The majority of medical documents and electronic health records are in text format that poses a challenge for data processing and finding relevant documents. Looking for ways to automatically retrieve the enormous amount of health and medical knowledge has always been an intriguing topic. Powerful methods have been developed in recent years to make the text processing automatic. One of the popular approaches to retrieve information based on discovering the themes in health and medical corpora is topic modeling; however, this approach still needs new perspectives. In this research, we describe fuzzy latent semantic analysis (FLSA), a novel approach in topic modeling using fuzzy perspective. FLSA can handle health and medical corpora redundancy issue and provides a new method to estimate the number of topics. The quantitative evaluations show that FLSA produces superior performance and features to latent Dirichlet allocation, the most popular topic model.
Topic modeling defines each topic as probability distribution over words and a document as probability distribution over topics. In health and medical text mining, latent Dirichlet allocation (LDA) shows better performance than other topic models @cite_37 .
@cite_37
2082369396
Large amount of electronic clinical data encompass important information in free text format. To be able to help guide medical decision-making, text needs to be efficiently processed and coded. In this research, we investigate techniques to improve classification of Emergency Department computed topography (CT) reports. The proposed system uses Natural Language Processing (NLP) to generate structured output from patient reports and then applies machine learning techniques to code for the presence of clinically important injuries for traumatic orbital fracture victims. Topic modeling of the corpora is also utilized as an alternative representation of the patient reports. Our results show that both NLP and topic modeling improve raw text classification results. Within NLP features, filtering the codes using modifiers produces the best performance. Topic modeling, on the other hand, shows mixed results. Topic vectors provide good dimensionality reduction and get comparable classification results as with NLP features. However, binary topic classification fails to improve upon raw text classification.