Development and validation of a set of six adaptable prognosis prediction (SAP) models based on time-series real-world big data analysis for patients with cancer receiving chemotherapy: A multicenter case crossover study Adaptable prognostic models for patients with cancer receiving chemotherapy
Yu Uneno, Kei Taneishi, Masashi Kanai, Kazuya Okamoto, Yosuke Yamamoto, Akira Yoshioka, Shuji Hiramoto, Akira Nozaki, Yoshitaka Nishikawa, Daisuke Yamaguchi, Teruko Tomono, Masahiko Nakatsui, Mika Baba, Tatsuya Morita, Shigemi Matsumoto, Tomohiro Kuroda, Yasushi Okuno, Manabu Muto, Wen-Lung Ma
Abstract
In this study, using time-series real-world big data, we successfully developed a set of six adaptable prognosis prediction models for patients with cancer receiving chemotherapy. Our current models allow physicians to select the optimal model according to the clinical condition of the patient and apply them repeatedly at any time point. In other words, physicians can re-estimate the prognosis of patients at any time point after the initiation of chemotherapy. We expect that our adaptable models will provide valuable information to both physicians and patients during decision-making for optima
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