Machine Learning Based Sentiment Text Classification for Evaluating Treatment Quality of Discharge Summary

TLDR

Discharge summaries serve as health sensors for assessing treatment quality, yet their unstructured natural‑language content makes automatic information extraction challenging. The study proposes a novel sentiment‑analysis approach to classify discharge summaries and evaluate treatment quality. The method combines vector‑space representations, statistical lexicon construction, association‑rule mining, and an extreme‑learning‑machine autoencoder to perform sentiment‑based classification of discharge summaries. Experiments show the approach achieves an F1 score of 0.89 with strong TPR and low FPR, outperforming state‑of‑the‑art methods and effectively detecting positive, negative, and neutral terms at the sentence level.

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.