Patient Clustering based on Health Data

Clustering individuals based on health data (demographics, lab results, etc.) is difficult because of the vast amount of data and the complex relationships between variables.

Quantum clustering algorithms, like quantum k-means, can process and analyze large datasets with high efficiency, enabling the grouping of individuals with similar health profiles or disease risks.

Better clustering can lead to more targeted interventions, personalised healthcare plans, and optimised resource allocation, which can ultimately improve public health outcomes and reduce costs associated with generalised treatments.