In general, physicians make a preliminary diagnosis based on patients’ admission narratives and admission conditions, largely depending on their experiences and professional knowledge. An automatic and accurate tentative diagnosis based on clinical narratives would be of great importance to physicians, particularly in the shortage of medical resources. Despite its great value, little work has been conducted on this diagnosis method. Thus, in this study, we propose a fusion model that integrates the semantic and symptom features contained in the clinical text. The semantic features of the input text are initially captured by an attention-based Bidirectional Long Short-Term Memory (BiLSTM) network. The symptom concepts, recognized from the input text, are then vectorized by using the term frequency-inverse document frequency method based on the relations between symptoms and diseases. Finally, two fusion strategies are utilized to recommend the most potential candidate for the international classification of diseases code. Model training and evaluation are performed on a public clinical dataset. The results show that both fusion strategies achieved a promising performance, in which the best performance obtained a top-3 accuracy of 0.7412.

The explosion of digital healthcare data has led to a surge of data-driven medical research based on machine learning. In recent years, as a powerful technique for big data, deep learning has gained a central position in machine learning circles for its great advantages in feature representation and pattern recognition. This article presents a comprehensive overview of studies that employ deep learning methods to deal with clinical data. Firstly, based on the analysis of the characteristics of clinical data, various types of clinical data (e.g., medical images, clinical notes, lab results, vital signs, and demographic informatics) are discussed and details provided of some public clinical datasets. Secondly, a brief review of common deep learning models and their characteristics is conducted. Then, considering the wide range of clinical research and the diversity of data types, several deep learning applications for clinical data are illustrated: auxiliary diagnosis, prognosis, early warning, and other tasks. Although there are challenges involved in applying deep learning techniques to clinical data, it is still worthwhile to look forward to a promising future for deep learning applications in clinical big data in the direction of precision medicine.