A Bayesian network to improve patient reported symptom monitoring in lung cancer
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2024-04-09
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en
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Abstract
Background: The SYMPRO-Lung study showed that patient-reported outcome (PRO)
symptom monitoring significantly improved health-related quality of life (HRQOL) in lung
cancer patients. Nonetheless, a substantial amount of alerts resulted in unnecessary con sults. To reduce the healthcare burden, we aimed to improve the alerting-algorithm through
the use of a Bayesian network (BN) to compute the probability that PRO symptoms indi cate an alarming situation.
Methods: A retrospective analysis was performed using data from the SYMPRO-Lung
study, which included 248 patients who collectively completed 7.239 weekly questionnaires
reporting their symptoms. The study focused on constructing a BN based on this data to
model the alerting-algorithm, with the goal of reducing unnecessary alerts. The BN’s per formance was evaluated against the alerting-algorithm. The learned relationships between
variables were used to infer the probability of the underlying reasons for symptoms and
alerts, to better understand the different alerts.
Results: In total 1326 alerts were sent by the alerting-algorithm, of which 468 (35%) were
found unnecessary by participants or healthcare practitioners (HCPs). The constructed BN
scored lower (accuracy 86% and precision 44%) on the performance metrics compared to
the alerting-algorithm (accuracy 94% and precision 86%). Treatment necessity exhibited
the highest likelihood of underlying symptoms and alert, followed by other reasons and
impaired emotional functioning, indicating varying alert contexts.
Conclusion: Although the BN can provide insight into the relationships between the vari ables in the alerting-algorithm and medical models, it did not perform better than the
alerting-algorithm. The BN found differences within both the necessary and unnecessary
alerts, showing alarming and non-alarming representations in both groups. Future work
should improve the clinical validity of its captured relationships between variables and ex plore a dynamic approach to the BN.
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Faculteit der Sociale Wetenschappen