Integrating Medical Code Descriptions and Building Text Classification Models for Diagnostic Decision Support

Accurate diagnosis plays an important role in the clinical decision-making process. To reduce diagnostic errors, the Diagnostic Decision Support System (DDSS) is designed to assist clinicians with the diagnostic decision-making. Recently, DDSS is developed by diagnosis classification models which employ deep learning networks training on real-world patient-visit data which are often in the form of medical codes. We realize the importance of medical code descriptions for diagnosis classification and believe that incorporating medical code descriptions can improve diagnostic accuracy. Incorporating medical code descriptions by mapping medical codes to textual descriptions, we build diagnosis classification models using 3 text classification methods (TextCNN, TextRCNN and CharCNN) on Ambulatory Health Care (AHC) data with 629,827 patient-visits in the United States, to provide diagnostic decision support under the certain situation when patients' data are recorded in the form of medical codes. The experiments demonstrate the improved accuracy performance and time efficiency of our models compared the baseline models (XGBoost and MLP) taking only medical codes as the input.