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REVIEW 4 major objections 6 minor 1 cited by

A Comprehensive Evaluation of Large Language Models on Mental Illnesses in Arabic Context

T0 review · 4 major / 6 minor · reviewed 2026-08-10 · deepseek-v4-flash

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Pith's one-line read Structured prompts outperform loose prompts by 14.5 points on multi-class Arabic mental-health classification, mainly through better instruction following.

desk verdict A solid, transparent benchmark for LLMs on Arabic mental-health text, with real label-quality caveats and an overbroad few-shot claim. read the letter →

arxiv 2501.06859 v1 pith:5BTJA46R submitted 2025-01-12 cs.CL cs.AI

Noureldin Zahran , Aya E. Fouda , Radwa J. Hanafy , Mohammed E. Fouda This is my paper · ORCID
classification cs.CLcs.AI
keywords largelanguagemodelsArabicmentalhealthNLPpromptengineeringfew-shotpromptingcross-lingualevaluationpsychiatricdiagnosticsinstructionfollowingbalancedaccuracy
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

The paper evaluates eight LLMs on Arabic mental-health datasets, both native Arabic corpora and English datasets machine-translated into Arabic, to find what drives diagnostic accuracy. Its central claim is that prompt structure is a major lever: a structured, explicitly formatted prompt beats a looser 'act as a psychologist' prompt by an average of 14.5 balanced-accuracy points on multi-class tasks, and this gap almost entirely disappears when only responses that both prompts can parse are compared. Model choice matters more than language: Phi-3.5 MoE leads in balanced accuracy, Mistral NeMo leads in severity error, and translating datasets from English to Arabic costs only about 6% accuracy on average. Few-shot prompting with one example per class consistently helps, boosting GPT-4o Mini by roughly 20% overall and 57.6% on multi-class tasks. The authors also show that label quality is a live threat: one public dataset was excluded after every model scored at or below chance, underscoring that benchmark labels must be audited before rankings are trusted.

What carries the argument

The central object is the controlled two-template prompt design with parse-validity filtering: for each task type, two semantically equivalent prompt templates differ only in structure and role framing, and a parsing pipeline classifies each model response as a valid label or invalid. By comparing performance on all responses versus only the subset where both prompts yield parsable output, the design isolates instruction-following failures from genuine diagnostic differences. A companion few-shot variant appends one example per class, and the same parsing and balanced-accuracy/MAE metrics are applied across all datasets and models.

What would settle it

Have expert clinicians re-annotate a random sample of ARADEPSU and MCD posts, then recompute the ZS-1 vs ZS-2 balanced-accuracy gap on the corrected labels; if the 14.5-point advantage disappears or reverses, the paper's headline prompt-engineering result is an artifact of label noise rather than instruction following.

Watch

Extended reading notes

Core claim

This study demonstrates that, for LLM-based psychiatric diagnosis in Arabic, how you prompt the model can shift performance as much as which model you choose. On multi-class datasets, a structured prompt with explicit formatting instructions outperformed a semantically identical but loosely structured 'act as a psychologist' prompt by 14.5 balanced-accuracy points; when only responses that both prompts could parse are compared, the gap collapses to 1.6 points, showing the difference is driven by instruction following, not clinical judgment. Model identity is the largest factor in diagnostic accuracy: Phi-3.5 MoE achieves the highest balanced accuracy, especially on binary tasks, while Mistral NeMo has the lowest mean absolute error on severity ratings. Language effects are modest, with English-native datasets beating their translated Arabic versions by about 6% on average, but translation quality and possible training-data leakage can create large artifacts, as seen in the 69% native-English advantage on MedMCQA. Few-shot prompting with one example per class improves performance consistently, with the largest gains on multi-class tasks.

Load-bearing premise

The evaluation assumes the ground-truth labels of the Arabic and translated datasets are accurate; if hidden labeling faults like those found in the excluded AMI dataset exist elsewhere, the model rankings and prompt-effect sizes could shift.

Editorial extensions

If this is right

  • Structured, explicitly formatted prompts should be the default for Arabic mental-health LLM tasks; loosely worded 'act as a psychologist' prompts lose about 14.5 balanced-accuracy points on multi-class datasets, mostly by producing unparseable responses.
  • Model choice outweighs language choice: Phi-3.5 MoE is the strongest option for balanced accuracy, and Mistral NeMo is the best pick when severity rating error is the priority.
  • One-example-per-class few-shot prompting is a cheap, consistent improvement, about 20% for GPT-4o Mini overall and 57.6% on multi-class tasks, and should be adopted before considering fine-tuning.
  • English-to-Arabic machine translation costs only about 6% balanced accuracy on average, but translation quality and possible training-data leakage can create artifacts larger than the language effect itself.
  • Arabic mental-health benchmark labels need independent auditing before results are trusted; the excluded AMI dataset shows that faulty labels can push every model to chance performance.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • The parsing-validity lens suggests that current Arabic mental-health LLM rankings partly measure output-formatting compliance; benchmarks that treat unparseable answers as errors rather than excluding them would narrow reported model gaps.
  • The modest language effect implies that well-translated English curricula could bootstrap Arabic psychiatric diagnostics, but the translation-quality bottleneck makes investment in native Arabic clinical corpora a higher-leverage intervention than further model tuning.
  • The MedMCQA native-English advantage is consistent with training-data memorization; a newly written Arabic clinical MCQ set that has never appeared online would separate true knowledge from leakage.
  • The AMI experience could be recycled as a cheap data-quality screen: if every tested model scores far below chance on a new dataset, suspect the labels before suspecting the models.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

4 major / 6 minor

Summary. The paper evaluates eight large language models on Arabic mental-health diagnostic tasks, combining native Arabic datasets with Google-translated versions of English datasets. It compares two zero-shot prompt templates and a few-shot variant across binary, multi-class, severity, and MCQ tasks, reporting balanced accuracy and normalized mean absolute error. The main findings are that the structured prompt ZS-2 outperforms the less structured ZS-1 largely by improving instruction following, with a 14.5-point balanced-accuracy advantage on multi-class datasets; that model choice is the dominant factor, with Phi-3.5 MoE best on balanced accuracy and Mistral NeMo best on severity MAE; that language/translation effects are modest; and that few-shot prompting helps, especially for GPT-4o Mini on multi-class tasks. The authors transparently report invalid-response rates, perform an intersection-of-valid-responses analysis, and exclude the AMI dataset after discovering widespread labeling faults.

Significance. If the findings hold, the paper offers useful practical guidance for LLM-based mental-health screening in Arabic: prompt structure matters mainly through instruction following, smaller open models can be competitive, and cross-lingual evaluations need to be interpreted with translation-quality caveats. The study is valuable for its breadth—eight models, multiple dataset types, and both native and translated Arabic data—and for its unusually candid treatment of parsing and label-quality confounds, including the explicit analysis of the 14.5-point prompt advantage when only parsable responses are considered. However, the headline quantitative claims currently rest on unverified dataset labels and on point estimates without uncertainty quantification, so the conclusions should be treated as provisional until those load-bearing assumptions are addressed.

major comments (4)
  1. [§2.1, §2.1.5, §2.2, §2.3.2, Appendix 6.4] Ground-truth label reliability is a load-bearing assumption that is not established. Several retained datasets have no documented annotator expertise (DCAT, MCD, ARADEPSU, CAIRODEP, MDE) or use automated labels (SDCNL), and Appendix 6.4 shows that the AMI dataset was excluded only after all models scored below 50% balanced accuracy and manual inspection revealed widespread faulty labeling. Because every model ranking and prompt-effect estimate in Tables 5–9 is computed against these labels, hidden errors of the same kind in any retained dataset could materially change the conclusions. The paper should audit labels on retained datasets, report any available annotator-agreement statistics, and/or run a sensitivity analysis excluding datasets with weak annotation provenance. In addition, the statement in Section 2.1 that 'dataset quality can later be assessed through the collective judgment of LLMs' is circular when the same LLMs are being scored.
  2. [§4.5, Table 9, Abstract] The few-shot claim is overgeneralized. The few-shot experiment covers only two models, Phi-3.5 MoE and GPT-4o Mini, yet the abstract states that 'few-shot prompting consistently improved performance.' Table 9 shows that Phi-3.5 MoE actually lost performance on binary tasks in both the ALL (-0.64) and AR (-1.27) groups, so 'consistently' is not supported even for the tested model. The claim should be restricted to the tested models and task types, or the experiment should be extended to more models before making a general statement.
  3. [§4.2, §4.3, §3.4] The headline quantities—the 14.5 BA multi-class prompt difference, the model ranking, and the language-effect averages—are reported as point estimates without confidence intervals, significance tests, or repeated runs. The 'random fluctuation threshold' of 5% in Section 4.2 is ad hoc, and the evaluation is itself a sample (Section 3.4). Since API outputs can be stochastic and the samples are finite, the paper should provide uncertainty measures (e.g., bootstrap confidence intervals or per-seed variances) for at least the abstract-level claims, or explicitly label them as exploratory. Without this, statements such as 'significantly influences' and 'crucial' go beyond what the data demonstrate.
  4. [§4.4.1, §4.4.3, §5] The conclusion that language influence is modest is confounded by machine translation quality. All translated corpora were produced by Google Translate with no human evaluation or translation-quality metric, and Section 4.4.2 shows that different translations of the same source (Google Translate vs. BiMediX) differ by up to 16.7 BA. The final section acknowledges this limitation, but the language-effect claims in Sections 4.4.1 and 4.4.3 are still presented as substantive results. A translation-quality check on a sample (e.g., human adequacy/fluency scores or back-translation agreement) would be needed to separate language effects from translation artifacts.
minor comments (6)
  1. [§2.3.1, Table 2] The text says the final DREADDIT dataset contains 3,553 labeled segments, while Table 2 reports a sample size of 1,000 posts; please reconcile these numbers.
  2. [§2.2, Table 10] The MDE class counts in Table 10 (600 + 597 + 600 = 1,797) do not match the stated total of 1,800 records; please verify.
  3. [§3.4] The term 'fair random sampling' should be described as stratified sampling, and the paper should list which datasets were sampled and the actual sample sizes used, since several datasets in Table 10 have fewer than 1,000 instances.
  4. [§4.2, Table 4] The confusion-matrix subtraction display is difficult to interpret; please clarify whether the entries are ZS-1 minus ZS-2 or the reverse, and explain how the reported 2.8% increase in negative predictions is derived from the table.
  5. [Table 7] The 'Best' prompt row is described as the optimal prompt for each model-dataset pair; please clarify that this is an oracle choice and note that it can overstate the performance achievable without access to ground-truth labels.
  6. [References] Several dataset citations are incomplete or lack venue and publication details (e.g., references [22], [26], and [27]); please complete the bibliographic information.

Circularity Check

0 steps flagged · score 2.0 of 10

No material circularity; central claims are empirical measurements against external labels, with only minor non-load-bearing self-citations for prompt/model selection.

full rationale

The paper's central claims—ZS-2 outperforming ZS-1 by 14.5 BA on multi-class datasets, few-shot gains, Phi-3.5 MoE leading BA, Mistral NeMo leading MAE—are all computed from fresh API evaluations against external, previously published datasets (DCAT, MCD, ARADEPSU, DREADDIT, etc.), not derived from any fitted parameter or from the paper's own assumptions. The 14.5 figure is explicitly decomposed: on the intersection of parsable responses the ZS-1/ZS-2 difference is only 1.6, so the gap is arithmetically attributed to invalid-response/instruction-following rates, a within-paper measurement rather than a circular prediction. The only self-citations are Section 3.2 (prompt templates 'Based on earlier English evaluation work [18]') and Section 3.6 (models 'chosen based on their strong general performance observed in our previous English evaluation study [18]'); these inform experimental design but do not force any reported outcome, since all rankings and effect sizes come from the current evaluations. The Appendix 6.4 AMI exclusion, where low scores prompted manual inspection that found faulty labels, is an honest external validation step, not circularity. The sentence in Section 2.1 suggesting 'dataset quality can later be assessed through the collective judgment of LLMs' is circular in principle if deployed, but it is not used to derive any result; the paper instead relies on published labels and manual inspection. Label-quality uncertainty (e.g., MCD, ARADEPSU) is a validity/correctness risk, not a circularity.

Assumptions & free parameters 3 free parameters · 5 assumptions · 0 invented entities

This is an empirical evaluation paper, so the ledger consists of methodological assumptions rather than mathematical axioms or invented entities. The most critical assumptions concern label reliability, translation adequacy, and the parsing convention.

free parameters (3)
  • Random fluctuation threshold = 5%
    Used in Sections 4.2 and 4.4 to classify prompt and language differences as wins, ties, or losses; changes the reported win counts though not the average differences.
  • Evaluation sample size cap = 1,000
    Datasets larger than 1,000 are subsampled to this cap; the Hoeffding justification assumes simple random sampling, but the study uses class-balanced sampling.
  • Severity binarization threshold = minimum severity plus one
    Severity datasets are binarized by mapping the minimum severity score to 0 and all higher scores to 1 for the balanced accuracy analysis, which discards severity granularity.
assumptions (5)
  • domain assumption The ground-truth labels of the Arabic and translated datasets are reliable enough to serve as evaluation targets.
    The paper includes datasets with unverified or unspecified annotator expertise (e.g., DCAT, MCD, ARADEPSU) and later excludes AMI due to labeling faults, indicating this assumption can fail.
  • domain assumption Google Translate preserves the diagnostic content of mental health posts when translating between English and Arabic.
    Used for all translated datasets; the paper acknowledges translation quality may affect results and calls for expert translation in future work.
  • domain assumption The parsing rule correctly extracts the model's intended response, and invalid responses are treated as incorrect.
    The paper shows invalid response rates vary widely by prompt and model; the ZS-1 vs ZS-2 balanced accuracy gap largely disappears when restricting to parsable responses, so the parsing convention affects conclusions.
  • domain assumption Class-balanced sampling yields an evaluation set representative of the full dataset within the claimed margin.
    The Hoeffding bound cited assumes independent and identically distributed sampling; the actual stratified sample changes class proportions, so the probabilistic guarantee does not directly apply.
  • domain assumption The eight selected models are representative of current LLMs for Arabic mental health tasks.
    Model choice is the main driver of performance differences, so conclusions about the best model may not generalize to other models.

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Cite this review

Pith. "Pith review of A Comprehensive Evaluation of Large Language Models on Mental Illnesses in Arabic Context." pith.science (2026). https://pith.science/paper/5BTJA46R

@misc{pith2026250106859,
  author       = {Pith},
  title        = {Pith review of: A Comprehensive Evaluation of Large Language Models on Mental Illnesses in Arabic Context},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/5BTJA46R}},
  note         = {Machine review of arXiv:2501.06859}
}
read the original abstract

Mental health disorders pose a growing public health concern in the Arab world, emphasizing the need for accessible diagnostic and intervention tools. Large language models (LLMs) offer a promising approach, but their application in Arabic contexts faces challenges including limited labeled datasets, linguistic complexity, and translation biases. This study comprehensively evaluates 8 LLMs, including general multi-lingual models, as well as bi-lingual ones, on diverse mental health datasets (such as AraDepSu, Dreaddit, MedMCQA), investigating the impact of prompt design, language configuration (native Arabic vs. translated English, and vice versa), and few-shot prompting on diagnostic performance. We find that prompt engineering significantly influences LLM scores mainly due to reduced instruction following, with our structured prompt outperforming a less structured variant on multi-class datasets, with an average difference of 14.5\%. While language influence on performance was modest, model selection proved crucial: Phi-3.5 MoE excelled in balanced accuracy, particularly for binary classification, while Mistral NeMo showed superior performance in mean absolute error for severity prediction tasks. Few-shot prompting consistently improved performance, with particularly substantial gains observed for GPT-4o Mini on multi-class classification, boosting accuracy by an average factor of 1.58. These findings underscore the importance of prompt optimization, multilingual analysis, and few-shot learning for developing culturally sensitive and effective LLM-based mental health tools for Arabic-speaking populations.

Figures

Figures reproduced from arXiv: 2501.06859 by the authors.

Figure 1
Figure 1. Example of a formatted prompt (ZS-2) for binary depression classification. [PITH_FULL_IMAGE:figures/full_fig_p008_1.png] view at source ↗
Figure 2
Figure 2. Example of a formatted prompt (ZS-2) for a binary classification task on depression. [PITH_FULL_IMAGE:figures/full_fig_p008_2.png] view at source ↗
Figure 3
Figure 3. Percentage of invalid responses for each dataset-model trial using the ZS-1 Prompt. Columns with a maximum [PITH_FULL_IMAGE:figures/full_fig_p011_3.png] view at source ↗
Figures from the paper (11 more)
Figure 4
Figure 4. Figure 4: Percentage of invalid responses for each dataset-model trial using the ZS-2 Prompt. Columns with a maximum [PITH_FULL_IMAGE:figures/full_fig_p012_4.png]
Figure 6
Figure 6. Figure 6: Comparison of model performance on MAE for prompts ZS-1 and ZS-2 across severity datasets, showing [PITH_FULL_IMAGE:figures/full_fig_p015_6.png]
Figure 8
Figure 8. Figure 8: Comparison of MAE for datasets in their original English versions versus their English-to-Arabic translations. [PITH_FULL_IMAGE:figures/full_fig_p020_8.png]
Figure 9
Figure 9. Figure 9: Comparison of BA performance between translated English and original Arabic versions of the same dataset. [PITH_FULL_IMAGE:figures/full_fig_p023_9.png]
Figure 10
Figure 10. Figure 10: An example of a few-shot (FS-2) prompt for binary depression detection. The prompt includes multiple [PITH_FULL_IMAGE:figures/full_fig_p024_10.png]
Figure 11
Figure 11. Figure 11: An example of a few-shot (FS-2) prompt for binary depression detection. The prompt includes multiple [PITH_FULL_IMAGE:figures/full_fig_p025_11.png]
Figure 12
Figure 12. Figure 12: Comparison of BA performance for each model and dataset when switching from ZS-2 to FS-2. Each cell [PITH_FULL_IMAGE:figures/full_fig_p025_12.png]
Figure 13
Figure 13. Figure 13: Comparison of MAE performance for each model and dataset when switching from ZS-2 to FS-2. Each cell [PITH_FULL_IMAGE:figures/full_fig_p026_13.png]
Figure 6
Figure 6. Figure 6: summarizes the class distributions for all datasets used in this study. These distributions are essential for [PITH_FULL_IMAGE:figures/full_fig_p028_6.png]
Figure 14
Figure 14. Figure 14: Average response length (words) for each model, dataset pair on ZS-1. Columns with a maximum value < 5 [PITH_FULL_IMAGE:figures/full_fig_p030_14.png]
Figure 15
Figure 15. Figure 15: Average response length (words) for each model, dataset pair on ZS-2. Columns with a maximum value < 5 [PITH_FULL_IMAGE:figures/full_fig_p031_15.png]

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Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. A Comprehensive Review of Datasets for Clinical Mental Health AI Systems

    cs.CL 2025-08 conditional novelty 5.0 of 10

    A systematic catalog of 89 clinical mental health datasets and 16 synthetic datasets, with a gap analysis on access, culture, and modality.

Reference graph

Works this paper leans on

38 extracted references · 33 canonical work pages · cited by 1 Pith paper

  1. [1]

    Depression, 2023

    World Health Organization. Depression, 2023. Accessed: September 14, 2024

    work page 2023

  2. [2]

    Anxiety disorders, 2022

    World Health Organization. Anxiety disorders, 2022. Accessed: 2024-09-26

    work page 2022

  3. [3]

    World Health Organization, Geneva, 2022

    World Health Organization.World Mental Health Report: Transforming Mental Health for All. World Health Organization, Geneva, 2022. Accessed: 2024-09-26

    work page 2022

  4. [4]

    Challenges of health care in the middle east and north africa.https://www.arabbarometer

    Kathrin Thomas. Challenges of health care in the middle east and north africa.https://www.arabbarometer. org/wp-content/uploads/ABV_Health_Report_FINAL_Public-Opinion-2019.pdf,2019. ArabBarom- eter – Wave V, Topic Report – Health Care

    work page 2019

  5. [5]

    Depressioninpublicservantsofupperegypt: Gender-specific prevalence and determining factors.Journal of Prevention, 43:623–638, 2022

    EhabSalahEshakandTarekAhmedAbd-ElRahman. Depressioninpublicservantsofupperegypt: Gender-specific prevalence and determining factors.Journal of Prevention, 43:623–638, 2022

    work page 2022

  6. [6]

    Depression and other common mental disorders: Global health estimates.https:// iris.who.int/bitstream/handle/10665/254610/WHO-MSD-MER-2017.2-eng.pdf?sequence=1, 2017

    World Health Organization. Depression and other common mental disorders: Global health estimates.https:// iris.who.int/bitstream/handle/10665/254610/WHO-MSD-MER-2017.2-eng.pdf?sequence=1, 2017. Licence: CC BY-NC-SA 3.0 IGO

    work page 2017

  7. [7]

    World Health Organization. Mental health atlas 2020 - member state profile: Egypt.https://cdn.who.int/ media/docs/default-source/mental-health/mental-health-atlas-2020-country-profiles/ egy.pdf?sfvrsn=4c708821_6&download=true, 2020. Licence: CC BY-NC-SA 3.0 IGO

    work page 2020

  8. [8]

    Mental health atlas

    World Health Organization. Mental health atlas. https://iris.who.int/bitstream/handle/10665/ 345946/9789240036703-eng.pdf?sequence=1, 2020

    work page 2020

Show all 38 references
  1. [9]

    Mental health discourse on reddit: Self-disclosure, social support, and anonymity

    Munmun De Choudhury and Sushovan De. Mental health discourse on reddit: Self-disclosure, social support, and anonymity. Proceedings of the International AAAI Conference on Web and Social Media, 8(1):71–80, May 2014. 26 LLMs Evaluation on Mental Illnesses in Arabic Context

    2014

  2. [10]

    Detecting depression and mental illness on social media: an integrative review.Current Opinion in Behavioral Sciences, 18:43–49, 2017

    Sharath Chandra Guntuku, David B Yaden, Margaret L Kern, Lyle H Ungar, and Johannes C Eichstaedt. Detecting depression and mental illness on social media: an integrative review.Current Opinion in Behavioral Sciences, 18:43–49, 2017. Big data in the behavioural sciences

    2017

  3. [11]

    QuantifyingmentalhealthsignalsinTwitter

    GlenCoppersmith,MarkDredze,andCraigHarman. QuantifyingmentalhealthsignalsinTwitter. In Proceedings oftheWorkshoponComputationalLinguisticsandClinicalPsychology: FromLinguisticSignaltoClinicalReality , pages 51–60, Baltimore, Maryland, USA, June 2014. Association for Computatio...

    2014

  4. [12]

    M Baklola, M Terra, A Taha, M Elnemr, M Yaseen, A Maher, AH Buzaid, R Alenazi, SA Osman Mohamed, DAbdelhady,andAHEl-Gilany.Mentalhealthliteracyandhelp-seekingbehaviouramongegyptianundergraduates: a cross-sectional national study.BMC Psychiatry, 24(1):202, 2024

    2024

  5. [13]

    Machinelearningandnaturallanguage processing in mental health: systematic review.Journal of Medical Internet Research, 23(5):e15708, 2021

    Aziliz Le Glaz, Yannis Haralambous, Deok-Hee Kim-Dufor, Philippe Lenca, Romain Billot, Taylor C Ryan, JonathanMarsh,JordanDevylder,MichelWalter,SofianBerrouiguet,etal. Machinelearningandnaturallanguage processing in mental health: systematic review.Journal of Medical Internet ...

    2021

  6. [14]

    Gpt-4 technical report.arXiv preprint arXiv:2303.08774, 2023

    Josh Achiam, Steven Adler, Sandhini Agarwal, et al. Gpt-4 technical report.arXiv preprint arXiv:2303.08774, 2023

    2023 arXiv

  7. [15]

    Natural language processing in mental health applications using large-scale text analysis.Journal of Medical Internet Research, 19(5):e240, 2017

    Rafael A Calvo, David N Milne, M Hussain, and Helen Christensen. Natural language processing in mental health applications using large-scale text analysis.Journal of Medical Internet Research, 19(5):e240, 2017

    2017

  8. [16]

    KathleenKFitzpatrick, AlisonDarcy, andMollyVierhile. Deliveringcognitivebehavioraltherapy toyoungadults with symptoms of depression and anxiety using a fully automated conversational agent (woebot): A randomized controlled trial.JMIR mental health, 4(2):e19, 2017

    2017

  9. [17]

    Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead.Nature Machine Intelligence, 1:206–215, 2019

    Cynthia Rudin. Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead.Nature Machine Intelligence, 1:206–215, 2019

    2019

  10. [18]

    A comprehen- sive evaluation of large language models on mental illnesses.arXiv preprint, 2024

    Abdelrahman Hanafi, Mohammed Saad, Noureldin Zahran, Radwa Hanafy, and Mohammed Fouda. A comprehen- sive evaluation of large language models on mental illnesses.arXiv preprint, 2024

    2024

  11. [19]

    Mental-llm: Leveraging large language models for mental health prediction via online text data

    XuhaiXu,BingshengYao,YuanzheDong,SaadiaGabriel,HongYu,JamesHendler,MarzyehGhassemi,AnindK Dey, and Dakuo Wang. Mental-llm: Leveraging large language models for mental health prediction via online text data. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous...

    2024

  12. [20]

    Bimedix: Bilingual medical mixture of experts llm.arXiv preprint arXiv:2402.13253, 2024

    SaraPieri,SahalShajiMullappilly,FahadShahbazKhan,RaoMuhammadAnwer,SalmanKhan,TimothyBaldwin, and Hisham Cholakkal. Bimedix: Bilingual medical mixture of experts llm.arXiv preprint arXiv:2402.13253, 2024

    2024 arXiv

  13. [21]

    Arabiangpt: Native arabic gpt-based large language model, 2024

    Anis Koubaa, Adel Ammar, Lahouari Ghouti, Omar Najar, and Serry Sibaee. Arabiangpt: Native arabic gpt-based large language model, 2024

    2024

  14. [22]

    Depression corpus of arabic tweets, 2022

    Radwa Nassar, AbdelMoniem Helmy, and Nagy Ramadan. Depression corpus of arabic tweets, 2022

    2022

  15. [23]

    Modern standard arabic mood changing and depression dataset.Data in Brief, 41:107999, 2022

    Ashwag Maghraby and Hosnia Ali. Modern standard arabic mood changing and depression dataset.Data in Brief, 41:107999, 2022

    2022

  16. [24]

    Aradepsu: Detecting depression and suicidal ideation in arabic tweets using transformers

    Mariam Hassib, Nancy Hossam, Jolie Sameh, and Marwan Torki. Aradepsu: Detecting depression and suicidal ideation in arabic tweets using transformers. InProceedings of the Seventh Arabic Natural Language Processing Workshop (WANLP), pages 302–311. Association for Computational ...

    2022

  17. [25]

    Cairodep: Detecting depression in arabic posts using bert transformers

    Mohammad El-Ramly, Hager Abu-Elyazid, Youseef Mo’men, Gameel Alshaer, Nardine Adib, Kareem Alaa Eldeen, and Mariam El-Shazly. Cairodep: Detecting depression in arabic posts using bert transformers. In2021 Tenth International Conference on Intelligent Computing and Information ...

    2021

  18. [26]

    Transfer learning-based automatic sentiment annotation of a twitter-based arabic mental illness (ami) dataset, 2023

    Arwa Diwali. Transfer learning-based automatic sentiment annotation of a twitter-based arabic mental illness (ami) dataset, 2023

    2023

  19. [27]

    Mental health supportive application

    Amany Adel Rofaida, Rofaida Ehab, Eman Eiad Nosiba, Esraa Mahmoud, and Norhan Ab- dul Menaem. Mental health supportive application. https://github.com/esraa-mahmoudsaid/ Classification-of-Mental-Disorders-in-Egyptian-dialect-of-Arabic , 2022. Graduation Project, Department of ...

    2022

  20. [28]

    No language left behind: Scaling human-centered machine translation.arXiv.2207.04672, 2022

    NLLB Team. No language left behind: Scaling human-centered machine translation.arXiv.2207.04672, 2022

    2022 arXiv

  21. [29]

    Hosni Mahmoud, Ajith Abraham, Samar Awadh Alanazi, and Laura García- Hernández

    Norah Saleh Alghamdi, Hanan A. Hosni Mahmoud, Ajith Abraham, Samar Awadh Alanazi, and Laura García- Hernández. Predicting depression symptoms in an arabic psychological forum.IEEE Access, 8:57317–57334, 2020. 27 LLMs Evaluation on Mental Illnesses in Arabic Context

    2020

  22. [30]

    Detecting arabic depressed users from twitter data

    Salma Almouzini, Maher khemakhem, and Asem Alageel. Detecting arabic depressed users from twitter data. Procedia Computer Science, 163:257–265, 2019. 16th Learning and Technology Conference 2019Artificial Intelligence and Machine Learning: Embedding the Intelligence

    2019

  23. [31]

    Abdulqader M. Almars. Attention-based bi-lstm model for arabic depression classification.Computers, Materials & Continua, 71(2):3091–3106, 2022

    2022

  24. [32]

    Prediction of depressed arab women using their tweets.Journal of Decision Systems, 30(2-3):102–117, 2021

    Eatedal Alabdulkreem. Prediction of depressed arab women using their tweets.Journal of Decision Systems, 30(2-3):102–117, 2021

    2021

  25. [33]

    Dreaddit: A reddit dataset for stress analysis in social media.arXiv preprint arXiv:1911.00133, 2019

    Elsbeth Turcan and Kathleen McKeown. Dreaddit: A reddit dataset for stress analysis in social media.arXiv preprint arXiv:1911.00133, 2019

    1911 arXiv

  26. [34]

    Deep learning for suicide and depression identification with unsupervised label correction

    Ayaan Haque, Viraaj Reddi, and Tyler Giallanza. Deep learning for suicide and depression identification with unsupervised label correction. InArtificial Neural Networks and Machine Learning–ICANN 2021: 30th InternationalConferenceonArtificialNeuralNetworks,Bratislava,Slovakia,...

    2021

  27. [35]

    Sad: A stress annotated dataset for recognizing everyday stressors in sms-like conversational systems

    Matthew Louis Mauriello, Thierry Lincoln, Grace Hon, Dorien Simon, Dan Jurafsky, and Pablo Paredes. Sad: A stress annotated dataset for recognizing everyday stressors in sms-like conversational systems. InExtended abstracts of the 2021 CHI conference on human factors in comput...

    2021

  28. [36]

    Deptweet: A typology for social media texts to detect depression severities

    Mohsinul Kabir, Tasnim Ahmed, Md Bakhtiar Hasan, Md Tahmid Rahman Laskar, Tarun Kumar Joarder, Hasan Mahmud, and Kamrul Hasan. Deptweet: A typology for social media texts to detect depression severities. Computers in Human Behavior, 139:107503, 2023

    2023

  29. [37]

    Data set creation and empirical analysis for detecting signs of depressionfromsocialmediapostings

    Kayalvizhi Sampath and Thenmozhi Durairaj. Data set creation and empirical analysis for detecting signs of depressionfromsocialmediapostings. In InternationalConferenceonComputationalIntelligenceinDataScience , pages 136–151. Springer, 2022

    2022

  30. [38]

    No Disorder

    S Kayalvizhi, Thenmozhi Durairaj, Bharathi Raja Chakravarthi, et al. Findings of the shared task on detecting signs of depression from social media. InProceedings of the Second Workshop on Language Technology for Equality, Diversity and Inclusion, pages 331–338, 2022. 6 Append...

    2022

Pith tools

Reviewed August 10, 2026 · model on record in the stance chip above.