We are currently witnessing an immense proliferation of natural language processing (NLP) applications. Natural language generation (NLG) has emerged from NLP and is now commonly utilized in various applications, including chatting applications. The objective of this paper is to propose a deep learning-based language generation model that simplifies the process of writing medical recommendations for doctors in an Arabic context, to improve service satisfaction and patient-doctor interactions. The developed language generation model is a predictive text system intended for next word prediction in a telemedicine service. Altibbi—a digital platform for telemedicine and teleconsultations services in the Middle East and the North Africa (MENA) region—was utilized as a case study for the textual prediction process. The proposed model was trained using data obtained from Altibbi databases related to medical recommendations, particularly gynecology, dermatology, psychiatric diseases, urology, and internist diseases. Variants of deep learning models were implemented and optimized for next word prediction, based on the unidirectional and bidirectional long short-term memory (LSTM and BiLSTM), the one-dimensional convolutional neural network (CONV1D), and a combination of LSTM and CONV1D (LSTM-CONV1D). The algorithms were trained using two versions of the datasets (i.e., 3-gram and 4-gram representations) and evaluated in terms of their training accuracy and loss, validation accuracy and loss, and testing accuracy per their matching scores. The proposed models’ performances were comparable. CONV1D produced the most promising matching score.

中文翻译：

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远程医疗中医学建议的预测文本系统：阿拉伯语环境中的深度学习方法

我们目前正在目睹自然语言处理 (NLP) 应用程序的大量涌现。自然语言生成 (NLG) 是从 NLP 中出现的，现在广泛用于各种应用程序，包括聊天应用程序。本文的目的是提出一种基于深度学习的语言生成模型，该模型简化了在阿拉伯语环境中为医生撰写医疗建议的过程，以提高服务满意度和医患互动。开发的语言生成模型是一种预测文本系统，用于远程医疗服务中的下一个单词预测。Altibbi——中东和北非 (MENA) 地区远程医疗和远程会诊服务的数字平台——被用作文本预测过程的案例研究。所提出的模型是使用从 Altibbi 数据库获得的与医疗建议相关的数据进行训练的，特别是妇科、皮肤科、精神疾病、泌尿科和内科疾病。基于单向和双向长短期记忆（LSTM 和 BiLSTM）、一维卷积神经网络 (CONV1D) 以及 LSTM 和 CONV1D 的组合，为下一个单词预测实现和优化了深度学习模型的变体。 LSTM-CONV1D）。这些算法使用两个版本的数据集（即 3-gram 和 4-gram 表示）进行训练，并根据其匹配分数的训练准确度和损失、验证准确度和损失以及测试准确度进行评估。所提出模型的性能具有可比性。CONV1D 产生了最有希望的匹配分数。