The PRIME framework for investigating emotions and other patient factors in low-intermediate risk prostate cancer patients based on online cancer support group discussions
To use the Patient Reported Information Multidimensional Exploration (PRIME) framework, a novel ensemble of machine learning and deep learning algorithms, to extract, analyse and correlate self-reported information from online support group discussions (OCSG) by patients (and partners of patients) with low-intermediate risk PCa undergoing radical prostatectomy (RP), external beam radiotherapy (EBRT) and active surveillance (AS), and investigate its efficacy in determining Quality of life (QOL) and emotion measures.
All discussions related to low-intermediate risk PCa were extracted from ten OCSG with active user participation. A total of 390,071 online discussions by 6084 patients were analysed using the PRIME framework. Side effects and emotional/QoL outcomes were analysed.
Side effect profiles differed between the modalities analysed, with men post-RP having more urinary and sexual side effects and men post-EBRT having more bowel symptoms. Key findings from the analysis of expressions of emotion; (i) PCa patients aged <40 expressed significantly high positive and negative emotions compared to other age groups, (ii) partners of patients expressed more negative emotions than patients, and (iii) selected cohorts (<40, >70, partners of patients) have frequently used the same terms to express their emotions which is indicative of QoL issues specific to those cohorts.
Despite recent advances in patient-centred care, patient emotions are largely overlooked in clinical research endeavours, especially in younger men diagnosed with PCa and their partners. We present a novel approach, the PRIME framework, to extract, analyse and correlate key patient factors. This framework improves understanding of QoL and thereby informed decision-making in low-intermediate risk PCa patients.