Mining and fusing unstructured online reviews and structured public index data for hospital selection

In the era of big data, publicly available data from official hospital sources and data from online reviews are easy to influence a patient's decision in choosing a hospital. However, existing research has rarely comprehensively considered the combined influence of these two factors. This paper proposes a hospital selection approach based on a fuzzy multi-criterion decision-making method, which considers sentiment evaluation values of unstructured data from online reviews and structured data of public indexes simultaneously. First, online reviews of general and specialized hospitals are collected and processed to get evaluation attributes of hospital and attribute weights. Then, text reviews are taken to classify topics and sentiments using logistic regression, light gradient boosting machine, and bidirectional encoder representations from transformers. The results of sentiment analysis are quantified using triangular fuzzy numbers to express evaluation values of hospitals. Based on patients' preferences for online reviews and structured data on publicly available attributes of hospitals, final preference scores of hospitals are obtained using the fuzzy technique for order preference by similarity to ideal solution method. A case study is performed to illustrate the applicability of the proposed method. Robustness analysis from patient perspective and hospital perspective are executed to validate the effectiveness of the method.

Automated summary

In this paper, a hospital selection approach based on a fuzzy multi-criterion decision-making method is proposed. This approach considers sentiment evaluation values of unstructured data from online reviews and structured data of public indexes simultaneously. The methodology involves collecting and processing online reviews, classifying topics and sentiments, quantifying sentiment analysis results using fuzzy numbers, and obtaining final preference scores of hospitals based on patients' preferences. A case study and robustness analysis are conducted to validate the effectiveness of the method.