Evidence map›Paper›PMID 40600164›Full record

ArticleProceedings. IEEE International Conference on Semantic Computing2025

Developing a high-performing network computation of big bipartite network data toward alcohol use disorder treatment referrals.

Muhammad Tuan Amith, Sharon Andrews, Angela Heads, Bruno Kluwe-Schiavon, Atchyutha Choday, Ramya Poonam, Sai Venkat Ballem, Cui Tao, Jane Hamilton

Abstract read
In one paragraph

Article in Proceedings. IEEE International Conference on Semantic Computing, 2025. 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

9 authors.

Muhammad Tuan AmithDepartment of Biostatistics and Data Science, University of Texas Medical Branch, Galveston, TX USA.
Sharon AndrewsCollege of Computing and Engineering, University of Houston Clear Lake, Houston, TX USA.
Angela HeadsDepartment of Psychiatry and Behavioral Sciences, University of Texas Health Science Center at Houston, Houston, TX USA.
Bruno Kluwe-SchiavonDepartment of Psychiatry and Behavioral Sciences, University of Texas Health Science Center at Houston, Houston, TX USA.
Atchyutha ChodayCollege of Computing and Engineering, University of Houston Clear Lake, Houston, TX USA.
Ramya PoonamCollege of Computing and Engineering, University of Houston Clear Lake, Houston, TX USA.
Sai Venkat BallemCollege of Computing and Engineering, University of Houston Clear Lake, Houston, TX USA.
Cui TaoDepartment of Artificial Intelligence and Informatics Mayo Clinic, Jacksonville, FL USA.
Jane HamiltonDepartment of Psychiatry and Behavioral Sciences, University of Texas Health Science Center at Houston, Houston, TX USA.

Funding

Dynamic learning for post-vaccine event prediction using temporal information in VAERSR01AI130460 · NIAID · UNIVERSITY OF TEXAS HLTH SCI CTR HOUSTON · PI CHEN, YONG, TAO, CUI · 2017 to 2021
$3.4M
Patient Medical History Representation, Extraction, and Inference from EHR DataR01LM011829 · NLM · UNIVERSITY OF TEXAS HLTH SCI CTR HOUSTON · PI TAO, CUI · 2014 to 2018
$1.7M
NIAID NIH HHS R01 AI130460NLM NIH HHS R01 LM011829
6 · The paper itself

Abstract

Electronic health care records offer big data to mine and analyze towards improving public health outcomes. The information extracted, specifically social network data, could help us understand the primary care referrals for patients experiencing alcohol use disorder and wield that knowledge to better inform the engagement of this patient population.

Indexed as

affiliation exposure modelbipartite graphnetwork exposure modelnetwork graphsocial networksoftware

Identifiers

PMID40600164
PMCPMC12212966

What OpenQuestion holds

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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.