Evidence map›Paper›PMID 35005628›Full record

ArticleProceedings of machine learning research2021

MIMIC-SBDH: A Dataset for Social and Behavioral Determinants of Health.

Hiba Ahsan, Emmie Ohnuki, Avijit Mitra, Hong Yu

Abstract read
In one paragraph

Article in Proceedings of machine learning research, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.

0numbers the graph read from it
0cells of the map it votes in
20citing 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

20 citing papers in PubMed.

  1. Article
  2. ClinNoteAgents: An LLM Multi-Agent System for Predicting and Interpreting Heart Failure 30-Day Readmission from Clinical Notes.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2026
    Article
  3. SDoH-GPT: using large language models to extract social determinants of health.Journal of the American Medical Informatics Association : JAMIA · 2026
    Article
  4. Article
  5. Synth-SBDH: A Synthetic Dataset of Social and Behavioral Determinants of Health for Clinical Text.Proceedings of the Conference on Empirical Methods in Natural Language Processing. Conference on Empirical Methods in Natural Language Processing · 2025
    Article
  6. Article
  7. Article
  8. Review
  9. Integrating Social Determinants of Health into Knowledge Graphs: Evaluating Prediction Bias and Fairness in Healthcare.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2025
    Article
  10. ODD: A Benchmark Dataset for the Natural Language Processing Based Opioid Related Aberrant Behavior Detection.Proceedings of the conference. Association for Computational Linguistics. North American Chapter. Meeting · 2024
    Article
  11. Stressful life events in electronic health records: a scoping review.Journal of the American Medical Informatics Association : JAMIA · 2024
    Article
  12. Article
  13. Article
  14. Article
  15. Article
  16. Article
  17. Automated identification of eviction status from electronic health record notes.Journal of the American Medical Informatics Association : JAMIA · 2023
    Article
  18. Benchmarking Transformer-Based Models for Identifying Social Determinants of Health in Clinical Notes.Proceedings. IEEE International Conference on Healthcare Informatics · 2023
    Article
  19. Capturing Individual-level Social Determinants from Clinical Text.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2023
    Article
  20. Article
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

4 authors.

Hiba AhsanCollege of Information and Computer Sciences, University of Massachusetts, Amherst, MA, USA.
Emmie OhnukiCollege of Information and Computer Sciences, University of Massachusetts, Amherst, MA, USA.
Avijit MitraCollege of Information and Computer Sciences, University of Massachusetts, Amherst, MA, USA.
Hong YuCollege of Information and Computer Sciences, University of Massachusetts, Amherst, MA, USA.

Funding

Improving Suicide Prediction using NLP-Extracted Social Determinants of HealthR01MH125027 · NIMH · UNIVERSITY OF MASSACHUSETTS LOWELL · PI YU, HONG · 2020 to 2023
$3.1M
Identifying Opioid Overdose Predictors using EHRsR01DA045816 · NIDA · UNIVERSITY OF MASSACHUSETTS LOWELL · PI BECKER, WILLIAM C, LI, WENJUN · 2018 to 2022
$3.0M
Social and behavioral determinants of MOUD utilization and opioid overdoseR01DA056470 · NIDA · UNIVERSITY OF MASSACHUSETTS LOWELL · PI Wenjun Li, DAVID A SMELSON · 2023 to 2026
$2.9M
NIDA NIH HHS R01 DA045816NIDA NIH HHS R01 DA056470NIMH NIH HHS R01 MH125027
6 · The paper itself

Abstract

Social and Behavioral Determinants of Health (SBDHs) are environmental and behavioral factors that have a profound impact on health and related outcomes. Given their importance, physicians document SBDHs of their patients in Electronic Health Records (EHRs). However, SBDHs are mostly documented in unstructured EHR notes. Determining the status of the SBDHs requires manually reviewing the notes which can be a tedious process. Therefore, there is a need to automate identifying the patients' SBDH status in EHR notes. In this work, we created MIMIC-SBDH, the first publicly available dataset of EHR notes annotated for patients' SBDH status. Specifically, we annotated 7,025 discharge summary notes for the status of 7 SBDHs as well as marked SBDH-related keywords. Using this annotated data for training and evaluation, we evaluated the performance of three machine learning models (Random Forest, XGBoost, and Bio-ClinicalBERT) on the task of identifying SBDH status in EHR notes. The performance ranged from the lowest 0.69 F1 score for Drug Use to the highest 0.96 F1 score for Community-Present. In addition to standard evaluation metrics such as the F1 score, we evaluated four capabilities that a model must possess to perform well on the task using the CheckList tool (Ribeiro et al., 2020). The results revealed several shortcomings of the models. Our results highlighted the need to perform more capability-centric evaluations in addition to standard metric comparisons.

Identifiers

PMID35005628
PMCPMC8734043

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Registered trials

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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.