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Stroke is one of the leading causes of death and impairments worldwide. After hospital discharge, it is necessary to monitor these patients during their recovery. This research addresses the implementation of a mobile app, entitled 'Quer N0 AVC', to improve the quality of stroke patient care in Joinville, Brazil. The study method was divided into two parts. The adaptation phase included all the necessary information in the app for monitoring stroke patients. The implementation phase aimed to prepare a routine for the Quer mobile app installation. One of the questionnaires collected data from 42 patients and identified that before hospital admission 29% of them did not have medical appointments, 36% had one or two appointments, 11% had three appointments, and 24% had four or more appointments. This research portrayed adaptation feasibility and the implementation of a cell phone app for following up on stroke patients. [ABSTRACT FROM AUTHOR]

Unstructured data in electronic health records, represented by clinical texts, are a vast source of healthcare information because they describe a patient's journey, including clinical findings, procedures, and information about the continuity of care. The publication of several studies on temporal relation extraction from clinical texts during the last decade and the realization of multiple shared tasks highlight the importance of this research theme. Therefore, we propose a review of temporal relation extraction in clinical texts. We analyzed 105 articles and verified that relations between events and document creation time, a coarse temporality type, were addressed with traditional machine learning-based models with few recent initiatives to push the state-of-the-art with deep learning-based models. For temporal relations between entities (event and temporal expressions) in the document, factors such as dataset imbalance because of candidate pair generation and task complexity directly affect the system's performance. The state-of-the-art resides on attention-based models, with contextualized word representations being fine-tuned for temporal relation extraction. However, further experiments and advances in the research topic are required until real-time clinical domain applications are released. Furthermore, most of the publications mainly reside on the same dataset, hindering the need for new annotation projects that provide datasets for different medical specialties, clinical text types, and even languages. [ABSTRACT FROM AUTHOR]

Decision-making in the field of healthcare is a very complex activity. Several tools have been developed to support the decision-making process. DMN, a modeling technique focused on decisions, is among these and has been gaining prominence in both, literature and business, as has the multi-criteria method PROMETHEE II that helps decision-makers with multicriteria in analyses. Thus, this research targets combining these two techniques and analyzing the decision support that these two tools afford together. The diagnostic stage of stroke patients was used to perform this work. The research demonstrated that this proposal can drive major gains in efficiency and assertiveness in decision-making in timesensitive hospital processes. After all, there is a noticeable dearth of hospitals with specialized teams as well as a shortfall of adequate infrastructure for this treatment. [ABSTRACT FROM AUTHOR]

Automatic detection of negated content is often a prerequisite in information extraction systems in various domains. In the biomedical domain especially, this task is important because negation plays an important role. In this work, two main contributions are proposed. First, we work with languages which have been poorly addressed up to now: Brazilian Portuguese and French. Thus, we developed new corpora for these two languages which have been manually annotated for marking up the negation cues and their scope. Second, we propose automatic methods based on supervised machine learning approaches for the automatic detection of negation marks and of their scopes. The methods show to be robust in both languages (Brazilian Portuguese and French) and in cross-domain (general and biomedical languages) contexts. The approach is also validated on English data from the state of the art: it yields very good results and outperforms other existing approaches. Besides, the application is accessible and usable online. We assume that, through these issues (new annotated corpora, application accessible online, and cross-domain robustness), the reproducibility of the results and the robustness of the NLP applications will be augmented. [ABSTRACT FROM AUTHOR]

There are different methods to evaluate Health Information Systems (HIS), such as Quality Evaluation of software products, human factors, and socio-technical approaches. This work aims to identify the main aspects used to evaluate HIS, and whether there are relationships between issues considered in assessment of software quality and the ones applied specific to the health domain. This was an exploratory study that included a literature search related to HIS evaluation and software quality analyses applying the norms of the International Organization for Standardization (ISO/IEC), to identify aspects and features applied during the assessment process. The result is a proposal of an evaluation method based on the integration of these two evaluative approaches, combining or complementing the considered aspects. The method was applied to an evaluation of a natural language processing system to identify continuity of care in discharge summaries. [ABSTRACT FROM AUTHOR]

Introduction: Fibromyalgia Syndrome (FMS) is characterised by chronic widespread pain and tender points on palpation. FMS is associated with depression, anxiety, fatigue, sleep disorders, and functional bowel disorders that can intensify painful feelings, altering functional capacity and quality of life. The health provider usually identifies these conditions through assessment and monitoring tools and recordsthe results on paper. Failure to use electronic information systems, which have specific methodology for the integration of Health Information Systems, hampers the applicability of these methods in clinical practice and does not allow visualisation and comparison of the results in a simple manner. Objective: To propose an information system for assessing and monitoring patients with FMS (SISFIBRO). Method: The first phase consisted of data collection to select evaluation and monitoring methods used by doctors, physical therapists, rheumatologists, and specialists in fibromyalgia. Next, we performed the modelling of the system in the Unified Modelling Language (UML). Based on the diagram of classes, we performed conceptual data modelling with entity-relationship diagrams. The prototype was implemented with the SISFIBRO Oracle database. Below, we included data from 36 patients with FMS to analyse whether the system provided all of the information and necessary functions. Results: In Brazil, most of the professionals (82.4%) record and store patient data on paper charts, and 17.6% do so using electronic systems. The most widely used instruments and methods arethe visual analogue scale (VAS) SF-36, theFibromyalgia Impact Questionnaire (FIQ), evaluation of tender points according to the American College of Rheumatology (ACR), and the Beck Depression Scale; all are available in SISFIBRO. We developed 1 diagram of use, 1 diagram of classes, 6 diagrams of objects, and 5 diagrams of sequences. We decided that SISFIBRO must be integrated with the Electronic Health Record (EHR) for the recording, storing, sharing, and retrieving of the information. This integration will assist in the clinical practice of health professionals. Conclusion: It was possible to create a system with the reliability needed to support and improve the assessment and treatment of FMS. [ABSTRACT FROM AUTHOR]

Clinical guidelines are documents that assist healthcare professionals, facilitating and standardizing diagnosis, management, and treatment in specific areas. Computerized guidelines as decision support systems (DSS) attempt to increase the performance of tasks and facilitate the use of guidelines. Most DSS are not integrated into the electronic health record (EHR), ordering some degree of rework especially related to data collection. This study's objective was to present a method for integrating clinical guidelines into the EHR. The study developed first a way to identify data and rules contained in the guidelines, and then incorporate rules into an archetype-based EHR. The proposed method tested was anemia treatment in the Chronic Kidney Disease Guideline. The phases of the method are: data and rules identification; archetypes elaboration; rules definition and inclusion in inference engine; and DSS-EHR integration and validation. The main feature of the proposed method is that it is generic and can be applied toany type of guideline. [ABSTRACT FROM AUTHOR]

This study describes MappICNP, an automatic method for mapping between Brazilian Portuguese clinical narratives in free text and International Classification for Nursing Practice (ICNP) concepts. It's composed of six natural language processing rules, related to terms comparison. A set of 2,638 terms extracted from hospitals nursing notes was mapped. MappICNP helps to map 1,607 terms, 113 less than a manual approach. The results demostrate its advantages in minimizing the time spent and reducing the scope of analysis through candidate terms of ICNP. [ABSTRACT FROM AUTHOR]

Discharge summaries are an important clinical narrative as they include the continuity of care information. Identification of data contained in their text is a difficult task due to its freeform text and lack of consensus on essential content. This research proposes a rule-based method to verify the presence of information about continuity of care in Portuguese texts, applying Natural Language Processing (NLP) techniques, and based on an annotated medical corpus. After the experiments, 4 rules were defined and applied in the text of 200 summaries to identify if they have or not the continuity of care information. This process had resulted in Precision value of 84%, Recall value of 70%, Specificity value of 97% and F-Measure value of 76% related to algorithm evaluation. [ABSTRACT FROM AUTHOR]

Background: The objective of amputation and disarticulation is to improve health. However, these treatments are associated with significant mortality rates that vary in relation to risk factors. Objective: To identify associations between determinants of postoperative mortality after amputation surgery. Methods: Case-control study (death vs. no death) considering data from 173 patients who underwent amputation surgery at a public hospital in Santa Catarina state, Brazil. These data were analyzed using a data mining approach to discover association rules and epidemiologic association metrics. Results: The main determinants were age > 60 years (odds ratio (OR) = 3.0), female sex (OR = 2.0), low education, hypertension (OR = 3.0), diabetes (OR = 1.6), and smoking (OR = 1.8). Among patients aged 60-69 years, 87.9% survived to discharge from hospital. The exceptions occurred when patients in this age range had peripheral vascular disease. The same was true when age was > 70 years, among whom diagnoses of embolism and thrombosis of arteries of the lower extremities were the exception factors (associated with death). The most common pathologies associated with death were vascular disease (47.0%) and diabetes (29.4%), heart disease (relative risk = 11.4), renal disease (OR = 10.4), and lung disease (OR = 5.2). Proximal surgeries were more strongly associated with death than distal ones. Among the deaths, 76.0% had been given spinal anesthesia and 24.0% general anesthesia. Conclusion: Data mining enabled identification of associations between death and a variety of different variables and diagnostic hypotheses; for example, age > 70 years and diagnosis of embolism and thrombosis of arteries of the lower extremities. [ABSTRACT FROM AUTHOR]

Introduction: Leprosy reactions (LR) are severe episodes of intense activation of the host inflammatory response of uncertain etiology, today the leading cause of permanent nerve damage in leprosy patients. Several genetic and non-genetic risk factors for LR have been described; however, there are limited attempts to combine this information to estimate the risk of a leprosy patient developing LR. Here we present an artificial intelligence (AI)-based system that can assess LR risk using clinical, demographic, and genetic data., Methods: The study includes four datasets from different regions of Brazil, totalizing 1,450 leprosy patients followed prospectively for at least 2 years to assess the occurrence of LR. Data mining using WEKA software was performed following a two-step protocol to select the variables included in the AI system, based on Bayesian Networks, and developed using the NETICA software., Results: Analysis of the complete database resulted in a system able to estimate LR risk with 82.7% accuracy, 79.3% sensitivity, and 86.2% specificity. When using only databases for which host genetic information associated with LR was included, the performance increased to 87.7% accuracy, 85.7% sensitivity, and 89.4% specificity., Conclusion: We produced an easy-to-use, online, free-access system that identifies leprosy patients at risk of developing LR. Risk assessment of LR for individual patients may detect candidates for close monitoring, with a potentially positive impact on the prevention of permanent disabilities, the quality of life of the patients, and upon leprosy control programs., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher., (Copyright © 2023 de Andrade Rodrigues, Heise, Hartmann, Rocha, Olandoski, de Araújo Stefani, Latini, Soares, Belone, Rosa, de Andrade Pontes, de Sá Gonçalves, Cruz, Penna, Carvalho, Fava, Bührer-Sékula, Penna, Moro, Nievola and Mira.)

Stroke is one of the leading causes of death and impairments worldwide. After hospital discharge, it is necessary to monitor these patients during their recovery. This research addresses the implementation of a mobile app, entitled 'Quer N0 AVC', to improve the quality of stroke patient care in Joinville, Brazil. The study method was divided into two parts. The adaptation phase included all the necessary information in the app for monitoring stroke patients. The implementation phase aimed to prepare a routine for the Quer mobile app installation. One of the questionnaires collected data from 42 patients and identified that before hospital admission 29% of them did not have medical appointments, 36% had one or two appointments, 11% had three appointments, and 24% had four or more appointments. This research portrayed adaptation feasibility and the implementation of a cell phone app for following up on stroke patients.

Objective: The study aimed to represent the content of nursing diagnosis and interventions in the openEHR standard., Methods: This is a developmental study with the models developed according to ISO 18104: 2014. The Ocean Archetype Editor tool from the openEHR Foundation was used., Results: Two archetypes were created; one to represent the nursing diagnosis concept and the other the nursing intervention concept. Existing archetypes available in the Clinical Knowledge Manager were reused in modeling., Conclusion: The representation of nursing diagnosis and interventions based on the openEHR standard contributes to representing nursing care phenomena and needs in health information systems., Competing Interests: None declared., (Thieme. All rights reserved.)

Background: Cross-mapping establishes equivalence between terms from different terminology systems, which is useful for interoperability, updated terminological versions, and reuse of terms. Due to the number of terms to be mapped, this work can be extensive, tedious, and thorough, and it is susceptible to errors; this can be minimized by automated processes, which use computational tools., Objective: The aim of this study was to compare the results of manual and automated term mapping processes., Methods: In this descriptive, quantitative study, we used the results of two mapping processes as an empirical basis: manual, which used 2638 terms of nurses' records from a university hospital in southern Brazil and the International Classification for Nursing Practice (ICNP); and automated, which used the same university hospital terms and the primitive terms of the ICNP through MappICNP, an algorithm based on rules of natural language processing. The two processes were compared via equality and exclusivity assessments of new terms of the automated process and of candidate terms., Results: The automated process mapped 569/2638 (21.56%) of the source bank's terms as identical, and the manual process mapped 650/2638 (24.63%) as identical. Regarding new terms, the automated process mapped 1031/2638 (39.08%) of the source bank's terms as new, while the manual process mapped 1251 (47.42%). In particular, manual mapping identified 101/2638 (3.82%) terms as identical and 429 (16.26%) as new, whereas the automated process identified 20 (0.75%) terms as identical and 209 (7.92%) as new. Of the 209 terms mapped as new by the automated process, it was possible to establish an equivalence with ICNP terms in 48 (23.0%) cases. An analysis of the candidate terms offered by the automated process to the 429 new terms mapped exclusively by the manual process resulted in 100 (23.3%) candidates that had a semantic relationship with the source term., Conclusions: The automated and manual processes map identical and new terms in similar ways and can be considered complementary. Direct identification of identical terms and the offering of candidate terms through the automated process facilitate and enhance the results of the mapping; confirmation of the precision of the automated mapping requires further analysis by researchers., Competing Interests: Conflicts of Interest: None declared., (©Fernanda Broering Gomes Torres, Denilsen Carvalho Gomes, Adriano Akira Ferreira Hino, Claudia Moro, Marcia Regina Cubas. Originally published in JMIR Nursing Informatics (https://nursing.jmir.org), 09.06.2020.)

This study describes MappICNP, an automatic method for mapping between Brazilian Portuguese clinical narratives in free text and International Classification for Nursing Practice (ICNP) concepts. It's composed of six natural language processing rules, related to terms comparison. A set of 2,638 terms extracted from hospitals nursing notes was mapped. MappICNP helps to map 1,607 terms, 113 less than a manual approach. The results demostrate its advantages in minimizing the time spent and reducing the scope of analysis through candidate terms of ICNP.

In this paper, we trained a set of Portuguese clinical word embedding models of different granularities from multi-specialty and multi-institutional clinical narrative datasets. Then, we assessed their impact on a downstream biomedical NLP task of Urinary Tract Infection disease identification. Additionally, we intrinsically evaluated our main model using an adapted version of Bio-SimLex for the Portuguese language. Our empirical results showed that the larger, coarse-grained model achieved a slightly better outcome when compared with the small, fine-grained model in the proposed task. Moreover, we obtained satisfactory results with Bio-SimLex intrinsic evaluation.

Studies describing Computer-Interpretable Clinical Guidelines (CIG) with temporal constrains (TC) generally have not addressed issues related to their integration into Electronic Health Record (EHR) systems. This study aimed to represent TCs contained in clinical guidelines by applying archetypes and Guideline Definition Language (GDL) to incorporate decision support into EHRs. An example of each TC class in the clinical guideline for management of Atrial Fibrillation was represented using archetypes and GDL.

Unlabelled: The lack of a unique, standardized format for representing data and knowledge is one of the existing difficulties to integrating decision support into Electronic Health Records (EHRs)., Objective: Propose an archetype-based model to allow the integration of Clinical Practice Guidelines (CPG) and EHRs; design and implement this proposed model., Results: A generic model was designed for the integration of CPG into EHRs, and an archetype-based EHR for Chronic Kidney Disease Prevention based on rules from CPGs, was made as a proof of concept of this novel integration.

Brazil has a long tradition in the use of health information systems, however--until now--there is no consensus on the minimum data set from which to compose discharge summaries. This article describes the methodology used by a group of experts--members of WG1 of ISOTC 215 Health Informatics Brazilian mirror committee--to define the information model of the discharge summary. This paper describes the current status of the standardization process and the first pilot tests with this infomation content.

Quality is fundamental during compounded preparations. In spite of this, there is not standardization for compounding formulations preparation. Nowadays, there are some information systems to compounding pharmacy area, however they don't offer functions related to the compounding elaboration. Standardization is essential to systems success. The goal of this paper was to propose a standard to pharmaceutical semisolid and liquid external use forms preparation. First was performed a study to check the characteristics on drug preparation at different establishments. Then, a standardization proposal was specified represented by flowcharts, including sequence, features and drug elaboration restrictions. Relevant variables were also identified and defined. Active's name, pH and compatibilities are some examples. Flowcharts cover prescription analysis, actives compatibility verification, solubility, pharmaceutical incipient addition and other aspects that are important considering drug preparation. These flowcharts were validated by compounding pharmacy specialists applying Delphi Method. An Information System to support compounding pharmacist's decision and help actives selection was developed, using the standard proposed, and the defined variables as knowledge base. Six common medical prescriptions, generally elaborated with wrong characteristics were used to evaluate the system. To all of them, the system offered a correct action plan to drug elaboration.

Leprosy is an infectious disease caused by Mycobacterium Leprae, and generally compromises neural fibers, leading to the development of disabilities. These limit daily activities or social life. In leprosy, the study of disability considered functional (physical) and activity limitations; and social participation. These are measured respectively by EHF and SALSA scales; by and PARTICIPATION SCALE: The objective of this work was to propose a support system, SeyeS, to eyes disabilities development and progression identification, applying Bayesians network - BN's. It is expected that the proposed system be applied in monitoring the patient during treatment and after therapeutic cure of leprosy. SeyeS presented specificity 1 and sensitivity 0.6 in the identification of ocular disabilities development. With Seyes was discovered that the presence of trichiasis and lagophthalmos, tend to increase the probability of developing more disabilities. Otherwise, characteristics as cataracts tend to decrease development of other disabilities, considering that medical interventions could reduce it. The more import of this system is to indicate what should be monitored, and which elements needs interventions to not increasing patient's ocular disabilities.