Public articles linked to the same research event.
medRxiv The study introduces LLMPopSim, a generative population simulation framework that integrates U.S. Census and CDC data to construct synthetic individuals and uses an LLM to simulate individual health behaviors whose aggregate outcomes can be evaluated at the community level; developed with historical Hawaiʻi data and evaluated for temporal and geographic generalizability using held-out 2022 cohorts from Hawaiʻi and New York State with colorectal cancer screening and mammography as proof-of-concept behaviors, the four state-outcome evaluations showed mean absolute error of 3.5 to 15.0 percentage points and correlations between simulated and observed ZCTA-level prevalence of 0.26 to 0.69.
The study introduces LLMPopSim, a generative population simulation framework that integrates U.S. Census and CDC data to construct synthetic individuals and uses an LLM to simulate individual health behaviors whose aggregate outcomes can be evaluated at the community level; developed with historical Hawaiʻi data and evaluated for temporal and geographic generalizability using held-out 2022 cohorts from Hawaiʻi and New York State with colorectal cancer screening and mammography as proof-of-concept behaviors, the four state-outcome evaluations showed mean absolute error of 3.5 to 15.0 percentage points and correlations between simulated and observed ZCTA-level prevalence of 0.26 to 0.69.
The study introduces LLMPopSim, a generative population simulation framework that integrates U.S. Census and CDC data to construct synthetic individuals and uses an LLM to simulate individual health behaviors whose aggregate outcomes can be evaluated at the community level; developed with historical Hawaiʻi data and evaluated for temporal and geographic generalizability using held-out 2022 cohorts from Hawaiʻi and New York State with colorectal cancer screening and mammography as proof-of-concept behaviors, the four state-outcome evaluations showed mean absolute error of 3.5 to 15.0 percentage points and correlations between simulated and observed ZCTA-level prevalence of 0.26 to 0.69.
The study introduces LLMPopSim, a generative population simulation framework that integrates U.S. Census and CDC data to construct synthetic individuals and uses an LLM to simulate individual health behaviors whose aggregate outcomes can be evaluated at the community level; developed with historical Hawaiʻi data and evaluated for temporal and geographic generalizability using held-out 2022 cohorts from Hawaiʻi and New York State with colorectal cancer screening and mammography as proof-of-concept behaviors, the four state-outcome evaluations showed mean absolute error of 3.5 to 15.0 percentage points and correlations between simulated and observed ZCTA-level prevalence of 0.26 to 0.69.