Bayesian multitask learning for medicine recommendation based on online patient reviews.

ABSTRACT:

Motivation

We propose a drug recommendation model that integrates information from both structured data (patient demographic information) and unstructured texts (patient reviews). It is based on multitask learning to predict review ratings of several satisfaction-related measures for a given medicine, where related tasks can learn from each other for prediction. The learned models can then be applied to new patients for drug recommendation. This is fundamentally different from most recommender systems in e-commerce, which do not work well for new customers (referred to as the cold-start problem). To extract information from review texts, we employ both topic modeling and sentiment analysis. We further incorporate variable selection into the model via Bayesian LASSO, which aims to f

SUBMITTER: Cheng Y 

PROVIDER: S-EPMC10425196 | biostudies-literature | 2023 Aug

REPOSITORIES: biostudies-literature