A depression model classified young patients from blood tests—but remains an internal hospital study
A gradient-boosting model trained on records from 600 patients aged 10–26 reported 87.8% accuracy. Its strongest inputs were laboratory markers, but the study does not show that the model works in another hospital or improves diagnosis.
The short version
- What happened
- The retrospective cohort contained 600 patients aged 10–26 from 2019–2025: 250 with depression and 350 without it.
- Who it affects
- If independently validated, a laboratory-based model might support—not replace—assessment where specialist mental-health capacity is limited. Premature use could also miss depressed patients or medicalise people whose blood patterns resemble the training data.
- What remains unproven
- The cohort was retrospective and limited to 600 patients from 2019–2025; selection and diagnostic-label bias may affect performance.
Keep in mind
The cohort was retrospective and limited to 600 patients from 2019–2025; selection and diagnostic-label bias may affect performance.
No external hospital, prospective workflow or population-screening validation is reported in the accessible article record.
The study did not compare patient outcomes or clinician performance with and without the model.
Sensitivity was 79.7% at the chosen threshold, leaving a material false-negative risk in the reported evaluation.
The authors reported no external funding and declared no competing interests.
Source analysis · 1 linked source. Primary analysed source date: 21 October 2026.
Read the full report · 3 min The evidence, methods, implications and limitations
The model found a pattern in nine variables
Researchers retrospectively assembled records for 600 patients aged 10–26, including 250 labelled with depression and 350 without it. Boruta feature selection retained nine inputs: S100β, platelet count, lactate dehydrogenase, white-cell count, creatine kinase-MB, sex and three ratios derived from laboratory measures.
The best reported gradient-boosting model used a 0.591 threshold. It reached 87.8% accuracy, 91.3% precision, 79.7% sensitivity, 94.1% specificity and an F1 score of 85.1%. S100β, platelet count and the LDH-to-platelet ratio contributed most in the model's SHAP explanation.[1]
A missed case still matters
Sensitivity of 79.7% means roughly one in five depression-labelled cases in the evaluation was not identified at the chosen threshold. High specificity reduces false positives in this dataset, but neither number tells clinicians how the tool would behave among people with different illnesses, medicines, laboratory equipment or referral patterns.
For a young person, depression diagnosis depends on symptoms, functioning, risk and clinical context—not only blood results. The paper offers a retrospective classifier, not a stand-alone blood test, and it did not measure whether using the model changes assessment time, treatment, self-harm risk or recovery.[1]
The distinction that matters
What the reported percentages mean
The model's internal evaluation separates correctly classified cases from different kinds of error.
All records
Classify 600 retrospective patients
87.8% accuracy
Depression-labelled
Look for a positive model result
79.7% sensitivity
Non-depressed
Look for a negative model result
94.1% specificity
External validation is the missing step
The accessible early-view article reports calibration and decision-curve analysis, but no independent hospital or prospective deployment. Internal performance can be optimistic when patient selection, laboratory practice and diagnostic labels come from the same care setting used to develop the model.
A convincing next study would freeze the model and threshold, test them at several unrelated hospitals, pre-specify age and sex subgroups, and compare decisions with and without the tool. The authors declared no funding and no competing interests; clinical benefit remains unmeasured.[1]