Representation Of Medical Information Research Articles

Medical Image Retrieval Using Empirical Mode Decomposition with Deep Convolutional Neural Network.

Content-based medical image retrieval (CBMIR) systems attempt to search medical image database to narrow the semantic gap in medical image analysis. The efficacy of high-level medical information representation using features is a major challenge in CBMIR systems. Features play a vital role in the accuracy and speed of the search process. In this paper, we propose a deep convolutional neural network- (CNN-) based framework to learn concise feature vector for medical image retrieval. The medical images are decomposed into five components using empirical mode decomposition (EMD). The deep CNN is trained in a supervised way with multicomponent input, and the learned features are used to retrieve medical images. The IRMA dataset, containing 11,000 X-ray images, 116 classes, is used to validate the proposed method. We achieve a total IRMA error of 43.21 and a mean average precision of 0.86 for retrieval task and IRMA error of 68.48 and F1 measure of 0.66 on classification task, which is the best result compared with existing literature for this dataset.

Information Is Selection-A Review of Basics Shows Substantial Potential for Improvement of Digital Information Representation.

Any piece of information is a selection from a set of possibilities. In this paper, this set is called a “domain”. Digital information consists of number sequences, which are selections from a domain. At present, these number sequences are defined contextually in a very variable way, which impairs their comparability. Therefore, global uniformly defined “domain vectors” (DVs), with a structure containing a “Uniform Locator” (“UL”), referred to as “UL plus number sequence”, are proposed. The “UL” is an efficient global pointer to the uniform online definition of the subsequent number sequence. DVs are globally defined, identified, comparable, and searchable by criteria which users can define online. In medicine, for example, patients, doctors, and medical specialists can define DVs online and can, therefore, form global criteria which are important for certain diagnoses. This allows for the immediate generation of precise diagnostic specific statistics of “similar medical cases”, in order to discern the best therapy. The introduction of a compact DV data structure may substantially improve the digital representation of medical information.

Visual representation of medical information: the importance of considering the end-user in the design of medical illustrations

This practice led research project explored visual representation through illustrations designed to communicate often complex medical information for different users within Auckland City Hospital, New Zealand. Media and tools were manipulated to affect varying degrees of naturalism or abstraction from reality in the creation of illustrations for a variety of real-life clinical projects, and user feedback on illustration preference gathered from both medical professionals and patients. While all users preferred the most realistic representations of medical information from the illustrations presented, patients often favoured illustrations that depicted a greater amount of information than professionals suggested was necessary.

Language Representation of Medical Information in English Texts for Patients

Language Representation of Medical Information in English Texts for Patients

Domain-specific analytical language modeling—The chief complaint as a case study

Purpose A large share of the information in electronic medical records (EMRs) consists of free-text compositions. From a computational point-of-view, the continuing prevalence of free-text entry is a major hindrance when the goal is to increase automation in EMRs. However, the efforts in developing standards for the structured representation of medical information have not proven to be a panacea. The information space of clinical medicine is very diverse and constantly evolving, making it challenging to develop standards for the domain. This paper reports a study aiming to increase automation in the EMR through the computational understanding of specific class of medical text in English, namely emergency department chief complaints. Methods We apply domain-specific analytical modeling for the computational understanding of chief complaints. We evaluate the performance of this approach in the automatic classification of chief complaints, e.g., for use in automatic syndromic surveillance. Results The evaluation in a multi-hospital setting showed that the presented algorithm was accurate in terms of classification correctness. Also, use of approximate matching in the algorithm to cope with typographic variance did not affect classification correctness while increasing classification completeness.

An event model of medical information representation.

Develop a model for structured and encoded representation of medical information that supports human review, decision support applications, ad hoc queries, statistical analysis, and natural-language processing. A medical information representation model was developed from manual and semiautomated analysis of patient data. The key assumption of the model is that medical information can be represented as a series of linked events. The event representation has two main components. The first component is a frame or template definition that specifies the attributes of the event. The second component is a structured vocabulary, the terms of which are taken as the values of the slots in the event template structure. Individual event instances are linked by specific named relationships. The proposed model was used to represent a chest-radiograph report. The event model of medical information representation provides a mechanism for formal definition of the logical structure of medical data and allows explicit time-oriented and associative relationships between event instances.

Structuring medical information into a language-independent database.

We describe one approach for natural language processing and database representation of medical information. The method is based in the semantic analysis of the statements and in the identification of patterns. The processed information is indexed and structured into a frame format containing semantic slots into the database. We tested our method analysing sentences describing symptoms extracted from case reports presented in the volume 328 of the New England Journal of Medicine. The results are: 73.41% of the sentences were formatted; 81.05% of the analysed words were identified; and 95.33% of the medical terms were indexed. We conclude that this semantic approach is not only efficient for processing natural language texts, but it can also be used for the organization of medical information using a language-independent format. This interlingua that is set into the database can be applied to semantic data retrieval; serving as a basis to organize a problem-orientated medical record; displaying simultaneously the DB information into two or more languages; information interchanging among different human languages and computers; and automatic translation, among others.