Researchers used unsupervised machine learning techniques to classify older adults aged 65 years and above starting long-term care into clinical subtypes based on 22 diseases. Six clinical subtypes were identified, including musculoskeletal and sensory diseases, cardiac diseases, neurological diseases, respiratory diseases and cancers, insulin-dependent diabetes, and others. The study found that cardiac disease, respiratory disease/cancer, and insulin-dependent diabetes had a higher mortality risk and were associated with a worsening care-need level compared to musculoskeletal and sensory diseases. The findings of this research could influence health care policy and help develop optimal interventions for older adults in long-term care.
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Unraveling disease patterns in older adults starting long-term care in Japan and their future health outcomes
