What is it about?
We profiled each patient's state transitions during treatment using energy landscape analysis and time-series clustering. The evaluation of state multistability enables us to understand a patient's current state in the context of overall state transitions related to drug treatment and to predict future state transitions.
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Why is it important?
This study suggested the possibility of optimizing the treatment plan based on the whole treatment course. We believe that this study will contribute to the development of personalized medicine utilizing real-world data.
Perspectives
In the realm of AI applications in healthcare, there is a fascinating parallel with the domain of chess, shogi, and go AI, where professionals occasionally find themselves defeated by seemingly unorthodox moves beyond human comprehension. Similarly, in the context of real-world practice visualization, the recommended treatment may significantly deviate from established clinical guidelines. Our research suggests the emergence of an innovative approach to the future landscape of medical research and practice, tentatively termed “AI-based randomized controlled trials,” to systematically investigate and corroborate the efficacy of AI-suggested clinical interventions.
Dr. Keiichi Yamamoto
Osaka Dental University
Read the Original
This page is a summary of: Energy landscape analysis and time-series clustering analysis of patient state multistability related to rheumatoid arthritis drug treatment: The KURAMA cohort study, PLOS One, May 2024, PLOS,
DOI: 10.1371/journal.pone.0302308.
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