Cancer is one of the most widespread diseases around the world with millions
of new patients each year. Bladder cancer is one of the most prevalent types of
cancer affecting all individuals alike with no obvious prototypical patient.
The current standard treatment for BC follows a routine weekly Bacillus
Calmette-Guerin (BCG) immunotherapy-based therapy protocol which is applied to
all patients alike. The clinical outcomes associated with BCG treatment vary
significantly among patients due to the biological and clinical complexity of
the interaction between the immune system, treatments, and cancer cells. In
this study, we take advantage of the patient's socio-demographics to offer a
personalized mathematical model that describes the clinical dynamics associated
with BCG-based treatment. To this end, we adopt a well-established BCG
treatment model and integrate a machine learning component to temporally adjust
and reconfigure key parameters within the model thus promoting its
personalization. Using real clinical data, we show that our personalized model
favorably compares with the original one in predicting the number of cancer
cells at the end of the treatment, with 14.8% improvement, on average.

通过综合机器学习组件调整模型参数，构建个性化机理模型，并使用真实临床数据，展示其相对于原始模型在预测结局时的优势，平均改善了 14.8%。