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(HealthDay Information) — Giant language fashions (LLMs) can probably enhance the identification of social determinants of well being (SDoH) in digital well being data (EHRs), based on a examine revealed on-line Jan. 11 in npj Digital Medication.
Noting that SDoH play an vital position in affected person outcomes however their documentation is usually lacking or incomplete in EHRs, Marco Guevara, from Mass Basic Brigham and Harvard Medical Faculty in Boston, and colleagues examined the optimum strategies for utilizing LLMs to extract six SDoH classes from narrative textual content within the EHR: employment, housing, transportation, parental standing, relationship, and social assist.
The researchers discovered that the best-performing fashions have been fine-tuned Flan-T5 XL and Flan-T5 XXL for any SDoH mentions and opposed SDoH mentions, respectively. Throughout fashions and structure, there was variation within the addition of LLM-generated artificial information to coaching, however this improved the efficiency of small Flan-T5 fashions. Within the zero- and few-shot setting, one of the best finetuned fashions outperformed zero- and few-shot efficiency of ChatGPT fashions, besides GPT4 with 10-shot prompting for opposed SDoH. When race/ethnicity and gender descriptors have been added to the textual content, finetuned fashions have been much less probably than ChatGPT to vary their prediction, suggesting much less algorithmic bias. Total, 93.8% of sufferers with opposed SDoH have been recognized with the fashions, whereas Worldwide Classification of Illnesses-Model 10 codes captured 2.0%.
“Sooner or later, these fashions might enhance our understanding of drivers of well being disparities by bettering real-world proof and will immediately assist affected person care by flagging sufferers who could profit most from proactive useful resource and social work referral,” the authors write.
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