Building a Human Digital Twin for Cognitive Diagnosis
Author Information
Author(s): Mavragani Amaryllis, Rocha Heber L, Tziritas Nikos, Sprint Gina PhD, Schmitter-Edgecombe Maureen PhD, Cook Diane PhD
Primary Institution: Washington State University
Hypothesis
Can a human digital twin (HDTwin) effectively unify diverse cognitive health data to improve diagnostic accuracy?
Conclusion
HDTwin enhances the accuracy and explainability of cognitive diagnoses by integrating diverse data sources.
Supporting Evidence
- HDTwin achieved a peak accuracy of 0.81, significantly outperforming baseline classifiers.
- On average, HDTwin yielded accuracy=0.77, precision=0.88, and recall=0.63.
- The approach shows promise for improving early detection and intervention strategies in cognitive health.
- Participants were categorized as cognitively healthy older adults (60.5%) or older adults with MCI (39.5%).
Takeaway
This study created a digital twin that helps doctors understand and diagnose cognitive health better by using lots of different information about a person.
Methodology
HDTwin integrates cognitive health data from multiple sources and uses a large language model to predict cognitive diagnoses.
Potential Biases
Nondeterminism of LLMs may affect reproducibility of results.
Limitations
The study faced limitations such as missing data, reliance on LLM for response extraction, and a small sample size for training.
Participant Demographics
Participants were independent-living older adults, mean age 70.48 years, 71.78% female.
Statistical Information
P-Value
p<0.001
Statistical Significance
p<0.001
Digital Object Identifier (DOI)
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