Stroke outcome measurements from electronic medical records : cross-sectional study on the effectiveness of neural and nonneural classifiers

Background: With the rapid adoption of electronic medical records (EMRs), there is an ever-increasing opportunity to collect data and extract knowledge from EMRs to support patient-centered stroke management. Objective: This study aims to compare the effectiveness of state-of-the-art automatic text classification methods in classifying data to support the prediction of clinical patient outcomes and the extraction of patient characteristics from EMRs. Methods: Our study addressed the computational problems of information extraction and automatic text classification. We identified essential tasks to be considered in an ischemic stroke value-based program. The 30 selected tasks were classified (manually labeled by specialists) according to the following value agenda: tier 1 (achieved health care status), tier 2 (recovery process), care related (clinical management and risk scores), and baseline characteristics. The analyzed data set was retrospectively extracted from the EMRs of patients with stroke from a private Brazilian hospital between 2018 and 2019. A total of 44,206 sentences from free-text medical records in Portuguese were used to train and develop 10 supervised computational machine learning methods, including state-of-the-art neural and nonneural methods, along with ontological rules. As an experimental protocol, we used a 5-fold cross-validation procedure repeated 6 times, along with subject-wise sampling. A heatmap was used to display comparative result analyses according to the best algorithmic effectiveness (F1 score), supported by statistical significance tests. A feature importance analysis was conducted to provide insights into the results. Results: The top-performing models were support vector machines trained with lexical and semantic textual features, showing the importance of dealing with noise in EMR textual representations. The support vector machine models produced statistically superior results in 71% (17/24) of tasks, with an F1 score >80% regarding care-related tasks (patient treatment location, fall risk, thrombolytic therapy, and pressure ulcer risk), the process of recovery (ability to feed orally or ambulate and communicate), health care status achieved (mortality), and baseline characteristics (diabetes, obesity, dyslipidemia, and smoking status). Neural methods were largely outperformed by more traditional nonneural methods, given the characteristics of the data set. Ontological rules were also effective in tasks such as baseline characteristics (alcoholism, atrial fibrillation, and coronary artery disease) and the Rankin scale. The complementarity in effectiveness among models suggests that a combination of models could enhance the results and cover more tasks in the future. Conclusions: Advances in information technology capacity are essential for scalability and agility in measuring health status outcomes. This study allowed us to measure effectiveness and identify opportunities for automating the classification of outcomes of specific tasks related to clinical conditions of stroke victims, and thus ultimately assess the possibility of proactively using these machine learning techniques in real-world situations

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear un derstanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5–7 vast areas of the tropics remain understudied.8–11 In the American tropics, Amazonia stands out as the world’s most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepre sented in biodiversity databases.13–15 To worsen this situation, human-induced modifications16,17 may elim inate pieces of the Amazon’s biodiversity puzzle before we can use them to understand how ecological com munities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple or ganism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region’s vulnerability to environmental change. 15%–18% of the most ne glected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lostinfo:eu-repo/semantics/publishedVersio

The Seventeenth Data Release of the Sloan Digital Sky Surveys: Complete Release of MaNGA, MaStar and APOGEE-2 Data

This paper documents the seventeenth data release (DR17) from the Sloan Digital Sky Surveys; the fifth and final release from the fourth phase (SDSS-IV). DR17 contains the complete release of the Mapping Nearby Galaxies at Apache Point Observatory (MaNGA) survey, which reached its goal of surveying over 10,000 nearby galaxies. The complete release of the MaNGA Stellar Library (MaStar) accompanies this data, providing observations of almost 30,000 stars through the MaNGA instrument during bright time. DR17 also contains the complete release of the Apache Point Observatory Galactic Evolution Experiment 2 (APOGEE-2) survey which publicly releases infra-red spectra of over 650,000 stars. The main sample from the Extended Baryon Oscillation Spectroscopic Survey (eBOSS), as well as the sub-survey Time Domain Spectroscopic Survey (TDSS) data were fully released in DR16. New single-fiber optical spectroscopy released in DR17 is from the SPectroscipic IDentification of ERosita Survey (SPIDERS) sub-survey and the eBOSS-RM program. Along with the primary data sets, DR17 includes 25 new or updated Value Added Catalogs (VACs). This paper concludes the release of SDSS-IV survey data. SDSS continues into its fifth phase with observations already underway for the Milky Way Mapper (MWM), Local Volume Mapper (LVM) and Black Hole Mapper (BHM) surveys

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost

CluWords: Explorando Clusters Semânticos entre Palavras para Aprimorar Modelagem de Tópicos

Neste trabalho avançamos o estado-da-arte na modelagem de tópicos por meio de uma nova representação de documentos baseada em word embeddings pré-treinados para fatoração de matriz não-probabilística. Nossa estratégia, chamada CluWords, explora as palavras mais próximas em um determinado espaço word embedding pré-treinado para gerar meta-palavras que são capazes de melhorar a representação de documentos, tanto em termos de informações sintáticas quanto semânticas. Em nossa avaliação, considerando 12 bases de dados e 8 linhas de base, obtivemos melhoras na maioria dos casos, com ganhos de mais de 50%. Nosso método também é capaz de melhorar representação dos documentos para a tarefa de classificação automática

Chemical profiling of Curatella americana Linn leaves by UPLC-HRMS and its wound healing activity in mice.

Based on ethnopharmacological studies, a lot of plants, as well as its compounds, have been investigated for the potential use as wound healing agents. In Brazil, Curatella americana is traditionally used by local people to treat wounds, ulcers and inflammations. However, to the best of our knowledge, its traditional use in the treatment of wounds has not been validated by a scientific study. Here, some compounds, many of them flavonoids, were identified in the hydroethanolic extract from the leaves of C. americana (HECA) by LC-HRMS and LC-MS/MS. Besides that, solutions containing different concentrations of HECA and a gel produced with this extract were evaluated for its antimicrobial, coagulant and wound healing activities on an excision mouse wound model as well as its acute dermal safety. A total of thirteen compounds were identified in HECA, mainly quercetin, kaempferol and glucoside derivatives of both, besides catechin and epicatechin known as wound healing agents. The group treated with 1% of HECA exhibited highest wound healing activity and best rate of wound contraction confirmed by histopathology results. The present study provides scientific evidence of, this extract (HECA) possess remarkable wound healing activity, thereby, supporting the traditional use

Incidência de tuberculose em pacientes com artrite reumatoide em uso de bloqueadores do TNF no Brasil: dados do Registro Brasileiro de Monitoração de Terapias Biológicas BiobadaBrasil

Objectives: To assess the incidence of tuberculosis and to screen for latent tuberculosis infection among Brazilians with rheumatoid arthritis using biologics in clinical practice. Patients and methods: This cohort study used data from the Brazilian Registry of Biological Therapies in Rheumatic Diseases (Registro Brasileiro de Monitoração de Terapias Biológicas - BiobadaBrasil), from 01/2009 to 05/2013, encompassing 1552 treatments, including 415 with only synthetic disease-modifying anti-rheumatic drugs, 942 synthetic DMARDs combined with anti-tumor necrosis factor (etanercept, infliximab, adalimumab) and 195 synthetic DMARDs combined with other biologics (abatacept, rituximab and tocilizumab). The occurrence of tuberculosis and the drug exposure time were assessed, and screening for tuberculosis was performed. Statistical analysis: Unpaired t-test and Fisher's two-tailed test; p < 0.05. Results: The exposure times were 981 patient-years in the controls, 1744 patient-years in the anti-TNF group (adalimumab = 676, infliximab = 547 and etanercept = 521 patient-years) and 336 patient-years in the other biologics group. The incidence rates of tuberculosis were 1.01/1000 patient-years in the controls and 2.87 patient-years among anti-TNF users (adalimumab = 4.43/1000 patient-years; etanercept = 1.92/1000 patient-years and infliximab = 1.82/1000 patient-years). No cases of tuberculosis occurred in the other biologics group. The mean drug exposure time until the occurrence of tuberculosis was 27(11) months for the anti-TNF group. Conclusions: The incidence of tuberculosis was higher among users of synthetic DMARDs and anti-TNF than among users of synthetic DMARDs and synthetic DMARDs and non-anti-TNF biologics and also occurred later, suggesting infection during treatment and no screening failure.Objetivos: Avaliar incidência de tuberculose e triagem para tuberculose latente em brasileiros com artrite reumatoide em uso de agentes biológicos na prática clinica. Pacientes e métodos: Estudo de coorte com dados do Registro Brasileiro de Monitoração de Terapias Biológicas (BiobadaBrasil), de 01/2009 a 05/2013, abrangeu 1.552 tratamentos, 415 somente com drogas modificadoras do curso da doença (MMCDs) sintéticas, 942 MMCDs sintéticas em associação com anti-TNF (etanercepte, infliximabe, adalimumabe) e 195 MMCDs sintéticas em associação com outros biológicos (abatacepte, rituximabe e tocilizumabe). Avaliaram-se ocorrência de tuberculose, tempo de exposição às drogas e triagem para TB. Análise estatística: teste t não pareado e teste de Fisher bicaudal; p < 0,05. Resultados: O tempo de exposição dos controles foi de 981 pacientes-ano, do grupo de anti-TNF foi de 1.744 pacientes-ano (adalimumabe = 676, infliximabe = 547 e etanercepte = 521 pacientes-ano) e o de outros biológicos de 336 pacientes-ano. A incidência de TB foi de 1,01/1.000 pacientes-ano nos controles e de 2,87 pacientes-ano nos usuários de anti-TNF (adalimumabe = 4,43/1.000 pacientes-ano; etanercepte = 1,92/1.000 pacientes-ano e infliximabe = 1,82/1.000 pacientes-ano). Não houve casos de tuberculose no grupo de outros biológicos. O tempo médio de exposição até a ocorrência de tuberculose foi de 27(11) meses para o grupo anti-TNF. Conclusões: A incidência de tuberculose foi maior nos usuários de MMCDs sintéticas e anti-TNF do que nos usuários de MMCDs sintéticas e de MMCDs sintéticas e biológicos não anti-TNF, e também mais tardia, sugerindo infecção durante o tratamento, e não falha na triagem