Key result
Gaussian Mixture Models outperform K-means in classifying humanistic care abilities with a 0.48 silhouette score.
Why the study?
This study was conducted to develop an unsupervised framework using wearable device multimodal physiological data to support the cultivation of medical students' humanistic care abilities.
Does an unsupervised Gaussian Mixture Model framework using wearable physiological data accurately classify medical students' humanistic care abilities compared to other clustering algorithms?
Observational (n=70)
Does an unsupervised Gaussian Mixture Model framework using wearable physiological data accurately classify medical students' humanistic care abilities compared to other clustering algorithms?
Absolute Event Rate: 0.48% vs 0.36%
An unsupervised GMM framework using wearable physiological data effectively classifies humanistic care abilities, outperforming other clustering methods and enabling personalized medical humanities training.
May aid personalized humanistic training via wearables; leaves open prospective validation before educational use.
This study develops an unsupervised framework using Gaussian Mixture Models (GMM) to analyze multimodal physiological data from wearable devices, aiming to support the cultivation of medical students’ humanistic care abilities. The framework integrates signals such as heart rate variability, skin conductance, and accelerometer readings obtained via textile-integrated sensors, and applies feature extraction and clustering to classify individual humanistic responsiveness. Personalized training pathways are generated based on the clustered physiological patterns, enabling targeted interventions ranging from foundational emotion awareness to advanced empathy simulation. Comparative evaluation demonstrates that GMM outperforms K-means, DBSCAN, hierarchical, and spectral clustering in terms of intra-cluster compactness, inter-cluster separation, and alignment with behavioral labels. Dynamic indicators, including low-to-high state transition frequency, provide better correlation with clinical performance than static measures, supporting flexible and adaptive training strategies. This approach offers a data-driven, end-to-end methodology for precise medical humanities education and can be integrated with antenna-enabled wearable devices, wireless physiological monitoring platforms, and edge-computing systems to facilitate real-time analysis and personalized educational interventions.
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J.K. REN (2026) conducted an observational in Humanistic care abilities (simulated) (n=70). Gaussian Mixture Model (GMM) clustering framework vs. K-means, DBSCAN, hierarchical, and spectral clustering was evaluated on Silhouette Score for clustering performance. The Gaussian Mixture Model framework outperformed K-means and other clustering algorithms in classifying humanistic care abilities, achieving a silhouette coefficient of 0.48 and an adjusted Rand index of 0.59.
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