Predicting ICD-9 Codes Using Self-Report of Patients
Source abstract
Abstract
The International Classification of Diseases (ICD) is a globally recognized medical classification system that aids in the identification of diseases and the regulation of health trends. The ICD framework makes it easy to keep track of records and evaluate medical data for evidence-based decision-making. Several methods have predicted ICD-9 codes based on the discharge summary, clinical notes, and nursing notes. In our study, our approach only utilizes the subjective component to predict ICD-9 codes. Data cleaning and segmentation, and Natural Language Processing (NLP) techniques are applied on the subjective component during the pre-processing. Our study builds the Long Short-Term Memory (LSTM) and the Gated Recurrent Unit (GRU) to develop a model for predicting ICD-9 codes. The ICD-9 codes contain different ICD levels such as chapter, block, three-digit code, and full code. The GRU model scores the highest recall of 57.91% in the chapter level and the top-10 experiment has a recall of 67.37%. Based on the subjective component, the model can help patients in the form of a remote assistance tool.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Machine Learning in Healthcare · Artificial Intelligence in Healthcare · Medical Coding and Health Information
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Graduate Institute of Applied Science and Engineering, Fu Jen Catholic University, New Taipei City 242062, Taiwan
Department of General Surgery, Shin Kong Wu Ho-Su Memorial Hospital, Taipei 111045, Taiwan
Department of Computer Science and Information Engineering, Fu Jen Catholic University, New Taipei City 242062, Taiwan
Funding
Ministry of Science and Technology, Taiwan
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