Hemogram data as a tool for decision-making in COVID-19 management : applications to resource scarcity scenarios

Background COVID-19 pandemics has challenged emergency response systems worldwide, with widespread reports of essential services breakdown and collapse of health care structure. A critical element involves essential workforce management since current protocols recommend release from duty for symptomatic individuals, including essential personnel. Testing capacity is also problematic in several countries, where diagnosis demand outnumbers available local testing capacity. Purpose This work describes a machine learning model derived from hemogram exam data performed in symptomatic patients and how they can be used to predict qRT-PCR test results. Methods Hemogram exams data from 510 symptomatic patients (73 positives and 437 negatives) were used to model and predict qRT-PCR results through Naïve-Bayes algorithms. Different scarcity scenarios were simulated, including symptomatic essential workforce management and absence of diagnostic tests. Adjusts in assumed prior probabilities allow fine-tuning of the model, according to actual prediction context. Results Proposed models can predict COVID-19 qRT-PCR results in symptomatic individuals with high accuracy, sensitivity and specificity, yielding a 100% sensitivity and 22.6% specificity with a prior of 0.9999; 76.7% for both sensitivity and specificity with a prior of 0.2933; and 0% sensitivity and 100% specificity with a prior of 0.001. Regarding background scarcity context, resources allocation can be significantly improved when model-based patient selection is observed, compared to random choice. Conclusions Machine learning models can be derived from widely available, quick, and inexpensive exam data in order to predict qRT-PCR results used in COVID-19 diagnosis. These models can be used to assist strategic decision-making in resource scarcity scenarios, including personnel shortage, lack of medical resources, and testing insufficiency

Comparison of machine learning techniques to handle imbalanced COVID-19 CBC datasets

The Coronavirus pandemic caused by the novel SARS-CoV-2 has significantly impacted human health and the economy, especially in countries struggling with financial resources for medical testing and treatment, such as Brazil’s case, the third most affected country by the pandemic. In this scenario, machine learning techniques have been heavily employed to analyze different types of medical data, and aid decision making, offering a low-cost alternative. Due to the urgency to fight the pandemic, a massive amount of works are applying machine learning approaches to clinical data, including complete blood count (CBC) tests, which are among the most widely available medical tests. In this work, we review the most employed machine learning classifiers for CBC data, together with popular sampling methods to deal with the class imbalance. Additionally, we describe and critically analyze three publicly available Brazilian COVID-19 CBC datasets and evaluate the performance of eight classifiers and five sampling techniques on the selected datasets. Our work provides a panorama of which classifier and sampling methods provide the best results for different relevant metrics and discuss their impact on future analyses. The metrics and algorithms are introduced in a way to aid newcomers to the field. Finally, the panorama discussed here can significantly benefit the comparison of the results of new ML algorithms

Perbandingan Pendekatan Tradisional dan Semantic Web untuk Akses Informasi Sebagai Penunjang Pengambilan Keputusan

Pengambilan keputusan pada dunia industry akan membutuhkan data teks, grafik dan juga bentuk data traditional lainnya. Dengan perkembangan teknologi informasi saat ini makasifat dari sumber informasi berkembang sehingga berjumlah sangat besar, keragaman jenis sumber informasi (sintaktik, struktur, semantic) dan data volume data semakin besar serta komplek

Lateral hypothalamic activity indicates hunger and satiety states in humans

Lateral hypothalamic area (LHA) local field potentials (LFPs) were recorded in a Prader–Willi patient undergoing deep brain stimulation (DBS) for obesity. During hunger, exposure to food-related cues induced an increase in beta/ low-gamma activity. In contrast, recordings during satiety were marked by prominent alpha rhythms. Based on these findings, we have delivered alphafrequency DBS prior to and during food intake. Despite reporting an early sensation of fullness, the patient continued to crave food. This suggests that the pattern of activity in LHA may indicate hunger/satiety states in humans but attest to the complexity of conducting neuromodulation studies in obesity

Concordância entre dois instrumentos para avaliação do letramento em saúde

Objective: To determine the agreement between short test of functional health literacy in adults (S-TOFHLA) and short assessment of health literacy for Portuguese-speaking adults (SAHLPA-18) measurement instruments as a strategy to estimate concurrent validity. Methods: Cross-sectional study conducted with users of the Unified Health System. To test concurrent validity, an agreement approach with a weighted Kappa test for qualitative data was applied. Results: 372 individuals participated. It was found that 66% and 62% of these did not have an adequate level of literacy through SAHLPA-18 and S-TOFHLA, respectively. There was a strong correlation between the instruments (p<0.001; r=0.60), however 65.3% agreement, considered weak (Kappa=0.35; p<0.001). Conclusion: SAHLPA-18 and S-TOFHLA instruments have different constructs and poor agreement. The use of different instruments is indicated in research to measure the level of literacy and the development of instruments specific to health conditions that allow obtaining results close to the real context of individuals.Objetivo: Determinar a concordância entre os instrumentos de mensuração short test of functional health literacy in adults (S-TOFHLA) e short assessment of health literacy for Portuguese-speaking adults (SAHLPA-18) como estratégia para estimar a validade concorrente. Métodos: Estudo transversal, com usuários do Sistema Único de Saúde. Para testar a validade concorrente, aplicou-se abordagem de concordância com teste de Kappa ponderado para dados qualitativos. Resultados: Participaram 372 indivíduos, dos quais 66% e 62% não apresentaram nível de letramento adequado, segundo o SAHLPA-18 e o S-TOFHLA respectivamente. Observou-se correlação forte entre os instrumentos (p<0,001; r=0,60); entretanto, a concordância encontrada, 65,3% (Kappa=0,35; p<0,001), foi considerada fraca. Conclusão: Os instrumentos SAHLPA-18 e S-TOFHLA apresentam constructos diferentes e fraca concordância. É indicado o uso de diferentes instrumentos em pesquisas de mensuração do nível de letramento; e o desenvolvimento de instrumentos específicos às condições de saúde presentes, que permitam obter resultados próximos ao real contexto dos indivíduos

Effects of conversion of native cerrado vegetation to pasture on soil hydro-physical properties, evapotranspiration and streamflow on the Amazonian agricultural frontier

Understanding the impacts of land-use change on landscape-hydrological dynamics is one of the main challenges in the Northern Brazilian Cerrado biome, where the Amazon agricultural frontier is located. Motivated by the gap in literature assessing these impacts, we characterized the soil hydro-physical properties and quantified surface water fluxes from catchments under contrasting land-use in this region. We used data from field measurements in two headwater micro-catchments with similar physical characteristics and different land use, i.e. cerrado sensu stricto vegetation and pasture for extensive cattle ranching. We determined hydraulic and physical properties of the soils, applied ground-based remote sensing techniques to estimate evapotranspiration, and monitored streamflow from October 2012 to September 2014. Our results show significant differences in soil hydro-physical properties between the catchments, with greater bulk density and smaller total porosity in the pasture catchment. We found that evapotranspiration is smaller in the pasture (639 ± 31% mm yr-1) than in the cerrado catchment (1,004 ± 24% mm yr-1), and that streamflow from the pasture catchment is greater with runoff coefficients of 0.40 for the pasture and 0.27 for the cerrado catchment. Overall, our results confirm that conversion of cerrado vegetation to pasture causes soil hydro-physical properties deterioration, reduction in evapotranspiration reduction, and increased streamflow