Bibliographic record
Abstract
Introduction The advent of artificial intelligence (AI) and machine learning has sparked interest in its applicability in the mental health domain, offering potential improvements in the efficiency and personalization of psychiatric services. Objectives To characterize the methodological and technical approaches in studies utilizing machine learning and natural language processing (NLP) within mental health, to evaluate their potential and impact in psychiatric clinical practice, and to address the associated ethical concerns. Methods A systematic review, adhering to the PRISMA guidelines, was conducted across four primary medical databases. Emphasis was placed on studies that applied machine learning and NLP techniques to psychiatric contexts, extracting data from sources such as medical records and social media. Results From 327 identified articles, 58 were considered relevant. Major themes included symptom extraction, illness severity classification, therapy effectiveness comparison, and psychopathological insight derivation. Notably, most studies focused on specific populations like social media users, emergency room attendees, or those within medical databases. Methodological findings showcased a preference for efficient classifiers and Python as the primary platform. Conclusions Machine learning and NLP offer a promising new avenue for psychiatric research and clinical practice, enabling the extraction of previously inaccessible patient information and supporting the decision-making process. However, the field must address inherent limitations, ethical considerations, and ensure that the tools augment, rather than replace, clinical judgment. Disclosure of Interest None Declared
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.017 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.002 | 0.033 |
| Scholarly communication | 0.011 | 0.014 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.007 | 0.012 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".