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Building a Human Digital Twin (HDTwin) Using Large Language Models for Cognitive Diagnosis: Algorithm Development and Validation

Building a Human Digital Twin (HDTwin) Using Large Language Models for Cognitive Diagnosis: Algorithm Development and Validation

Similarly, Sprint et al [19] demonstrated that LLMs could anticipate future health states based on past EMA reports and sensor-based behavior data. Recently, researchers have extended LLM capabilities to interpret nontextual data inputs. Yu et al [20] direct LLMs to diagnose sleep apnea and cardiac conditions by leveraging large databases. Jin et al [21] convert time-series data into text to forecast electricity usage.

Gina Sprint, Maureen Schmitter-Edgecombe, Diane Cook

JMIR Form Res 2024;8:e63866

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