TY  - JOUR
AU  - Sprint, Gina
AU  - Schmitter-Edgecombe, Maureen
AU  - Cook, Diane
PY  - 2024
DA  - 2024/12/23
TI  - Building a Human Digital Twin (HDTwin) Using Large Language Models for Cognitive Diagnosis: Algorithm Development and Validation
JO  - JMIR Form Res
SP  - e63866
VL  - 8
KW  - human digital twin
KW  - cognitive health
KW  - cognitive diagnosis
KW  - large language models
KW  - artificial intelligence
KW  - machine learning
KW  - digital behavior marker
KW  - interview marker
KW  - health information
KW  - chatbot
KW  - digital twin
KW  - smartwatch
AB  - Background: Human digital twins have the potential to change the practice of personalizing cognitive health diagnosis because these systems can integrate multiple sources of health information and influence into a unified model. Cognitive health is multifaceted, yet researchers and clinical professionals struggle to align diverse sources of information into a single model. Objective: This study aims to introduce a method called HDTwin, for unifying heterogeneous data using large language models. HDTwin is designed to predict cognitive diagnoses and offer explanations for its inferences. Methods: HDTwin integrates cognitive health data from multiple sources, including demographic, behavioral, ecological momentary assessment, n-back test, speech, and baseline experimenter testing session markers. Data are converted into text prompts for a large language model. The system then combines these inputs with relevant external knowledge from scientific literature to construct a predictive model. The model’s performance is validated using data from 3 studies involving 124 participants, comparing its diagnostic accuracy with baseline machine learning classifiers. Results: HDTwin achieves a peak accuracy of 0.81 based on the automated selection of markers, significantly outperforming baseline classifiers. On average, HDTwin yielded accuracy=0.77, precision=0.88, recall=0.63, and Matthews correlation coefficient=0.57. In comparison, the baseline classifiers yielded average accuracy=0.65, precision=0.86, recall=0.35, and Matthews correlation coefficient=0.36. The experiments also reveal that HDTwin yields superior predictive accuracy when information sources are fused compared to single sources. HDTwin’s chatbot interface provides interactive dialogues, aiding in diagnosis interpretation and allowing further exploration of patient data. Conclusions: HDTwin integrates diverse cognitive health data, enhancing the accuracy and explainability of cognitive diagnoses. This approach outperforms traditional models and provides an interface for navigating patient information. The approach shows promise for improving early detection and intervention strategies in cognitive health. 
SN  - 2561-326X
UR  - https://formative.jmir.org/2024/1/e63866
UR  - https://doi.org/10.2196/63866
DO  - 10.2196/63866
ID  - info:doi/10.2196/63866
ER  -