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State-Specific Explainable Machine Learning for Predicting Premature Dropout in Medication for Opioid Use Disorder

(2025)10.1145/3721201.3721390Source: DataRank Database

State-Specific Explainable Machine Learning for Predicting Premature Dropout in Medication for Opioid Use Disorder is a research paper (2025). On theSindex it has a DataRank of 0.

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0DataRank · unranked

Scored on demand from live citation data

Open Access0 citations

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›Methodology & internals

datarank_citation_only_1hop_v6· scope data_onlyMethodology

Pipeline:MetadataData-paper checkEnrichmentCitation networkScoring

Enrichment:Funding (2 grants)Impact (FWCI 0.00)OA: goldTopics (10)IDs (PubMed, OpenAlex)

FAIR Checklist

Context only (not used in score)

Findable (1/2)

Has DOI

Accessible (1/2)

Open Access

Interoperable (0/2)

Reusable (0/3)

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Authors (6)

Tongnian Wang ORCID,Yuanxiong Guo ORCID,Carolina Vivas-Valencia ORCID,Cici Bauer ORCID,Yanmin Gong ORCID

Live enrichment

PubMed:21097893 OpenAlex:https://openalex.org/W4416144450

UT San Antonio, AIM-AHEAD Coordinating Center

Grant: OTA-21-017

National Institutes of Health Agreement

Grant: No. 1OT2OD032581

FWCI

0.00

Citation Percentile

0.4%

goldLicense: cc-by

https://dl.acm.org/doi/pdf/10.1145/3721201.3721390

https://doi.org/10.1145/3721201.3721390

Fields of Study

Opioid Use Disorder TreatmentMental Health via WritingMachine Learning in Healthcare

Keywords

Opioid use disorderDropout (neural networks)Psychological interventionPersonalizationKey (lock)OpioidPublic health