Abstract
Healthcare systems generate an immense volume of data, which presents a unique opportunity to make a significant impact. This chapter examines the role of information retrieval systems in healthcare, specifically focusing on how text analysis can be utilized to enhance the understanding of medical data. By employing advanced text mining tools, this chapter demonstrates how we can extract valuable insights from these complex documents. It also presents text analytics as a solution-oriented approach, particularly beneficial in managing crises within healthcare systems and in making informed decisions based on accurate data analysis. The technical foundation of the study is rooted in the fields of natural language processing and artificial intelligence, with a focus on methodologies related to the semantics of words and text (e.g., text corpus, dictionaries, and text embeddings). Through this exploration, the chapter aims to highlight the transformative potential of text analysis and information retrieval systems in revolutionizing healthcare data understanding.
TopMain Focus Of The Chapter
This chapter aims to illuminate the potential of advanced text retrieval and analysis mechanisms in healthcare. A key challenge in this domain is ensuring easy and streamlined access to various healthcare-related text resources, such as insurance documents, hospital terms and conditions, health reports, and electronic medical records. By employing text mining tools, not only can we extract insights from these complex documents, but we can also simplify their comprehension. This chapter delves into tools like document summarizers, word maps, and search engines, elaborating on their suitability for these purposes. Traditionally, text analytic tools have been viewed as a component of broader data analysis efforts. This chapter, however, seeks to emphasize the direct application of these tools in healthcare, highlighting their practical utility in this specific context. It presents text analytics as a solution-oriented approach, particularly useful in managing crises within healthcare. For instance, while the use of text search tools to determine popularity trends is common, this chapter introduces their application in identifying under-researched articles to uncover fresh perspectives or discover new solutions. The technical foundation of this study lies within the fields of natural language processing (NLP) in artificial intelligence (AI). It predominantly focuses on methodologies related to the semantics of words and text, encompassing aspects such as text corpus and dictionaries. A significant concept discussed throughout the chapter is text embeddings, which involve encoding text in a machine-understandable format. These embeddings vary, with some based on word frequency, while others capture meanings and contexts. In the context of healthcare data, both approaches are shown to be effective.
Key Terms in this Chapter
Similarity Score: A quantitative measure indicating the degree of resemblance or correlation between two entities, used in fields like information retrieval and machine learning to assess similarity in a given context.
Natural Language Processing: A field of artificial intelligence focused on the interaction between computers and human language. It involves computational techniques for analyzing, interpreting, and synthesizing human language, enabling machines to understand and respond to text and spoken words.
Query Processing Model: These models refer to methodologies and algorithms used to handle and execute user queries in database management systems. They encompass the steps involved in interpreting, optimizing, and executing queries to retrieve or manipulate data efficiently.
Co-Occurrence Matrix: A co-occurrence matrix is a square matrix that represents the frequency of how often pairs of items, such as words, occur together in a given dataset or context. In natural language processing, it is used to analyze relationships between words in a text corpus. Each element in the matrix reflects the number of times two items appear together within a specified window of proximity, aiding in capturing semantic relationships and contextual information.
Word Embedding: A technique in natural language processing that represents words as vectors in a high-dimensional space. This captures semantic relationships and contextual meanings to enhance language understanding and machine learning applications.
Text Analytics: Processes related to deriving actionable insights from text data within a specific domain or across multiple domains.
Information Retrieval: This is the process of obtaining relevant information from a large repository or dataset. It involves systematic and efficient extraction of data, documents, or resources that match a user's information needs, often through search engines, databases, or other systems. Techniques include indexing, querying, and ranking algorithms to organize and retrieve information based on relevance.
Sentiment Analysis: This automated process determines and categorizes the emotional tone or subjective opinions in text. It’s often used to understand whether the sentiment expressed is positive, negative, or neutral.