Abstract
Patients’ discharge summaries (documents) are health sensors that are used for measuring the quality of treatment in medical centers. However, extracting information automatically from discharge summaries with unstructured natural language is considered challenging. These kinds of documents include various aspects of patient information that could be used to test the treatment quality for improving medical-related decisions. One of the significant techniques in literature for discharge summaries classification is feature extraction techniques from the domain of natural language processing on text data. We propose a novel sentiment analysis method for discharge summaries classification that relies on vector space models, statistical methods, association rule, and extreme learning machine autoencoder (ELM-AE). Our novel hybrid model is based on statistical methods that build the lexicon in a domain related to health and medical records. Meanwhile, our method examines treatment quality based on an idea inspired by sentiment analysis. Experiments prove that our proposed method obtains a higher F1 value of 0.89 with good TPR (True Positive Rate) and FPR (False Positive Rate) values compared with various well-known state-of-the-art methods with different size of training and testing datasets. The results also prove that our method provides a flexible and effective technique to examine treatment quality based on positive, negative, and neutral terms for sentence-level in each discharge summary.
Highlights
Discharge summaries (DS) are a part of clinical notes which have an unstructured design.These kinds of documents include information about drugs, treatments, and diseases
Our research is focused on experimenting with the health care and treatment quality based on techniques of sentiment analysis
We focused on the text challenges based on the discharge summaries to examine and evaluate the treatment quality
Summary
Discharge summaries (DS) are a part of clinical notes which have an unstructured design These kinds of documents include information about drugs, treatments, and diseases. Extraction of useful knowledge from a huge number of unstructured discharge summaries has become a significant challenge in recent times [3] This extracted knowledge is significant in various stages of a patient’s life, for example, it is an important factor for the patient’s health when physicians want to get the information about a patient’s health progress (during the treatment stages). Extracting knowledge from these summaries allows the evaluation of the treatment quality in a better way that can benefit both patients and health centers [4,5]
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have