scholarly journals Facilitating fine-grained intra-urban dengue forecasting by integrating urban environments measured from street-view images

2021 ◽  
Vol 10 (1) ◽  
Author(s):  
Kang Liu ◽  
Ling Yin ◽  
Meng Zhang ◽  
Min Kang ◽  
Ai-Ping Deng ◽  
...  
Keyword(s):  
Machine Learning ◽  
High Risk ◽  
Feature Vector ◽  
Urban Environments ◽  
Environmental Feature ◽  
Fine Grained ◽  
Environmental Features ◽  
Street View ◽  
The City ◽  
Dengue Epidemic

Abstract Background Dengue fever (DF) is a mosquito-borne infectious disease that has threatened tropical and subtropical regions in recent decades. An early and targeted warning of a dengue epidemic is important for vector control. Current studies have primarily determined weather conditions to be the main factor for dengue forecasting, thereby neglecting that environmental suitability for mosquito breeding is also an important factor, especially in fine-grained intra-urban settings. Considering that street-view images are promising for depicting physical environments, this study proposes a framework for facilitating fine-grained intra-urban dengue forecasting by integrating the urban environments measured from street-view images. Methods The dengue epidemic that occurred in 167 townships of Guangzhou City, China, between 2015 and 2019 was taken as a study case. First, feature vectors of street-view images acquired inside each township were extracted by a pre-trained convolutional neural network, and then aggregated as an environmental feature vector of the township. Thus, townships with similar physical settings would exhibit similar environmental features. Second, the environmental feature vector is combined with commonly used features (e.g., temperature, rainfall, and past case count) as inputs to machine-learning models for weekly dengue forecasting. Results The performance of machine-learning forecasting models (i.e., MLP and SVM) integrated with and without environmental features were compared. This indicates that models integrating environmental features can identify high-risk urban units across the city more precisely than those using common features alone. In addition, the top 30% of high-risk townships predicted by our proposed methods can capture approximately 50–60% of dengue cases across the city. Conclusions Incorporating local environments measured from street view images is effective in facilitating fine-grained intra-urban dengue forecasting, which is beneficial for conducting spatially precise dengue prevention and control.

Minimetrò in Perugia as leverage for urban regeneration

TERRITORIO ◽  
2020 ◽  
pp. 148-163
Author(s):  
Luca Fondacci
Keyword(s):  
Urban Regeneration ◽  
Urban Environments ◽  
Low Carbon ◽  
Sustainable Mobility ◽  
Field Surveys ◽  
Peripheral Area ◽  
Alternative Means ◽  
Considerable Impact ◽  
The City ◽  
Historical Centre

In the 1970s, the fragile historical centre of the city of Perugia was a key area where the binomial of sustainable mobility and urban regeneration was developed and applied. At the turn of the xxi century, the low carbon automatic people-mover Minimetrò broadened that application from the city's historical centre to the outskirts, promoting the enhancement of several urban environments. This paper is the outcome of an investigation of original sources, field surveys and direct interviews, which addresses the Minimetrò as the backbone of a wide regeneration process which has had a considerable impact on the economic development of a peripheral area of the city which was previously devoid of any clear urban sense. The conclusion proposes some solutions to improve the nature of the Minimetrò as an experimental alternative means of transport.

Avaliação do Perfil Nutricional dos Funcionários das Equipes Estratégia Saúde da Família e Núcleo de Apoio a Saúde da Família do Município de Pedralva – MG/ Evaluation of the Nutritional Profile of Workers from the Family Health Strategy Team and the Supp

1970 ◽  
Vol 4 (3) ◽  
pp. 12-21
Author(s):  
Ana Isabel da Silva Ferreira ◽  
Edna Slob
Keyword(s):  
High Risk ◽  
Nutritional Status ◽  
Dietary Habits ◽  
Family Health ◽  
Abdominal Circumference ◽  
Nutritional Profile ◽  
Health Strategy ◽  
The Family ◽  
Family Health Strategy ◽  
The City

Objetivo: Avaliar o perfil nutricional das equipes da Estratégia Saúde da Família e do Núcleo de Apoio a Saúde da Família do município de Pedralva. Materiais e métodos: Estudo descritivo, analítico, transversal, realizado com 42 profissionais que compõem as equipes Estratégias Saúde da Família (ESF) e Núcleo de Apoio a Saúde da Família (NASF) no município de Pedralva/MG. Foram coletados dados antropométricos, como peso, altura e circunferências corporais e aplicado um formulário de frequência do consumo alimentar, para avaliar os hábitos alimentares desses profissionais Resultados: Quanto ao estado nutricional, 40,5% dos funcionários se encontravam eutróficos, 40,5% com sobrepeso e 19% com obesidade. Em relação ao risco de doenças cardiovascular, analisando a circunferência abdominal, 9% não apresentou risco, 24% dos funcionários tinham risco elevado e 67%, risco muito elevado; ao se utilizar a relação cintura quadril, encontrou-se 76% dos funcionários sem risco e 24% com risco. De acordo com o inquérito alimentar, observou frequente consumo de arroz, feijão, salada, salgados e carne, pouco consumo de frutas, legumes e peixes. Conclusão: medidas preventivas devem ser tomadas, a fim de evitar que o quadro atual do perfil antropométrico dessa população se torne ainda mais comprometedor, além de garantir melhoria da qualidade de vida dos mesmos.  Palavras-chave: Avaliação nutricional. Estado nutricional. Inquérito alimentar.  ABSTRACT Objective: To evaluate the nutritional profile of the teams of the Family Health Strategy and Support Center for Family Health in the city of Pedralva. Materials and methods: descriptive, analytical, transversal study performed with 42 professionals that make up the teams of the Family Health Strategy (FHS) and the Support Center for Family Health (NASF) in the city of Pedralva/MG. Anthropometric data such as weight, height and body circumferences were collected and a questionare about the frequency of food consumption was applied to assess the dietary habits of these Professional Results: Regarding nutritional status, 40.5% of employees were normal weight, 40.5% overweight and 19% obese. Regarding the risk of cardiovascular diseases, analyzing abdominal circumference, 9% showed no risk, 24% of employees had high risk and 67%, very high risk; when using the waist-hip ratio, we found 76% of employees with no risk and 24% at risk. According to food survey observed frequent consumption of rice, beans, salad, savory meat and little consumption of fruits, vegetables and fish. Conclusion: Preventive measures should be taken to prevent the current situation of anthropometric profile of this population from becoming even more engaging and also ensure improved quality of life for them.  Keywords: Nutritional evaluation. Nutritional status. Dietary survey. 

Personalized stratification of back to work risk amidst COVID-19: A machine learning approach (Preprint)

2020 ◽  
Author(s):  
Carson Lam ◽  
Jacob Calvert ◽  
Gina Barnes ◽  
Emily Pellegrini ◽  
Anna Lynn-Palevsky ◽  
...  
Keyword(s):  
Machine Learning ◽  
High Risk ◽  
Learning Algorithm ◽  
Severe Disease ◽  
High Specificity ◽  
Population Level ◽  
The United States ◽  
Health Condition ◽  
Available P ◽  
Severe Illness

BACKGROUND In the wake of COVID-19, the United States has developed a three stage plan to outline the parameters to determine when states may reopen businesses and ease travel restrictions. The guidelines also identify subpopulations of Americans that should continue to stay at home due to being at high risk for severe disease should they contract COVID-19. These guidelines were based on population level demographics, rather than individual-level risk factors. As such, they may misidentify individuals at high risk for severe illness and who should therefore not return to work until vaccination or widespread serological testing is available. OBJECTIVE This study evaluated a machine learning algorithm for the prediction of serious illness due to COVID-19 using inpatient data collected from electronic health records. METHODS The algorithm was trained to identify patients for whom a diagnosis of COVID-19 was likely to result in hospitalization, and compared against four U.S policy-based criteria: age over 65, having a serious underlying health condition, age over 65 or having a serious underlying health condition, and age over 65 and having a serious underlying health condition. RESULTS This algorithm identified 80% of patients at risk for hospitalization due to COVID-19, versus at most 62% that are identified by government guidelines. The algorithm also achieved a high specificity of 95%, outperforming government guidelines. CONCLUSIONS This algorithm may help to enable a broad reopening of the American economy while ensuring that patients at high risk for serious disease remain home until vaccination and testing become available.

scholarly journals New risk model is able to identify patients with a low risk of progression in systemic sclerosis

RMD Open ◽  
2021 ◽  
Vol 7 (2) ◽  
pp. e001524
Author(s):  
Nina Marijn van Leeuwen ◽  
Marc Maurits ◽  
Sophie Liem ◽  
Jacopo Ciaffi ◽  
Nina Ajmone Marsan ◽  
...  
Keyword(s):  
Machine Learning ◽  
Systemic Sclerosis ◽  
High Risk ◽  
Disease Progression ◽  
Risk Model ◽  
Immunosuppressive Drugs ◽  
Low Risk ◽  
Expert Assessment ◽  
Risk Of Progression

ObjectivesTo develop a prediction model to guide annual assessment of systemic sclerosis (SSc) patients tailored in accordance to disease activity.MethodsA machine learning approach was used to develop a model that can identify patients without disease progression. SSc patients included in the prospective Leiden SSc cohort and fulfilling the ACR/EULAR 2013 criteria were included. Disease progression was defined as progression in ≥1 organ system, and/or start of immunosuppression or death. Using elastic-net-regularisation, and including 90 independent clinical variables (100% complete), we trained the model on 75% and validated it on 25% of the patients, optimising on negative predictive value (NPV) to minimise the likelihood of missing progression. Probability cutoffs were identified for low and high risk for disease progression by expert assessment.ResultsOf the 492 SSc patients (follow-up range: 2–10 years), disease progression during follow-up was observed in 52% (median time 4.9 years). Performance of the model in the test set showed an AUC-ROC of 0.66. Probability score cutoffs were defined: low risk for disease progression (<0.197, NPV:1.0; 29% of patients), intermediate risk (0.197–0.223, NPV:0.82; 27%) and high risk (>0.223, NPV:0.78; 44%). The relevant variables for the model were: previous use of cyclophosphamide or corticosteroids, start with immunosuppressive drugs, previous gastrointestinal progression, previous cardiovascular event, pulmonary arterial hypertension, modified Rodnan Skin Score, creatine kinase and diffusing capacity for carbon monoxide.ConclusionOur machine-learning-assisted model for progression enabled us to classify 29% of SSc patients as ‘low risk’. In this group, annual assessment programmes could be less extensive than indicated by international guidelines.

scholarly journals An Alternative Risk Assessment Model of Urban Waterlogging: A Case Study of Ningbo City

2021 ◽  
Vol 13 (2) ◽  
pp. 826
Author(s):  
Meiling Zhou ◽  
Xiuli Feng ◽  
Kaikai Liu ◽  
Chi Zhang ◽  
Lijian Xie ◽  
...  
Keyword(s):  
Risk Assessment ◽  
High Risk ◽  
Assessment Model ◽  
Risk Assessments ◽  
Risk Assessment Model ◽  
Risk Area ◽  
The North ◽  
Coastal Cities ◽  
Risk Areas ◽  
The City

Influenced by climate change, extreme weather events occur frequently, and bring huge impacts to urban areas, including urban waterlogging. Conducting risk assessments of urban waterlogging is a critical step to diagnose problems, improve infrastructure and achieve sustainable development facing extreme weathers. This study takes Ningbo, a typical coastal city in the Yangtze River Delta, as an example to conduct a risk assessment of urban waterlogging with high-resolution remote sensing images and high-precision digital elevation models to further analyze the spatial distribution characteristics of waterlogging risk. Results indicate that waterlogging risk in the city proper of Ningbo is mainly low risk, accounting for 36.9%. The higher-risk and medium-risk areas have the same proportions, accounting for 18.7%. They are followed by the lower-risk and high-risk areas, accounting for 15.5% and 9.6%, respectively. In terms of space, waterlogging risk in the city proper of Ningbo is high in the south and low in the north. The high-risk area is mainly located to the west of Jiangdong district and the middle of Haishu district. The low-risk area is mainly distributed in the north of Jiangbei district. These results are consistent with the historical situation of waterlogging in Ningbo, which prove the effectiveness of the risk assessment model and provide an important reference for the government to prevent and mitigate waterlogging. The optimized risk assessment model is also of importance for waterlogging risk assessments in coastal cities. Based on this model, the waterlogging risk of coastal cities can be quickly assessed, combining with local characteristics, which will help improve the city’s capability of responding to waterlogging disasters and reduce socio-economic loss.

scholarly journals Identifying and characterizing high-risk clusters in a heterogeneous ICU population with deep embedded clustering

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
José Castela Forte ◽  
Galiya Yeshmagambetova ◽  
Maureen L. van der Grinten ◽  
Bart Hiemstra ◽  
Thomas Kaufmann ◽  
...  
Keyword(s):  
Machine Learning ◽  
High Risk ◽  
Mortality Risk ◽  
Clustering Algorithms ◽  
Kidney Injury ◽  
Risk Groups ◽  
Admission Diagnosis ◽  
Dutch Hospital ◽  
Cluster Membership

AbstractCritically ill patients constitute a highly heterogeneous population, with seemingly distinct patients having similar outcomes, and patients with the same admission diagnosis having opposite clinical trajectories. We aimed to develop a machine learning methodology that identifies and provides better characterization of patient clusters at high risk of mortality and kidney injury. We analysed prospectively collected data including co-morbidities, clinical examination, and laboratory parameters from a minimally-selected population of 743 patients admitted to the ICU of a Dutch hospital between 2015 and 2017. We compared four clustering methodologies and trained a classifier to predict and validate cluster membership. The contribution of different variables to the predicted cluster membership was assessed using SHapley Additive exPlanations values. We found that deep embedded clustering yielded better results compared to the traditional clustering algorithms. The best cluster configuration was achieved for 6 clusters. All clusters were clinically recognizable, and differed in in-ICU, 30-day, and 90-day mortality, as well as incidence of acute kidney injury. We identified two high mortality risk clusters with at least 60%, 40%, and 30% increased. ICU, 30-day and 90-day mortality, and a low risk cluster with 25–56% lower mortality risk. This machine learning methodology combining deep embedded clustering and variable importance analysis, which we made publicly available, is a possible solution to challenges previously encountered by clustering analyses in heterogeneous patient populations and may help improve the characterization of risk groups in critical care.

scholarly journals A Novel Epigenetic Machine Learning Model to Define Risk of Progression for Hepatocellular Carcinoma Patients

2021 ◽  
Vol 22 (3) ◽  
pp. 1075
Author(s):  
Luca Bedon ◽  
Michele Dal Bo ◽  
Monica Mossenta ◽  
Davide Busato ◽  
Giuseppe Toffoli ◽  
...  
Keyword(s):  
Machine Learning ◽  
Hepatocellular Carcinoma ◽  
High Risk ◽  
Progression Free Survival ◽  
Low Risk ◽  
Functional Enrichment ◽  
Free Survival ◽  
High Risk Patients ◽  
Risk Of Progression ◽  
Risk Patients

Although extensive advancements have been made in treatment against hepatocellular carcinoma (HCC), the prognosis of HCC patients remains unsatisfied. It is now clearly established that extensive epigenetic changes act as a driver in human tumors. This study exploits HCC epigenetic deregulation to define a novel prognostic model for monitoring the progression of HCC. We analyzed the genome-wide DNA methylation profile of 374 primary tumor specimens using the Illumina 450 K array data from The Cancer Genome Atlas. We initially used a novel combination of Machine Learning algorithms (Recursive Features Selection, Boruta) to capture early tumor progression features. The subsets of probes obtained were used to train and validate Random Forest models to predict a Progression Free Survival greater or less than 6 months. The model based on 34 epigenetic probes showed the best performance, scoring 0.80 accuracy and 0.51 Matthews Correlation Coefficient on testset. Then, we generated and validated a progression signature based on 4 methylation probes capable of stratifying HCC patients at high and low risk of progression. Survival analysis showed that high risk patients are characterized by a poorer progression free survival compared to low risk patients. Moreover, decision curve analysis confirmed the strength of this predictive tool over conventional clinical parameters. Functional enrichment analysis highlighted that high risk patients differentiated themselves by the upregulation of proliferative pathways. Ultimately, we propose the oncogenic MCM2 gene as a methylation-driven gene of which the representative epigenetic markers could serve both as predictive and prognostic markers. Briefly, our work provides several potential HCC progression epigenetic biomarkers as well as a new signature that may enhance patients surveillance and advances in personalized treatment.

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