Reinforcement Learning–Based Digital Therapeutic Intervention for Post-Prostatectomy Incontinence: Development and Pilot Feasibility Study
ABSTRACT
Background:
Post-prostatectomy urinary incontinence (PPI) affects 4.2% to 87% of prostate cancer patients following robot-assisted radical prostatectomy (RARP), significantly impairing their quality of life (QoL). Despite strong evidence supporting behavioral interventions such as pelvic floor muscle training (PFMT) and bladder diaries, poor adherence and lack of personalized tailoring hinder their effectiveness.
Objective:
This study aims to develop and characterize a novel Clinical Behavioral Intervention-Supporting System (CBISs) that leverages reinforcement learning (RL) to deliver adaptive, personalized rehabilitation for PPI patients.
Methods:
A prospective cohort of 150 PPI patients contributed over 10,000 voiding events recorded in 3-day bladder diaries. Twenty-nine dynamic features across six categories were engineered, including temporal patterns, behavioral ratios, contextual markers, and adherence metrics. An XGBoost-based reinforcement learning model optimized individualized training plans via Bayesian optimization. The CBIS mobile platform integrated four main functional modules: rehabilitation training, bladder diary, assessment tools, and incontinence dermatitis care.
Results:
The CBIS dynamically adjusted interventions through continuous, reinforcement learning-driven optimization, incorporating: (1) patient-specific regimen generation based on multidimensional data, including voiding patterns, fluid intake, and adherence scores; (2) iterative plan updates informed by patient feedback and progress data; (3) clinician oversight portals featuring automated incontinence dermatitis alerts; and (4) SM3/SM4 encryption with hierarchical data access.
Conclusions:
The CBIS represents the first reinforcement learning-powered digital therapeutic system for PPI, enabling adaptive, evidence-based behavioral optimization. By bridging gaps in traditional rehabilitation, such as static protocols and declining adherence, it offers a scalable solution for personalized PPI management. Future multicenter trials are warranted to validate its efficacy.
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