On Merging Feature Engineering and Deep Learning for Diagnosis, Risk-Prediction and Age Estimation Based on the 12-Lead ECG

Objective: Machine learning techniques have been used extensively for 12-lead electrocardiogram (ECG) analysis. For physiological time series, deep learning (DL) superiority to feature engineering (FE) approaches based on domain knowledge is still an open question. Moreover, it remains unclear whether combining DL with FE may improve performance. Methods: We considered three tasks intending to address these research gaps: cardiac arrhythmia diagnosis (multiclass-multilabel classification), atrial fibrillation risk prediction (binary classification), and age estimation (regression). We used an overall dataset of 2.3M 12-lead ECG recordings to train the following models for each task: i) a random forest taking the FE as input was trained as a classical machine learning approach; ii) an end-to-end DL model; and iii) a merged model of FE+DL. Results: FE yielded comparable results to DL while necessitating significantly less data for the two classification tasks and it was outperformed by DL for the regression task. For all tasks, merging FE with DL did not improve performance over DL alone. Conclusion: We found that for traditional 12-lead ECG based diagnosis tasks DL did not yield a meaningful improvement over FE, while it improved significantly the nontraditional regression task. We also found that combining FE with DL did not improve over DL alone which suggests that the FE were redundant with the features learned by DL. Significance: Our findings provides important recommendations on what machine learning strategy and data regime to chose with respect to the task at hand for the development of new machine learning models based on the 12-lead ECG

Automatic diagnosis of the 12-lead ECG using a deep neural network

The role of automatic electrocardiogram (ECG) analysis in clinical practice is limited by the accuracy of existing models. Deep Neural Networks (DNNs) are models composed of stacked transformations that learn tasks by examples. This technology has recently achieved striking success in a variety of task and there are great expectations on how it might improve clinical practice. Here we present a DNN model trained in a dataset with more than 2 million labeled exams analyzed by the Telehealth Network of Minas Gerais and collected under the scope of the CODE (Clinical Outcomes in Digital Electrocardiology) study. The DNN outperform cardiology resident medical doctors in recognizing 6 types of abnormalities in 12-lead ECG recordings, with F1 scores above 80% and specificity over 99%. These results indicate ECG analysis based on DNNs, previously studied in a single-lead setup, generalizes well to 12-lead exams, taking the technology closer to the standard clinical practice

QUALIDADE DE VIDA E FATORES BIOPSICOSSOCIAIS NO CÂNCER DE MAMA – UMA REVISÃO BIBLIOGRÁFICA

O número crescente de mulheres afetadas com a neoplasia maligna de mama instigou a criação de novas técnicas terapêuticas para a doença e consequentemente um melhor prognóstico para as doentes. Com isto, torna-se importante a busca pela qualidade de vida destas mulheres, tanto durante o tratamento quanto na sua reabilitação. As consequências psicológicas e sociais da doença passaram a ser objeto de diversos estudos. Essa revisão tem o objetivo de compreender como os impactos biológicos afetam o psicológico durante o tratamento e vice versa. O estudo revisa a literatura de 21 artigos sobre como a doente e a sociedade que a cerca lida com o processo saúde doença no câncer de mama, utilizando questionários para analisar qualidade de vida como o FACT-B e EORTC. Os resultados dos artigos convergem quanto à importância do apoio familiar e da disponibilidade de vários tipos de tratamento para uma maior qualidade de vida, além da orientação sobre as vantagens do diagnóstico precoce feito pelo autoexame. Houve consenso também quanto ao tratamento cirúrgico menos invasivo, o qual obteve resultado mais satisfatório em relação à imagem corporal das pacientes. Portanto, pode-se concluir que a qualidade de vida das pacientes vai além do tratamento da fisiopatologia dos tumores. Não há como negligenciar os aspectos sociais, sexuais, psicológicos e familiares, pois estes fatores fazem parte do processo saúde doença

Tele-electrocardiography and bigdata: the CODE (Clinical Outcomes in Digital Electrocardiography) study

Digital electrocardiographs are now widely available and a large number of digital electrocardiograms (ECGs) have been recorded and stored. The present study describes the development and clinical applications of a large database of such digital ECGs, namely the CODE (Clinical Outcomes in Digital Electrocardiology) study. ECGs obtained by the Telehealth Network of Minas Gerais, Brazil, from 2010 to 17, were organized in a structured database. A hierarchical free-text machine learning algorithm recognized specific ECG diagnoses from cardiologist reports. The Glasgow ECG Analysis Program provided Minnesota Codes and automatic diagnostic statements. The presence of a specific ECG abnormality was considered when both automatic and medical diagnosis were concordant; cases of discordance were decided using heuristisc rules and manual review. The ECG database was linked to the national mortality information system using probabilistic linkage methods. From 2,470,424 ECGs, 1,773,689 patients were identified. After excluding the ECGs with technical problems and patients <16 years-old, 1,558,415 patients were studied. High performance measures were obtained using an end-to-end deep neural network trained to detect 6 types of ECG abnormalities, with F1 scores >80% and specificity >99% in an independent test dataset. We also evaluated the risk of mortality associated with the presence of atrial fibrillation (AF), which showed that AF was a strong predictor of cardiovascular mortality and mortality for all causes, with increased risk in women. In conclusion, a large database that comprises all ECGs performed by a large telehealth network can be useful for further developments in the field of digital electrocardiography, clinical cardiology and cardiovascular epidemiology

Energy levels and lysine, calcium and phosphorus adjustments on broiler nutrient digestibility and performance

Abstract Chicken broilers digestibility and performance fed with different ME levels, with and without adjustments of digestible lysine, calcium, and available phosphorus, were evaluated. For digestibility, 210 male Cobb 500 chicken broilers were used and distributed into a 3x2+1 factorial arrangement, with three ME levels (3050; 3125 and 3200 kcal/kg) with and without nutrient adjustment, plus one control treatment (2975 kcal ME/kg), totaling seven treatments including six repetitions with five birds into each repetition. For initial performance, 1120 birds were distributed randomly with eight replications within treatments and 20 birds for each replication. For final performance, 1008 chickens were distributed with eight replications and 18 birds for each replication. The DCDM and DCCP were improved (P0.05) between energy and nutrient adjustment, but the increase in energy levels improved the feed conversion ratio (FCR=1.370). Increasing energy density with nutrient adjustment improves both nutrient utilization and bird performance

Unexpected species diversity in electric eels with a description of the strongest living bioelectricity generator

Is there only one electric eel species? For two and a half centuries since its description by Linnaeus, Electrophorus electricus has captivated humankind by its capacity to generate strong electric discharges. Despite the importance of Electrophorus in multiple fields of science, the possibility of additional species-level diversity in the genus, which could also reveal a hidden variety of substances and bioelectrogenic functions, has hitherto not been explored. Here, based on overwhelming patterns of genetic, morphological, and ecological data, we reject the hypothesis of a single species broadly distributed throughout Greater Amazonia. Our analyses readily identify three major lineages that diverged during the Miocene and Pliocene—two of which warrant recognition as new species. For one of the new species, we recorded a discharge of 860 V, well above 650 V previously cited for Electrophorus, making it the strongest living bioelectricity generator. © 2019, The Author(s)