Evidence map›Paper›PMID 41781940›Full record

ArticleBMC medical informatics and decision making2026

Explainable counterfactual reasoning in depression medication selection at multi-levels (personalized and population).

Xinyu Qin, Mark H Chignell, Alexandria Greifenberger, Sachinthya Lokuge, Elssa Toumeh, Tia Sternat, Martin Katzman, Lu Wang

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In one paragraph

Article in BMC medical informatics and decision making, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Xinyu QinDepartment of Biomedical Engineering, Cullen College of Engineering, University of Houston, 4226 Martin Luther King Boulevard, Houston, TX, 77204, USA.
Mark H ChignellDepartment of Mechanical & Industrial Engineering, University of Toronto, 5 King's College Road, Toronto, Ontario, M5S 3G8, Canada.
Alexandria GreifenbergerSTART Clinic for Mood and Anxiety Disorders, 32 Park Road, Toronto, Ontario, M4W 2N4, Canada.
Sachinthya LokugeDepartment of Psychology, Virginia Tech, 890 Drillfield Drive, Blacksburg, VA, 24060, USA.
Elssa ToumehSTART Clinic for Mood and Anxiety Disorders, 32 Park Road, Toronto, Ontario, M4W 2N4, Canada.
Tia SternatSTART Clinic for Mood and Anxiety Disorders, 32 Park Road, Toronto, Ontario, M4W 2N4, Canada.
Martin KatzmanSTART Clinic for Mood and Anxiety Disorders, 32 Park Road, Toronto, Ontario, M4W 2N4, Canada.
Lu WangDepartment of Biomedical Engineering, Cullen College of Engineering, University of Houston, 4226 Martin Luther King Boulevard, Houston, TX, 77204, USA. lwang71@central.uh.edu.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThis study investigates how variations in Major Depressive Disorder (MDD) symptoms (HAM-D) are associated in a predictive model with randomized clinical trial (RCT) arm assignment between SSRIs and SNRIs.

methodsWe applied explainable counterfactual reasoning with counterfactual explanations (CFs) to assess the impact of specific symptom changes on model-predicted RCT arm assignment.

resultsAcross 17 classifiers, CatBoost achieved the highest performance; typical test metrics ranged  0.74–0.78 with best ROC-AUC  0.7640. Sample-based CFs revealed both local and global feature importance of individual symptoms in medication selection.

conclusionCounterfactual reasoning highlights which MDD symptoms the model uses to distinguish SSRI vs. SNRI trial assignments, supporting interpretable AI-based decision support while requiring prospective real-world validation beyond the RCT context. Future work should validate these findings on more diverse cohorts and refine algorithms for clinical deployment.

Indexed as

Antidepressive AgentsDecision Support Systems, ClinicalMajor Depressive DisorderSelective Serotonin Reuptake InhibitorsAdultBoosting Machine Learning AlgorithmsFemaleHumansMaleRandomized Controlled Trials as TopicAntidepressive AgentsSelective Serotonin Reuptake InhibitorsAI-based clinical decision support systemsCounterfactual reasoningeXplainable AI (XAI)Major depressive disorderPopulation healthPrecision medicine

Identifiers

PMID41781940
PMCPMC13067738

What OpenQuestion holds

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LicenceCC BY-NC-ND
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.