Brain Disorders Prediction [65.95783479249745]
  We propose a new incomplete multimodal data integration approach that employs transformers and generative adversarial networks. We apply our new method to predict cognitive degeneration and disease outcomes using the multimodal imaging genetic data from Alzheimer's Disease Neuroimaging Initiative cohort.
  arXiv  Detail & Related papers  (2023-05-25T16:29:16Z)
• A Survey on Computer Vision based Human Analysis in the COVID-19 Era [58.79053747159797]
  The emergence of COVID-19 has had a global and profound impact, not only on society as a whole, but also on the lives of individuals. Various prevention measures were introduced around the world to limit the transmission of the disease, including face masks, mandates for social distancing and regular disinfection in public spaces, and the use of screening applications. These developments triggered the need for novel and improved computer vision techniques capable of (i) providing support to the prevention measures through an automated analysis of visual data, on the one hand, and (ii) facilitating normal operation of existing vision-based services, such as biometric authentication
  arXiv  Detail & Related papers  (2022-11-07T17:20:39Z)
• Bias Reducing Multitask Learning on Mental Health Prediction [18.32551434711739]
  There has been an increase in research in developing machine learning models for mental health detection or prediction. In this work, we aim to perform a fairness analysis and implement a multi-task learning based bias mitigation method on anxiety prediction models. Our analysis showed that our anxiety prediction base model introduced some bias with regards to age, income, ethnicity, and whether a participant is born in the U.S. or not.
  arXiv  Detail & Related papers  (2022-08-07T02:28:32Z)
• Data-Centric Epidemic Forecasting: A Survey [56.99209141838794]
  This survey delves into various data-driven methodological and practical advancements. We enumerate the large number of epidemiological datasets and novel data streams that are relevant to epidemic forecasting. We also discuss experiences and challenges that arise in real-world deployment of these forecasting systems.
  arXiv  Detail & Related papers  (2022-07-19T16:15:11Z)
• Practical Challenges in Differentially-Private Federated Survival Analysis of Medical Data [57.19441629270029]
  In this paper, we take advantage of the inherent properties of neural networks to federate the process of training of survival analysis models. In the realistic setting of small medical datasets and only a few data centers, this noise makes it harder for the models to converge. We propose DPFed-post which adds a post-processing stage to the private federated learning scheme.
  arXiv  Detail & Related papers  (2022-02-08T10:03:24Z)
• Deep Multi-task Learning for Depression Detection and Prediction in Longitudinal Data [50.02223091927777]
  Depression is among the most prevalent mental disorders, affecting millions of people of all ages globally. Machine learning techniques have shown effective in enabling automated detection and prediction of depression for early intervention and treatment. We introduce a novel deep multi-task recurrent neural network to tackle this challenge, in which depression classification is jointly optimized with two auxiliary tasks.
  arXiv  Detail & Related papers  (2020-12-05T05:14:14Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.