Natural language processing methods for assessing social determinants of health in the electronic health records: A narrative review
Bibliographic record
Abstract
Over the years, social determinants of health have increasingly been discovered to have a significant impact on an extensive range of mental and physical health outcomes. The wide adoption of electronic health records has made it possible for the development of automated techniques to conduct studies on the effects of such factors on health. With the recent advancements in machine learning, it has become the key technology for extracting patient-level social and behavioral factors from electronic health records. However, the current state-of-the-art machine learning techniques used to extract social and behavioral factors have yet to be deciphered and compared. This narrative review aimed at evaluating advancements in machine learning technology used in the literature over the past decade for the extraction of social determinants of health factors from electronic health records data to gain a better understanding of how and when to leverage them. This was conducted by analyzing the social determinants of health categories, evaluating the relationship between these characteristics and patient health outcomes, reviewing documentation practices of the characteristics in electronic health records, summarizing and comparing current machine learning techniques in the field as well as assessing their limitation, and suggesting some future directions of study. Leveraging machine learning can overcome challenges faced with parsing unstructured clinical data, ease the extraction of relevant concepts in medical text, and aid in studying the health risks caused by social behavior. Despite current knowledge of the significant impact of social and behavioral factors on health, they are rarely documented and investigated in healthcare systems. Understanding current state-of-the-art machine learning technology to support the identification of social determinants of health in a clinical setting can influence clinical decision-making to provide better overall patient wellness with the ultimate goal of producing health equity.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.013 | 0.060 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.010 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".