Text Classification of Patient Experience Comments in Saudi Dialect Using Deep Learning Techniques

Abstract

Improving the quality of healthcare services is of the utmost importance in healthcare systems. Patient experience is a key aspect that should be gauged and monitored continuously. However, the measurement of such a vital indicator typically cannot be carried out directly, instead being derived from the opinions of patients who usually express their experience in free text. When it comes to patient comments written in the Arabic language, the currently used strategy to classify Arabic comments is totally reliant on human annotation, which is time-consuming and prone to subjectivity and error. Thus, fully using the value of patient feedback in a timely manner is difficult. This paper addresses the problem of classifying patient experience (PX) comments written in Arabic into 25 classes by using deep learning- and BERT-based models. A real-world data set of patient comments is obtained from the Saudi Ministry of Health for this purpose. Features are extracted from the data set, then used to train deep learning-based classifiers—including BiLSTM and BiGRU—for which pre-trained static word embedding and pre-training vector word embeddings are utilized. Furthermore, we utilize several Arabic pre-trained BERT models, in addition to building PX_BERT, a customized BERT model using the PX unlabeled database. From the experimental results for the 28 classifiers built in this study, the best-performing models (based on the F1 score) are found to be PX_BERT and AraBERTv02. To the best of our knowledge, this is the first study to tackle PX comment classification for the Arabic language.

Enhancing Arabic Dialect Detection on Social Media: A Hybrid Model with an Attention Mechanism

“…Many attempts have been proposed in the area of automatic dialect identification (ADI), and early uses are based on dictionaries, rules, and language modeling [5][6][7][8][9][10]; more recently, a shift was made toward employing machine learning techniques [11][12][13][14][15][16][17][18][19][20][21][22][23][24], deep learning approaches [25][26][27][28][29][30][31][32][33][34][35][36][37][38][39][40], and transfer learning methods [41][42][43][44][45][46][47][48][49]. Many of these investigations utilize prominent and accessible datasets, such as MADAR [49], NADI [50][51]…”