Enhancing early depression detection with AI: a comparative use of NLP models

Figures & data

Figure 1. Overview of presented depression detection pipeline.

Table 1. Dataset class distribution.

Dataset	depressed	non-depressed
DAIC-WOZ	56	133
Extended-DAIC	66	209

Table 2. BERT model parameters.

Model	BERT-Base uncased
Environment	12-layer, 768-hidden, 12-heads
Parameters	110 M
Batch size	16
Length embedding	27
Epochs	20
Optimizer	AdamW
Learning rate	3e-5
Dropout hidden	0.3
Dropout attention	0.3

Table 3. An example prompt used to predict the PHQ-8 scores of the participants using GPT-based models.

Prompt	Give me one score (0 to 24 like the PHQ-8) for the whole interview. Only look at the Participant's answers. 0 is no depression, 24 is a severe depression. If the Score is greater or equal 10 the Participant is classified as 1 depressive. Only give me the score as an INTEGER WITHOUT EXPLANATION!
	[Interview]
	Interviewer: I'm going to ask you a battery of questions that all relate to the past seven weeks. Is it okay with you? Participant: Yes Interviewer: How was your mood during the past week? ··· .
Output:	9

Table 4. Rate limits comparison between gpt-3.5 and gpt-4 models.

Model	TPM${}^{\mathrm{a}}$	RPM${}^{\mathrm{b}}$
GPT-3.5-turbo	160,000	5000
GPT-4	10,000	200

${}^{\mathrm{a}}$Tokens per minute.${}^{\mathrm{b}}$Requests per minute.

Table 5. Experimental results and comparison.

Work	Precision	Recall	F1 score
DAIC-WOZ dataset N = 42
Villatoro-Tello (BERT)[39]	.59	.59	.59
Senn et al. (BERT) [40]			.60
Danner et al. (BERT) [6]	.63	.66	.64
Danner et al. (GPT-3.5) [6]	.78	.79	.78
Danner et al. (ChatGPT-4) [6]	.70	.60	.61
Ours (GPT-4)	0.81	0.70	.71
Simulated dataset N = 5
Danner et al. (BERT) [6]	.68	.41	.43
Ours (GPT-4)	1.00	1.00	1.00
KID dataset N = 10
Ours (BERT)	.76	.71	.73
Ours (GPT-3.5)	.83	.70	.73
Ours (GPT-4)	.87	.87	.87

Villatoro-Tello E, Ramirez-de-la Rosa G, Gática-Pérez D, et al. Approximating the mental lexicon from clinical interviews as a support tool for depression detection. In: Proceedings of the 2021 International Conference on Multimodal Interaction; 2021; p. 557–566.

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Senn S, Tlachac M, Flores R, et al. Ensembles of bert for depression classification. In: 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC); 2022; p. 4691–4694.

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Danner M, Hadzic B, Gerhardt S, et al. Advancing mental health diagnostics: Gpt-based method for depression detection. In: Proceedings Title; 62nd Annual Conference of the Society of Instrument and Control Engineers of Japan (SICE); Tsu City, Japan; 2023; p. 1290–1296.

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