Description Module

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Description Module

The Description Module contains narrative descriptions of the clinical trial, including a brief summary and detailed description. These descriptions provide important information about the study's purpose, methodology, and key details in language accessible to both researchers and the general public.

Description Module path is as follows:

Study -> Protocol Section -> Description Module

Description Module

Ignite Creation Date: 2025-12-25 @ 1:38 AM
Ignite Modification Date: 2025-12-25 @ 1:38 AM
NCT ID: NCT05123794
Brief Summary: Participants will be asked to fill out an online questionnaire about their demographics information and all 42 items from the Depression Anxiety Stress Scale (DASS-42). A series of machine learning techniques will be applied to the dataset to develop a shortened assessment using the most important demographics and DASS-42 items from the original questionnaire, to predict depression levels indicated by DASS-42.
Detailed Description: Clinical depression affects 5-10% of the world population each year and is a serious mental health issue globally. There are many traditional psychological scales that assess levels of depression in adults, where their items are often redundant in the information they carry, and their scoring is not necessarily linear to the item scores. Thus, machine learning techniques can help find the redundancy in the items, as well as the nonlinear relationship between the item scores and the final prediction. Using the Depression Anxiety Stress Scale 42 (DASS-42) as the basis, participants will be asked to fill out an online questionnaire about their demographics information (age, gender, country of residence, race, etc.) and all 42 items of DASS-42 to provide a dataset for this study. Feature selection techniques such as MRMR and Gini feature importance were applied to identify the most important features in the dataset. Then, using machine learning methods such as Logistic Regression, XGBoost, and Ensemble models, models will be fitted on the most important features to develop a shortened depression scale (7-9 items consisting of demographics items and DASS items) that accurately predicted the levels of depression (as measured by the AUC, ROC and F1 scores.
Study: NCT05123794
Study Brief:
Protocol Section: NCT05123794