Predicting Treatment Efficacy in Individuals with Major Depression -- Deep Neural Networks for Physiological Patient Parameters
Bibliographic record
Abstract
Major Depression is a serious mental illness that globally affects 11.1% of people over the course of their lives [1] and that is projected to be responsible for the majority of Disability-Adjusted Life Years (DALY’s) lost by 2030 [2,3]. While a range of effective treatments do exist, these are not equivalently effective for all patients and some patients can spend years finding the right choice from the dozens of medications, multiple psychotherapies, and five neurostimulation techniques available. Currently, most patients and their physicians have little option but to go through a “guess and check” approach to finding the right treatment. For a patient with depression, trying a new treatment means several weeks of therapy or medication titration to start seeing if there is a positive effect. This is time lost in the patient’s life -- time that is potentially away from work and when they are not able to be fully present in their families’ lives. Inadequately treated depression also leads to risks of suicide and self-harm. What’s more, many patients with depression will not improve after the first treatment -- in the STAR*D trial, only about one third of patients improved after their first treatment trial, with decreasing response rates after further trials [4]. This means that the decision about which treatment to try is one that has significant consequences. It is clear that a research objective for depression, other than improving diagnostic rates and access to care, should be developing an evidence-based approach for rapidly selecting the most effective treatment for a given patient, as early on in their clinical course as possible, while minimizing side effects that lead to reduced quality of life or treatment adherence. Existing psychiatric guidelines do separate the large of array of treatment options into first, second, and third line treatments [5]; and clinical experience has taught mental health professionals that certain types of medications or psychotherapy approaches work best in certain kinds of patients. Different patients develop different side effects to the same medication in an often unpredictable manner, further complicating treatment choice [5].However, there is not a systematic, evidence-based tool that predicts treatments in a way that is personalized to a given patient [5,6,7,8].
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".