Neuropsychiatric Symptoms and Commonly Used Biomarkers of Alzheimer's Disease: A Literature Review from a Machine Learning Perspective
J Shah, MM Rahman Siddiquee… - Journal of …, 2023 - content.iospress.com
There is a growing interest in the application of machine learning (ML) in Alzheimer's
disease (AD) research. However, neuropsychiatric symptoms (NPS), frequent in subjects …
disease (AD) research. However, neuropsychiatric symptoms (NPS), frequent in subjects …
[HTML][HTML] AIDA: Artificial intelligence based depression assessment applied to Bangladeshi students
Depression is a common psychiatric disorder that is becoming more prevalent in developing
countries like Bangladesh. Depression has been found to be prevalent among youths and …
countries like Bangladesh. Depression has been found to be prevalent among youths and …
[HTML][HTML] Identifying depression in the United States veterans using deep learning algorithms, NHANES 2005–2018
Z Qu, Y Wang, D Guo, G He, C Sui, Y Duan, X Zhang… - BMC psychiatry, 2023 - Springer
Background Depression is a common mental health problem among veterans, with high
mortality. Despite the numerous conducted investigations, the prediction and identification of …
mortality. Despite the numerous conducted investigations, the prediction and identification of …
[HTML][HTML] A novel diabetic foot ulcer diagnostic model: identification and analysis of genes related to glutamine metabolism and immune infiltration
H Shi, X Yuan, X Yang, R Huang, W Fan, G Liu - BMC genomics, 2024 - Springer
Background Diabetic foot ulcer (DFU) is one of the most common and severe complications
of diabetes, with vascular changes, neuropathy, and infections being the primary …
of diabetes, with vascular changes, neuropathy, and infections being the primary …
[HTML][HTML] Identification and immuno-infiltration analysis of cuproptosis regulators in human spermatogenic dysfunction
M Zhao, WX Yu, SJ Liu, YJ Deng, ZW Zhao… - Frontiers in …, 2023 - frontiersin.org
Introduction: Cuproptosis seems to promote the progression of diverse diseases. Hence, we
explored the cuproptosis regulators in human spermatogenic dysfunction (SD), analyzed the …
explored the cuproptosis regulators in human spermatogenic dysfunction (SD), analyzed the …
[HTML][HTML] Prediction of depressive symptoms severity based on sleep quality, anxiety, and gray matter volume: a generalizable machine learning approach across three …
Background Depressive symptoms are rising in the general population, but their associated
factors are unclear. Although the link between sleep disturbances and depressive symptoms …
factors are unclear. Although the link between sleep disturbances and depressive symptoms …
Review of Class Imbalance Dataset Handling Techniques for Depression Prediction and Detection
S Ndaba - Available at SSRN 4387416, 2023 - papers.ssrn.com
Depression is a prevailing mental disturbance affecting an individual's thinking and mental
development. There have been much research demonstrating effective automated …
development. There have been much research demonstrating effective automated …
[HTML][HTML] The usefulness of machine learning analysis for predicting the presence of depression with the results of the Korea National Health and Nutrition Examination …
SW Kim, MC Chang - Annals of Palliative Medicine, 2023 - apm.amegroups.org
Background: Depression is a major public health concern, with an estimated 10.8% of adults
experiencing depression. Depression can have a significant impact on an individual's …
experiencing depression. Depression can have a significant impact on an individual's …
Ensemble Learning to Identify Depression Indicators for Korean Farmers
J Park, H Ahn, K Youn, M Lee, S Hong - IEEE Access, 2023 - ieeexplore.ieee.org
Understanding the factors contributing to depression in farmers is crucial for ensuring their
well-being and productivity. To address this issue, our study delves into depression factors …
well-being and productivity. To address this issue, our study delves into depression factors …
Causality analysis and prediction of riverine algal blooms by combining empirical dynamic modeling and machine learning techniques
River algal blooms have become a global environmental problem due to their large impact
range and environmental hazards. However, the complex mechanisms underlying these …
range and environmental hazards. However, the complex mechanisms underlying these …