Approaches to text mining for clinical medical records

The 21st Annual ACM Symposium on Applied Computing 2006, Technical tracks on Computer Applications in Health Care (CAHC 2006), Dijon, France, April 23 -27, 2006. Retrieved 6/21/2006 from http://www.ischool.drexel.edu/faculty/hhan/SAC2006_CAHC.pdf.Clinical medical records contain a wealth of information, largely in free-text form. Means to extract structured information from free-text records is an important research endeavor. In this paper, we describe a MEDical Information Extraction (MedIE) system that extracts and mines a variety of patient information with breast complaints from free-text clinical records. MedIE is a part of medical text mining project being conducted in Drexel University. Three approaches are proposed to solve different IE tasks and very good performance (precision and recall) was achieved. A graph-based approach which uses the parsing result of link-grammar parser was invented for relation extraction; high accuracy was achieved. A simple but efficient ontology-based approach was adopted to extract medical terms of interest. Finally, an NLP-based feature extraction method coupled with an ID3-based decision tree was used to perform text classification

The Research on the Detection of Noteworthy Symptom Descriptions

The advance of mobile devices and communication technologies enable patients to communicate with their doctors in a more convenient way. We have developed an App that allows patients to record their symptoms and submit them to their doctors. Physicians can keep track of patients’ conditions by looking at the self-report messages. Nevertheless, physicians are usually busy and may be overwhelmed by the large amount of incoming messages. As a result, critical messages may not receive immediate attentions, and patient care is compromised. It is imperative to identify the messages that require physicians’ attention, called noteworthy messages. In this research, we propose an approach that applies text-mining technologies to identify medical symptoms conveyed in the messages and their associated sentiment orientation, as well as other factors. Noteworthy messages are subsequently characterized by symptom sentiment and symptom change features. We then construct a prediction model to identify messages that are noteworthy to the physicians. We show from our experiments using data collected from a teaching hospital in Taiwan that the different features have different degrees of impact on the performance of the prediction model, and our proposed approach can effectively identify noteworthy messages

Automatically detect diagnostic patterns based on clinical notes through Text Mining

The importance of standardized treatment for patients is huge because it can reduce waiting times, costs in hospitals and make treatment more effective for patients. According to these patterns, the creation of a tool that can make the admission and interpretation of free text will become an important step in the medical field. For the analysis of the unstructured text, the "RapidMiner" tool was used. Following the text analysis, the word frequency technique will be used in the reports and the respective word counts, as well as the cluster analysis that allows the creation of combinations of words. For the modeling we used several Text Mining techniques focused on the main algorithms, since these are properly scientifically proven and that, normally, they are able to obtain better results. (C) 2019 The Authors. Published by Elsevier B.V.This work has been supported by national funds through FCT -Fundacao para a Ciencia e Tecnologia within the Project Scope: UID/CEC/00319/2019 and Deus ex Machina (DEM): Symbiotic technology for societal efficiency gains -NORTE-01-0145-FEDER-000026

Conceptual graph formalism for financial text representation

We present an approach to automatically transform a financial text into conceptual graph formalism.The approach exploits the constituent structure of sentences and general English grammar rules to perform the transformation.We suggest face validation and traces as the evaluation method to be performed on the resulting formalism to validate its accuracy. We also discuss the potential manipulation and application of the constructed conceptual graph database

Dissimilarity algorithm on conceptual graphs to mine text outliers

The graphical text representation method such as Conceptual Graphs (CGs) attempts to capture the structure and semantics of documents.As such, they are the preferred text representation approach for a wide range of problems namely in natural language processing, information retrieval and text mining.In a number of these applications, it is necessary to measure the dissimilarity (or similarity) between knowledge represented in the CGs.In this paper, we would like to present a dissimilarity algorithm to detect outliers from a collection of text represented with Conceptual Graph Interchange Format (CGIF).In order to avoid the NP-complete problem of graph matching algorithm, we introduce the use of a standard CG in the dissimilarity computation.We evaluate our method in the context of analyzing real world financial statements for identifying outlying performance indicators.For evaluation purposes, we compare the proposed dissimilarity function with a dice-coefficient similarity function used in a related previous work.Experimental results indicate that our method outperforms the existing method and correlates better to human judgements. In Comparison to other text outlier detection method, this approach managed to capture the semantics of documents through the use of CGs and is convenient to detect outliers through a simple dissimilarity function.Furthermore, our proposed algorithm retains a linear complexity with the increasing number of CGs

Sistema predictiu per a evolució de pacients. Aplicació al cas d'asma infantil

[CATALÀ] S'ha dissenyat una aplicació que permet treballar amb dades de pacients i episodis, preprocessar-les i entrenar models amb algorismes d'aprenentatge automàtic que ofereixen bons resultats. S'ha experimentat amb dades de pacients d'asma infantil de l'hospital de Sant Joan de Déu.[ANGLÈS] We've developed an application that can deal with patient data, preprocess it and train models with machine learning algorithms that have given good results. We've experimented with data from Hospital de Sant Joan de Déu of child patients that suffer from Asthma

Concept graphs: Applications to biomedical text categorization and concept extraction

As science advances, the underlying literature grows rapidly providing valuable knowledge mines for researchers and practitioners. The text content that makes up these knowledge collections is often unstructured and, thus, extracting relevant or novel information could be nontrivial and costly. In addition, human knowledge and expertise are being transformed into structured digital information in the form of vocabulary databases and ontologies. These knowledge bases hold substantial hierarchical and semantic relationships of common domain concepts. Consequently, automating learning tasks could be reinforced with those knowledge bases through constructing human-like representations of knowledge. This allows developing algorithms that simulate the human reasoning tasks of content perception, concept identification, and classification. This study explores the representation of text documents using concept graphs that are constructed with the help of a domain ontology. In particular, the target data sets are collections of biomedical text documents, and the domain ontology is a collection of predefined biomedical concepts and relationships among them. The proposed representation preserves those relationships and allows using the structural features of graphs in text mining and learning algorithms. Those features emphasize the significance of the underlying relationship information that exists in the text content behind the interrelated topics and concepts of a text document. The experiments presented in this study include text categorization and concept extraction applied on biomedical data sets. The experimental results demonstrate how the relationships extracted from text and captured in graph structures can be used to improve the performance of the aforementioned applications. The discussed techniques can be used in creating and maintaining digital libraries through enhancing indexing, retrieval, and management of documents as well as in a broad range of domain-specific applications such as drug discovery, hypothesis generation, and the analysis of molecular structures in chemoinformatics

Utilização de técnicas de text mining sobre registos clínicos de epilepsia em crianças, para auxílio ao diagnóstico e classificação

Dissertação apresentado à Escola Superior de Tecnologia e Gestão do IPL para obtenção do grau de Mestre em Engenharia Informática - Computação Móvel, orientada pelo Doutor Rui Rijo e pela Doutora Catarina Silva.A informação médica tem aumentado continuamente ao longo do tempo, produzindo-se quantidades elevadíssimas de dados. A análise e a extração desses dados oferecem possibilidades de reduzir o esforço e o tempo na sugestão e classificação de um diagnóstico. O processamento dos dados médicos representa um grande desafio, considerando que estes dados são geralmente apresentados em texto livre e com vocabulário técnico específico. Entre os dados mais ricos e relevantes encontram-se os registos clínicos. A análise de registos clínicos é complexa pois para a realização de um diagnóstico correto é necessário ter em conta várias características como sintomas, exames, historial do paciente, tratamentos, medicamentos, entre outros. Além disso, esta análise requer um domínio de diferentes áreas de conhecimento para a realização de um diagnóstico fiável, entre outras data mining, text mining, registos clínicos eletrónicos, e a área clínica. Estes diagnósticos devem ainda ser classificados segundo normalizações, para que o médico possa tomar procedimentos e prescrever tratamentos mais corretos segundo determinadas classificações. O presente trabalho sugere uma abordagem que incide na área de epilepsia infantil, analisando e extraindo informação relevante de registos clínicos eletrónicos, para ajudar os médicos a tomar decisões, tais como identificar e classificar diagnósticos, ajudar na prescrição de tratamentos, medicamentos e na sugestão de procedimentos. A epilepsia infantil é complexa e não linear, uma vez que os médicos têm de analisar diferentes causas, entre outras, genéticas, estruturais, metabólicas, e um diagnóstico errado pode modificar a vida de uma criança. Os registos clínicos reais e anónimos foram fornecidos e transcritos com a ajuda do serviço de pediatria do Hospital Santo André. Os resultados alcançados são promissores, estando no entanto ainda longe dos desejados para permitir uma sugestão e classificação de diagnósticos de forma precisa e segura. Esta abordagem permite ainda uma classificação dos diagnósticos baseadas em normalizações, de forma a sugerir os melhores procedimentos, prognósticos e tratamentos dependendo da classificação encontrada. Desta forma, será possível ajudar a reduzir o erro médico na classificação de diagnósticos, o erro na prescrição, e aumentar a eficácia no processamento dos dados médicos, poupando tempo e dinheiro