Buıldıng Bayesıan Networks Based On Patıent Reported Outcome Questıonnaıres For Musculo-Skeletal Condıtıons
Özet
Machine learning (ML) which is a branch of artificial intelligence (AI), has been an
important approach used in the medical domain. ML approaches learn from historical
data to evaluate and predict patient status. These approaches have been successful in
medical domains, such as radiology and dermatology, where a large amount of data exists
with clearly labelled patient outcomes. However, such clearly labelled outcome data do
not exist in large amounts in most medical domains. Patient reported outcome measures
(PROMS) are the primary way to assess patient outcomes in many medical areas. Filling
in PROMs regularly and repetitively can be difficult due to time and cognitive-load
requirements. Considering that some PROMs contain over 30 questions, collecting large
amounts of patient outcome data can be difficult in these domains. This study proposes
an approach for collecting patient outcome data with less time and cognitive-load
requirements. In this context, an ML approach called Bayesian networks (BNs) is used to
predict patient outcomes with missing PROM inputs, and to identify the most informative
PROM questions for specific patients. Also, random questions were selected from the
PROMs and these questions were used to determine the patient status. The obtained estimation results were compared with the estimation results obtained by using the most
informative questions. The proposed approach has been applied to PROMS used in the
musculo-skeletal domain. Results were evaluated by cross validation method. Crossvalidation
results show that the proposed approach can accurately predict patient
outcomes with fewer PROM questions.
