AVALIAÇÃO AMBIENTAL DA ÁGUA SUPERFICIAL DO ARROIO SCHMIDT (CAMPO BOM, RS), POR MEIO DE ANÁLISES FÍSICO-QUÍMICA, BIOLÓGICA E TOXICOLÓGICA EM DOIS PONTOS

A poluição dos corpos hídricos constitui um dos grandes problemas ambientais causados pelo crescimento populacional e lançamento de resíduos industriais e domésticos, podendo causar danos irreversíveis à biota e a população humana. O Arroio Schmidt está localizado no munícipio de Campo Bom/RS, na região do Vale dos Sinos, sendo um afluente do Rio dos Sinos. O monitoramento realizado avaliou a qualidade da água do arroio por meio de análises químicas, biológicas e toxicológicas em dois pontos com 1,6 km de distância entre si. Realizaram-se análises relacionadas ao potencial de hidrogênio (pH), oxigênio dissolvido (OD), demanda bioquímica de oxigênio (DBO), cor aparente, condutividade, coliformes totais e cafeína. Esses parâmetros foram comparados com os limites estipulados pela Resolução 357/2005 do CONAMA, que dispõe sobre o enquadramento dos recursos hídricos do país. De acordo com a avaliação dos dois pontos de coleta, o Arroio Schmidt enquadra-se na classe 4, ou seja, passível de uso apenas para navegação e harmonia paisagística

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

Ontology based classification of electronic health records to support value-based health care

Value-based health care management models require a precise accounting of health indexes such as risk events monitoring, clinical conditions, patient handling and outcomes. Currently this accounting is performed by manually reading and searching through electronic health records for these indexes. Our research proposes a way to make this an autonomous task that is performed by a computer using a Portuguese free-text concept classifier model based on ontologies. To validate our model we tested it on digital clinical records from 191 patients under ischemic stroke care. We have selected 30 management indexes to be identified in these texts. Our model reached, on average 56,8% of f1-score, varying from 5,83% to 94,78% f1-score across different management indexes.Financially supported by the Brazilian Coordination of Superior Level Staff Improvement (CAPES), the by Portuguese Foundation for Science and Technology (FCT) under the projects CEECIND/01997/2017, UIDB/00057/2020 and, the Brazilian National Council for Scientific and Technological Development (CNPq) (project:465518/2014-1)

AVALIAÇÃO AMBIENTAL DA ÁGUA SUPERFICIAL DO ARROIO SCHMIDT (CAMPO BOM, RS), POR MEIO DE ANÁLISES FÍSICO-QUÍMICA, BIOLÓGICA E TOXICOLÓGICA EM DOIS PONTOS

A poluição dos corpos hídricos constitui um dos grandes problemas ambientais causados pelo crescimento populacional e lançamento de resíduos industriais e domésticos, podendo causar danos irreversíveis à biota e a população humana. O Arroio Schmidt está localizado no munícipio de Campo Bom/RS, na região do Vale dos Sinos, sendo um afluente do Rio dos Sinos. O monitoramento realizado avaliou a qualidade da água do arroio por meio de análises químicas, biológicas e toxicológicas em dois pontos com 1,6 km de distância entre si. Realizaram-se análises relacionadas ao potencial de hidrogênio (pH), oxigênio dissolvido (OD), demanda bioquímica de oxigênio (DBO), cor aparente, condutividade, coliformes totais e cafeína. Esses parâmetros foram comparados com os limites estipulados pela Resolução 357/2005 do CONAMA, que dispõe sobre o enquadramento dos recursos hídricos do país. De acordo com a avaliação dos dois pontos de coleta, o Arroio Schmidt enquadra-se na classe 4, ou seja, passível de uso apenas para navegação e harmonia paisagística

PCV50 Automatic Classification of Electronic Health Records for a Value-Based Program through Machine Learning

This study presents a comparative assessment of supervised machine learning (ML) methods to capture outcomes and patients' characteristics from electronic health records (EHR). We explored automatic classification of free-text data from EHRs to support a value-based program

Stroke Outcome Measurements from Electronic Medical Records: 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: The research reported in this article aims at comparing 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 problem 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 healthcare status), Tier 2 (recovery process), care-related (clinical management and risk scores), and baseline characteristics. The analyzed dataset was retrospectively extracted from the EMRs of stroke patients 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 (ML) methods, including state-of-the-art neural and non-neural 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. Feature importance analysis was conducted to provide insights regarding the results. Results: The top-performing models were support vector machines (SVM) trained with lexical and semantic textual features, showing the importance of dealing with noise in EMR’s textual representations. The SVM models produced statistically superior results in a total of 17 tasks out of 24 (70%), 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/ambulate and communicate), healthcare status achieved (mortality), and baseline characteristics (diabetes, obesity, dyslipidemia, and smoking status). Neural methods were largely outperformed by more traditional non-neural methods given the characteristics of the dataset. 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 stroke victims’ clinical conditions, and thus, ultimately assess the possibility of proactively using these machine-learning techniques in real-world situations

"Delirium Day": A nationwide point prevalence study of delirium in older hospitalized patients using an easy standardized diagnostic tool

Background: To date, delirium prevalence in adult acute hospital populations has been estimated generally from pooled findings of single-center studies and/or among specific patient populations. Furthermore, the number of participants in these studies has not exceeded a few hundred. To overcome these limitations, we have determined, in a multicenter study, the prevalence of delirium over a single day among a large population of patients admitted to acute and rehabilitation hospital wards in Italy. Methods: This is a point prevalence study (called "Delirium Day") including 1867 older patients (aged 65 years or more) across 108 acute and 12 rehabilitation wards in Italian hospitals. Delirium was assessed on the same day in all patients using the 4AT, a validated and briefly administered tool which does not require training. We also collected data regarding motoric subtypes of delirium, functional and nutritional status, dementia, comorbidity, medications, feeding tubes, peripheral venous and urinary catheters, and physical restraints. Results: The mean sample age was 82.0 ± 7.5 years (58 % female). Overall, 429 patients (22.9 %) had delirium. Hypoactive was the commonest subtype (132/344 patients, 38.5 %), followed by mixed, hyperactive, and nonmotoric delirium. The prevalence was highest in Neurology (28.5 %) and Geriatrics (24.7 %), lowest in Rehabilitation (14.0 %), and intermediate in Orthopedic (20.6 %) and Internal Medicine wards (21.4 %). In a multivariable logistic regression, age (odds ratio [OR] 1.03, 95 % confidence interval [CI] 1.01-1.05), Activities of Daily Living dependence (OR 1.19, 95 % CI 1.12-1.27), dementia (OR 3.25, 95 % CI 2.41-4.38), malnutrition (OR 2.01, 95 % CI 1.29-3.14), and use of antipsychotics (OR 2.03, 95 % CI 1.45-2.82), feeding tubes (OR 2.51, 95 % CI 1.11-5.66), peripheral venous catheters (OR 1.41, 95 % CI 1.06-1.87), urinary catheters (OR 1.73, 95 % CI 1.30-2.29), and physical restraints (OR 1.84, 95 % CI 1.40-2.40) were associated with delirium. Admission to Neurology wards was also associated with delirium (OR 2.00, 95 % CI 1.29-3.14), while admission to other settings was not. Conclusions: Delirium occurred in more than one out of five patients in acute and rehabilitation hospital wards. Prevalence was highest in Neurology and lowest in Rehabilitation divisions. The "Delirium Day" project might become a useful method to assess delirium across hospital settings and a benchmarking platform for future surveys