Detecting Bipolar Disorder from Misdiagnosed Major Depressive Disorder with Mood-Aware Multi-Task Learning
This codebase contains the python scripts for the model for the NAACL 2024. https://aclanthology.org/2024.naacl-long.278/
Python 3.8 & Pytorch 1.12
pip install -r requirements.txt
Execute the following steps in the same environment:
cd Detecting-BD-from-Misdiagnosed-MDD_NAACL_2024 & python main.py
Processed dataset format should be a DataFrame as a .json file having the following columns:
- window_id : unique id
- mood_label : mood level -3 to 3.
- trans_730_y : diagnosis type 0(MDD) and 1(BD).
- sen_enc : list of lists consisting of 1024-dimensional encoding for each reddit post.
- m_change_1st : mood variation -6 to 6.
To label the collected Reddit dataset, we recruited three researchers, who are knowledgeable in psychology and fluent in English, as annotators. With the supervision of a psychiatrist, the three trained annotators labeled 1,025 users and their 7,346 anonymized Reddit posts using the open-source text annotation tool Doccano. During annotations, we mainly consider two different label categories: (i) Diagnosis Type (e.g., MDD, BD) and (ii) BD Mood Level with a scale ranging from -3 to 3. If there is any conflict in the annotated labels across the annotators, all the annotators discuss and reach to an agreement under the supervision of the psychiatrists.
We carefully consider potential ethical issues in this work: (i) protecting users' privacies on Reddit and (ii) avoiding potentially harmful uses of the proposed dataset. The Reddit privacy policy explicitly authorizes third parties to copy user content through the Reddit API. We follow the widely-accepted social media research ethics policies that allow researchers to utilize user data without explicit consent if anonymity is protected (benton et al. 2017; Williams et al., 2017). Any metadata that could be used to specify the author was not collected. In addition, all content is manually scanned to remove personally identifiable information and mask all the named entities. More importantly, the BD dataset will be shared only with other researchers who have agreed to the ethical use of the dataset. This study was reviewed and approved by the Institutional Review Board (SKKU2022-11-038).
While it is important to ensure that all necessary precautions are taken, we are enthusiastic about sharing this valuable resource with fellow researchers. To request access to the dataset, please contact Daeun Lee (delee12@skku.edu). Access requests should follow the format of the sample application provided below, which consists of three parts:
- Part 0: Download a sample application form (https://sites.google.com/view/daeun-lee/dataset/naacl-2024)
- Part 1: Applicant Information
- Part 2: Dataset Access Application
- Part 3: Ethical Review by Your Organization
The dataset was produced at Sungkyunkwan University (SKKU) in South Korea, and the research conducted on this dataset at SKKU has been granted exemption from Institutional Review Board (IRB) evaluation by SKKU's IRB (SKKU2022-11-038). This exemption applies to the analysis of pre-existing data that is publicly accessible or involves individuals who cannot be directly identified or linked to identifiable information. Nevertheless, due to the potentially sensitive nature of this data, we require that researchers who receive the data obtain ethical approval from their respective organizations.
Please submit your access request to Daeun Lee (delee12@skku.edu) and ensure that you include all the necessary information and address the points outlined in the sample application.
Inspired by the data sharing system of previous research (Zirikly et al. 2019), we have decided to establish a governance process for researcher access to the dataset, following the procedure outlined below. Due to limitations in the number of available individuals, three out of the seven authors will be selected to review access requests submitted in the format specified below. The outcomes of the review will result in the following responses:
- Approval: If all three members give their approval, the application will be deemed approved, and Daeun will proceed to share the dataset with the researcher.
- Inquiries: The authors may have questions or seek clarification, prompting further communication.
- Revision and resubmission: Should the authors provide specific suggestions for revising and resubmitting the application, the researcher will have the opportunity to address them.
- Rejection: In the event of unanimous disapproval from the authors, the dataset will not be shared.
The authors will prioritize and promote diversity and inclusivity among the reviewers and the community of researchers utilizing the dataset.
Reference Zirikly, A., Resnik, P., Uzuner, O., & Hollingshead, K. (2019, June). CLPsych 2019 shared task: Predicting the degree of suicide risk in Reddit posts. In Proceedings of the sixth workshop on computational linguistics and clinical psychology (pp. 24-33)
@inproceedings{lee2024detecting,
title={Detecting Bipolar Disorder from Misdiagnosed Major Depressive Disorder with Mood-Aware Multi-Task Learning},
author={Lee, Daeun and Jeon, Hyolim and Son, Sejung and Park, Chaewon and hyun An, Ji and Kim, Seungbae and Han, Jinyoung},
booktitle={Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)},
pages={4954--4970},
year={2024}
}
This research was supported by the National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIT) (No. 2023R1A2C2007625), and by the MSIT (Ministry of Science and ICT), Korea, under the ICAN (ICT Challenge and Advanced Network of HRD) support program (IITP-2024-RS-2023-00259497) supervised by the IITP(Institute for Information & Communications Technology Planning & Evaluation).
Data Science & Artificial Intelligence Laboratory (DSAIL) @ Sungkyunkwan University