Domain knowledge enhanced text mining for identifying mental disorder patterns

Maryam Shahabikargar; Amin Beheshti; Amin Khatami; Ricky Nguyen; Xuyun Zhang; Hamid Alinejad-Rokny

doi:10.1109/DSAA54385.2022.10032346

Domain knowledge enhanced text mining for identifying mental disorder patterns

Maryam Shahabikargar^*, Amin Beheshti, Amin Khatami, Ricky Nguyen, Xuyun Zhang, Hamid Alinejad-Rokny

^*Corresponding author for this work

School of Computing

Research output: Chapter in Book/Report/Conference proceeding › Conference proceeding contribution › peer-review

1 Citation (Scopus)

Abstract

Mental health disorders may cause severe consequences for countries' economies and health. Identifying early signs of these disorders is vital. The state-of-the-art research in identifying mental health disorder patterns from textual data, uses hand-labeled training sets, especially when a domain expert's knowledge is required to analyze various symptoms in a patient. This task could be time-consuming and expensive. To address this challenge, in this paper, we study and analyze the various clinical and non-clinical approaches to identifying mental health disorders. We leverage the domain knowledge and expertise in cognitive science to build a domain-specific Knowledge Base for the mental health disorder concepts and patterns. We present a weaker form of supervision by facilitating and generating training data from a domain-specific Knowledge Base. We adopt a typical scenario for analyzing social media to identify depression symptoms from the textual content generated by social users.

Original language	English
Title of host publication	2022 IEEE 9th International Conference on Data Science and Advanced Analytics DSAA'2022
Subtitle of host publication	proceedings
Editors	Joshua Zhexue Huang, Yi Pan, Barbara Hammer, Muhammad Khurram Khan, Xing Xie, Laizhong Cui, Yulin He
Place of Publication	Piscataway, NJ
Publisher	Institute of Electrical and Electronics Engineers (IEEE)
Number of pages	10
ISBN (Electronic)	9781665473309, 9781655473309
ISBN (Print)	9781665473316
DOIs	https://doi.org/10.1109/DSAA54385.2022.10032346
Publication status	Published - 2022
Event	9th IEEE International Conference on Data Science and Advanced Analytics, DSAA 2022 - Shenzhen, China Duration: 13 Oct 2022 → 16 Oct 2022

Conference

Conference	9th IEEE International Conference on Data Science and Advanced Analytics, DSAA 2022
Country/Territory	China
City	Shenzhen
Period	13/10/22 → 16/10/22

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10.1109/DSAA54385.2022.10032346

Cite this

Shahabikargar, M., Beheshti, A., Khatami, A., Nguyen, R., Zhang, X., & Alinejad-Rokny, H. (2022). Domain knowledge enhanced text mining for identifying mental disorder patterns. In J. Z. Huang, Y. Pan, B. Hammer, M. K. Khan, X. Xie, L. Cui, & Y. He (Eds.), 2022 IEEE 9th International Conference on Data Science and Advanced Analytics DSAA'2022: proceedings Institute of Electrical and Electronics Engineers (IEEE). https://doi.org/10.1109/DSAA54385.2022.10032346

Shahabikargar, Maryam ; Beheshti, Amin ; Khatami, Amin et al. / Domain knowledge enhanced text mining for identifying mental disorder patterns. 2022 IEEE 9th International Conference on Data Science and Advanced Analytics DSAA'2022: proceedings. editor / Joshua Zhexue Huang ; Yi Pan ; Barbara Hammer ; Muhammad Khurram Khan ; Xing Xie ; Laizhong Cui ; Yulin He. Piscataway, NJ : Institute of Electrical and Electronics Engineers (IEEE), 2022.

@inproceedings{b1be0620211f4c32911690c008bcbbf6,

title = "Domain knowledge enhanced text mining for identifying mental disorder patterns",

abstract = "Mental health disorders may cause severe consequences for countries' economies and health. Identifying early signs of these disorders is vital. The state-of-the-art research in identifying mental health disorder patterns from textual data, uses hand-labeled training sets, especially when a domain expert's knowledge is required to analyze various symptoms in a patient. This task could be time-consuming and expensive. To address this challenge, in this paper, we study and analyze the various clinical and non-clinical approaches to identifying mental health disorders. We leverage the domain knowledge and expertise in cognitive science to build a domain-specific Knowledge Base for the mental health disorder concepts and patterns. We present a weaker form of supervision by facilitating and generating training data from a domain-specific Knowledge Base. We adopt a typical scenario for analyzing social media to identify depression symptoms from the textual content generated by social users.",

author = "Maryam Shahabikargar and Amin Beheshti and Amin Khatami and Ricky Nguyen and Xuyun Zhang and Hamid Alinejad-Rokny",

year = "2022",

doi = "10.1109/DSAA54385.2022.10032346",

language = "English",

isbn = "9781665473316",

editor = "Huang, \{Joshua Zhexue\} and Yi Pan and Barbara Hammer and Khan, \{Muhammad Khurram\} and Xing Xie and Laizhong Cui and Yulin He",

booktitle = "2022 IEEE 9th International Conference on Data Science and Advanced Analytics DSAA'2022",

publisher = "Institute of Electrical and Electronics Engineers (IEEE)",

address = "United States",

note = "9th IEEE International Conference on Data Science and Advanced Analytics, DSAA 2022 ; Conference date: 13-10-2022 Through 16-10-2022",

}

Shahabikargar, M, Beheshti, A, Khatami, A, Nguyen, R, Zhang, X & Alinejad-Rokny, H 2022, Domain knowledge enhanced text mining for identifying mental disorder patterns. in JZ Huang, Y Pan, B Hammer, MK Khan, X Xie, L Cui & Y He (eds), 2022 IEEE 9th International Conference on Data Science and Advanced Analytics DSAA'2022: proceedings. Institute of Electrical and Electronics Engineers (IEEE), Piscataway, NJ, 9th IEEE International Conference on Data Science and Advanced Analytics, DSAA 2022, Shenzhen, China, 13/10/22. https://doi.org/10.1109/DSAA54385.2022.10032346

Domain knowledge enhanced text mining for identifying mental disorder patterns. / Shahabikargar, Maryam; Beheshti, Amin; Khatami, Amin et al.
2022 IEEE 9th International Conference on Data Science and Advanced Analytics DSAA'2022: proceedings. ed. / Joshua Zhexue Huang; Yi Pan; Barbara Hammer; Muhammad Khurram Khan; Xing Xie; Laizhong Cui; Yulin He. Piscataway, NJ: Institute of Electrical and Electronics Engineers (IEEE), 2022.

Research output: Chapter in Book/Report/Conference proceeding › Conference proceeding contribution › peer-review

TY - GEN

T1 - Domain knowledge enhanced text mining for identifying mental disorder patterns

AU - Shahabikargar, Maryam

AU - Beheshti, Amin

AU - Khatami, Amin

AU - Nguyen, Ricky

AU - Zhang, Xuyun

AU - Alinejad-Rokny, Hamid

PY - 2022

Y1 - 2022

N2 - Mental health disorders may cause severe consequences for countries' economies and health. Identifying early signs of these disorders is vital. The state-of-the-art research in identifying mental health disorder patterns from textual data, uses hand-labeled training sets, especially when a domain expert's knowledge is required to analyze various symptoms in a patient. This task could be time-consuming and expensive. To address this challenge, in this paper, we study and analyze the various clinical and non-clinical approaches to identifying mental health disorders. We leverage the domain knowledge and expertise in cognitive science to build a domain-specific Knowledge Base for the mental health disorder concepts and patterns. We present a weaker form of supervision by facilitating and generating training data from a domain-specific Knowledge Base. We adopt a typical scenario for analyzing social media to identify depression symptoms from the textual content generated by social users.

AB - Mental health disorders may cause severe consequences for countries' economies and health. Identifying early signs of these disorders is vital. The state-of-the-art research in identifying mental health disorder patterns from textual data, uses hand-labeled training sets, especially when a domain expert's knowledge is required to analyze various symptoms in a patient. This task could be time-consuming and expensive. To address this challenge, in this paper, we study and analyze the various clinical and non-clinical approaches to identifying mental health disorders. We leverage the domain knowledge and expertise in cognitive science to build a domain-specific Knowledge Base for the mental health disorder concepts and patterns. We present a weaker form of supervision by facilitating and generating training data from a domain-specific Knowledge Base. We adopt a typical scenario for analyzing social media to identify depression symptoms from the textual content generated by social users.

UR - https://www.scopus.com/pages/publications/85148540853

UR - http://purl.org/au-research/grants/arc/DE210101458

U2 - 10.1109/DSAA54385.2022.10032346

DO - 10.1109/DSAA54385.2022.10032346

M3 - Conference proceeding contribution

SN - 9781665473316

BT - 2022 IEEE 9th International Conference on Data Science and Advanced Analytics DSAA'2022

A2 - Huang, Joshua Zhexue

A2 - Pan, Yi

A2 - Hammer, Barbara

A2 - Khan, Muhammad Khurram

A2 - Xie, Xing

A2 - Cui, Laizhong

A2 - He, Yulin

PB - Institute of Electrical and Electronics Engineers (IEEE)

CY - Piscataway, NJ

T2 - 9th IEEE International Conference on Data Science and Advanced Analytics, DSAA 2022

Y2 - 13 October 2022 through 16 October 2022

ER -

Shahabikargar M, Beheshti A, Khatami A, Nguyen R, Zhang X, Alinejad-Rokny H. Domain knowledge enhanced text mining for identifying mental disorder patterns. In Huang JZ, Pan Y, Hammer B, Khan MK, Xie X, Cui L, He Y, editors, 2022 IEEE 9th International Conference on Data Science and Advanced Analytics DSAA'2022: proceedings. Piscataway, NJ: Institute of Electrical and Electronics Engineers (IEEE). 2022 doi: 10.1109/DSAA54385.2022.10032346

Domain knowledge enhanced text mining for identifying mental disorder patterns

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DE21 : Scalable and Deep Anomaly Detection from Big Data with Similarity Hashing

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Domain knowledge enhanced text mining for identifying mental disorder patterns

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DE21 : Scalable and Deep Anomaly Detection from Big Data with Similarity Hashing

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