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1.
Global Health ; 17(1): 119, 2021 10 09.
Artigo em Inglês | MEDLINE | ID: mdl-34627303

RESUMO

The major threat to human societies posed by undernutrition has been recognised for millennia. Despite substantial economic development and scientific innovation, however, progress in addressing this global challenge has been inadequate. Paradoxically, the last half-century also saw the rapid emergence of obesity, first in high-income countries but now also in low- and middle-income countries. Traditionally, these problems were approached separately, but there is increasing recognition that they have common drivers and need integrated responses. The new nutrition reality comprises a global 'double burden' of malnutrition, where the challenges of food insecurity, nutritional deficiencies and undernutrition coexist and interact with obesity, sedentary behaviour, unhealthy diets and environments that foster unhealthy behaviour. Beyond immediate efforts to prevent and treat malnutrition, what must change in order to reduce the future burden? Here, we present a conceptual framework that focuses on the deeper structural drivers of malnutrition embedded in society, and their interaction with biological mechanisms of appetite regulation and physiological homeostasis. Building on a review of malnutrition in past societies, our framework brings to the fore the power dynamics that characterise contemporary human food systems at many levels. We focus on the concept of agency, the ability of individuals or organisations to pursue their goals. In globalized food systems, the agency of individuals is directly confronted by the agency of several other types of actor, including corporations, governments and supranational institutions. The intakes of energy and nutrients by individuals are powerfully shaped by this 'competition of agency', and we therefore argue that the greatest opportunities to reduce malnutrition lie in rebalancing agency across the competing actors. The effect of the COVID-19 pandemic on food systems and individuals illustrates our conceptual framework. Efforts to improve agency must both drive and respond to complementary efforts to promote and maintain equitable societies and planetary health.


Assuntos
Previsões , Saúde Global/tendências , Desnutrição/epidemiologia , Desnutrição/prevenção & controle , Humanos
2.
Sensors (Basel) ; 21(19)2021 Sep 25.
Artigo em Inglês | MEDLINE | ID: mdl-34640732

RESUMO

Road traffic accidents have been listed in the top 10 global causes of death for many decades. Traditional measures such as education and legislation have contributed to limited improvements in terms of reducing accidents due to people driving in undesirable statuses, such as when suffering from stress or drowsiness. Attention is drawn to predicting drivers' future status so that precautions can be taken in advance as effective preventative measures. Common prediction algorithms include recurrent neural networks (RNNs), gated recurrent units (GRUs), and long short-term memory (LSTM) networks. To benefit from the advantages of each algorithm, nondominated sorting genetic algorithm-III (NSGA-III) can be applied to merge the three algorithms. This is named NSGA-III-optimized RNN-GRU-LSTM. An analysis can be made to compare the proposed prediction algorithm with the individual RNN, GRU, and LSTM algorithms. Our proposed model improves the overall accuracy by 11.2-13.6% and 10.2-12.2% in driver stress prediction and driver drowsiness prediction, respectively. Likewise, it improves the overall accuracy by 6.9-12.7% and 6.9-8.9%, respectively, compared with boosting learning with multiple RNNs, multiple GRUs, and multiple LSTMs algorithms. Compared with existing works, this proposal offers to enhance performance by taking some key factors into account-namely, using a real-world driving dataset, a greater sample size, hybrid algorithms, and cross-validation. Future research directions have been suggested for further exploration and performance enhancement.


Assuntos
Algoritmos , Redes Neurais de Computação , Atenção , Previsões , Humanos , Memória de Longo Prazo
3.
Sensors (Basel) ; 21(19)2021 Sep 30.
Artigo em Inglês | MEDLINE | ID: mdl-34640890

RESUMO

In recent years, machine learning for trading has been widely studied. The direction and size of position should be determined in trading decisions based on market conditions. However, there is no research so far that considers variable position sizes in models developed for trading purposes. In this paper, we propose a deep reinforcement learning model named LSTM-DDPG to make trading decisions with variable positions. Specifically, we consider the trading process as a Partially Observable Markov Decision Process, in which the long short-term memory (LSTM) network is used to extract market state features and the deep deterministic policy gradient (DDPG) framework is used to make trading decisions concerning the direction and variable size of position. We test the LSTM-DDPG model on IF300 (index futures of China stock market) data and the results show that LSTM-DDPG with variable positions performs better in terms of return and risk than models with fixed or few-level positions. In addition, the investment potential of the model can be better tapped by the reward function of the differential Sharpe ratio than that of profit reward function.


Assuntos
Investimentos em Saúde , Memória de Longo Prazo , Previsões , Aprendizado de Máquina , Políticas
4.
Artigo em Inglês | MEDLINE | ID: mdl-34639572

RESUMO

Despite significant developments in Aboriginal and Torres Strait Islander Health information over the last 25 years, many challenges remain. There are still uncertainties about the accuracy of estimates of the summary measure of life expectancy, and methods to estimate changes in life expectancy over time are unreliable because of changing patterns of identification. Far too little use is made of the wealth of information that is available, and formal systems for systematically using that information are often vestigial to non-existent. Available information has focussed largely on traditional biomedical topics and too little on access to, expenditure on, and availability of services required to improve health outcomes, and on the underpinning issues of social and emotional wellbeing. It is of concern that statistical artefacts may have been misrepresented as indicating real progress in key health indices. Challenges and opportunities for the future include improving the accuracy of estimation of life expectancy, provision of community level data, information on the availability and effectiveness of health services, measurement of the underpinning issues of racism, culture and social and emotional wellbeing (SEWB), enhancing the interoperability of data systems, and capacity building and mechanisms for Indigenous data governance. There is little point in having information unless it is used, and formal mechanisms for making full use of information in a proper policy/planning cycle are urgently required.


Assuntos
Serviços de Saúde do Indígena , Racismo , Austrália , Fortalecimento Institucional , Previsões , Humanos , Grupo com Ancestrais Oceânicos
5.
Sci Rep ; 11(1): 20271, 2021 10 12.
Artigo em Inglês | MEDLINE | ID: mdl-34642405

RESUMO

To support public health policymakers in Connecticut, we developed a flexible county-structured compartmental SEIR-type model of SARS-CoV-2 transmission and COVID-19 disease progression. Our goals were to provide projections of infections, hospitalizations, and deaths, and estimates of important features of disease transmission and clinical progression. In this paper, we outline the model design, implementation and calibration, and describe how projections and estimates were used to meet the changing requirements of policymakers and officials in Connecticut from March 2020 to February 2021. The approach takes advantage of our unique access to Connecticut public health surveillance and hospital data and our direct connection to state officials and policymakers. We calibrated this model to data on deaths and hospitalizations and developed a novel measure of close interpersonal contact frequency to capture changes in transmission risk over time and used multiple local data sources to infer dynamics of time-varying model inputs. Estimated epidemiologic features of the COVID-19 epidemic in Connecticut include the effective reproduction number, cumulative incidence of infection, infection hospitalization and fatality ratios, and the case detection ratio. We conclude with a discussion of the limitations inherent in predicting uncertain epidemic trajectories and lessons learned from one year of providing COVID-19 projections in Connecticut.


Assuntos
COVID-19 , Modelos Estatísticos , Pandemias , Vigilância em Saúde Pública/métodos , COVID-19/epidemiologia , COVID-19/transmissão , Connecticut/epidemiologia , Previsões , Humanos , Pandemias/prevenção & controle , Pandemias/estatística & dados numéricos
6.
Nat Commun ; 12(1): 5730, 2021 09 30.
Artigo em Inglês | MEDLINE | ID: mdl-34593807

RESUMO

Viral reproduction of SARS-CoV-2 provides opportunities for the acquisition of advantageous mutations, altering viral transmissibility, disease severity, and/or allowing escape from natural or vaccine-derived immunity. We use three mathematical models: a parsimonious deterministic model with homogeneous mixing; an age-structured model; and a stochastic importation model to investigate the effect of potential variants of concern (VOCs). Calibrating to the situation in England in May 2021, we find epidemiological trajectories for putative VOCs are wide-ranging and dependent on their transmissibility, immune escape capability, and the introduction timing of a postulated VOC-targeted vaccine. We demonstrate that a VOC with a substantial transmission advantage over resident variants, or with immune escape properties, can generate a wave of infections and hospitalisations comparable to the winter 2020-2021 wave. Moreover, a variant that is less transmissible, but shows partial immune-escape could provoke a wave of infection that would not be revealed until control measures are further relaxed.


Assuntos
COVID-19/transmissão , Evasão da Resposta Imune/genética , Modelos Biológicos , Pandemias/estatística & dados numéricos , SARS-CoV-2/patogenicidade , Adolescente , Adulto , COVID-19/epidemiologia , COVID-19/imunologia , COVID-19/prevenção & controle , Vacinas contra COVID-19/administração & dosagem , Simulação por Computador , Previsões/métodos , Humanos , Pessoa de Meia-Idade , Mutação , Pandemias/prevenção & controle , SARS-CoV-2/genética , SARS-CoV-2/imunologia , Processos Estocásticos , Reino Unido/epidemiologia , Vacinação/estatística & dados numéricos , Adulto Jovem
9.
Zhonghua Yi Xue Za Zhi ; 101(5): 311-316, 2021 Feb 02.
Artigo em Chinês | MEDLINE | ID: mdl-34645249

RESUMO

Evidence-based interventional radiology is the result of the evolution and integration of evidence-based medicine and interventional radiology. It adopts the concepts and methods of evidence-based medicine to guide the best clinical practice in interventional radiology. We aim to systematically elaborate on the status quo of Clinical Research, Systematic Review/Meta-Analysis and Clinical Practice Guidelines in interventional radiology, analyze the existing problems, and put forward thoughts and suggestions on promoting the development of evidence-based interventional radiology in the future.


Assuntos
Medicina Baseada em Evidências , Radiologia Intervencionista , Previsões
10.
Comput Intell Neurosci ; 2021: 8128879, 2021.
Artigo em Inglês | MEDLINE | ID: mdl-34621309

RESUMO

Stock price prediction is very important in financial decision-making, and it is also the most difficult part of economic forecasting. The factors affecting stock prices are complex and changeable, and stock price fluctuations have a certain degree of randomness. If we can accurately predict stock prices, regulatory authorities can conduct reasonable supervision of the stock market and provide investors with valuable investment decision-making information. As we know, the LSTM (Long Short-Term Memory) algorithm is mainly used in large-scale data mining competitions, but it has not yet been used to predict the stock market. Therefore, this article uses this algorithm to predict the closing price of stocks. As an emerging research field, LSTM is superior to traditional time-series models and machine learning models and is suitable for stock market analysis and forecasting. However, the general LSTM model has some shortcomings, so this paper designs a LightGBM-optimized LSTM to realize short-term stock price forecasting. In order to verify its effectiveness compared with other deep network models such as RNN (Recurrent Neural Network) and GRU (Gated Recurrent Unit), the LightGBM-LSTM, RNN, and GRU are respectively used to predict the Shanghai and Shenzhen 300 indexes. Experimental results show that the LightGBM-LSTM has the highest prediction accuracy and the best ability to track stock index price trends, and its effect is better than the GRU and RNN algorithms.


Assuntos
Investimentos em Saúde , Redes Neurais de Computação , Algoritmos , China , Previsões
11.
Sci Rep ; 11(1): 18959, 2021 09 23.
Artigo em Inglês | MEDLINE | ID: mdl-34556789

RESUMO

The COVID-19 pandemic has put massive strains on hospitals, and tools to guide hospital planners in resource allocation during the ebbs and flows of the pandemic are urgently needed. We investigate whether machine learning (ML) can be used for predictions of intensive care requirements a fixed number of days into the future. Retrospective design where health Records from 42,526 SARS-CoV-2 positive patients in Denmark was extracted. Random Forest (RF) models were trained to predict risk of ICU admission and use of mechanical ventilation after n days (n = 1, 2, …, 15). An extended analysis was provided for n = 5 and n = 10. Models predicted n-day risk of ICU admission with an area under the receiver operator characteristic curve (ROC-AUC) between 0.981 and 0.995, and n-day risk of use of ventilation with an ROC-AUC between 0.982 and 0.997. The corresponding n-day forecasting models predicted the needed ICU capacity with a coefficient of determination (R2) between 0.334 and 0.989 and use of ventilation with an R2 between 0.446 and 0.973. The forecasting models performed worst, when forecasting many days into the future (for large n). For n = 5, ICU capacity was predicted with ROC-AUC 0.990 and R2 0.928, and use of ventilator was predicted with ROC-AUC 0.994 and R2 0.854. Random Forest-based modelling can be used for accurate n-day forecasting predictions of ICU resource requirements, when n is not too large.


Assuntos
COVID-19/epidemiologia , Previsões/métodos , Unidades de Terapia Intensiva/tendências , Área Sob a Curva , Biologia Computacional/métodos , Cuidados Críticos/estatística & dados numéricos , Cuidados Críticos/tendências , Dinamarca/epidemiologia , Hospitalização/tendências , Hospitais/tendências , Humanos , Aprendizado de Máquina , Pandemias , Curva ROC , Respiração Artificial/estatística & dados numéricos , Respiração Artificial/tendências , Estudos Retrospectivos , Medição de Risco/métodos , Fatores de Risco , SARS-CoV-2/patogenicidade , Ventiladores Mecânicos/tendências
12.
J Biomed Inform ; 122: 103905, 2021 10.
Artigo em Inglês | MEDLINE | ID: mdl-34481056

RESUMO

Compartment-based infectious disease models that consider the transmission rate (or contact rate) as a constant during the course of an epidemic can be limiting regarding effective capture of the dynamics of infectious disease. This study proposed a novel approach based on a dynamic time-varying transmission rate with a control rate governing the speed of disease spread, which may be associated with the information related to infectious disease intervention. Integration of multiple sources of data with disease modelling has the potential to improve modelling performance. Taking the global mobility trend of vehicle driving available via Apple Maps as an example, this study explored different ways of processing the mobility trend data and investigated their relationship with the control rate. The proposed method was evaluated based on COVID-19 data from six European countries. The results suggest that the proposed model with dynamic transmission rate improved the performance of model fitting and forecasting during the early stage of the pandemic. Positive correlation has been found between the average daily change of mobility trend and control rate. The results encourage further development for incorporation of multiple resources into infectious disease modelling in the future.


Assuntos
COVID-19 , Malus , Previsões , Humanos , Pandemias , SARS-CoV-2
13.
Cancer J ; 27(3): 213-221, 2021.
Artigo em Inglês | MEDLINE | ID: mdl-34549910

RESUMO

ABSTRACT: The summation of 20 years of biological studies and the comprehensive analysis of more than 1000 multiple myeloma genomes with data linked to clinical outcome has enabled an increased understanding of the pathogenesis of multiple myeloma in the context of normal plasma cell biology. This novel data have facilitated the identification of prognostic markers and targets suitable for therapeutic manipulation. The challenge moving forward is to translate this genetic and biological information into the clinic to improve patient care. This review discusses the key data required to achieve this and provides a framework within which to explore the use of response-adapted, biologically targeted, molecularly targeted, and risk-stratified therapeutic approaches to improve the management of patients with multiple myeloma.


Assuntos
Mieloma Múltiplo , Previsões , Genômica , Humanos , Mieloma Múltiplo/genética , Mieloma Múltiplo/terapia
14.
Environ Monit Assess ; 193(10): 622, 2021 Sep 03.
Artigo em Inglês | MEDLINE | ID: mdl-34477984

RESUMO

In this study, monthly particulate matter (PM2.5) of Paris for the period between January 2000 and December 2019 is investigated by utilizing a periodogram-based time series methodology. The main contribution of the study is modeling the PM2.5 of Paris by extracting the information purely from the examined time series data, where proposed model implicitly captures the effects of other factors, as all their periodic and seasonal effects reside in the air pollution data. Periodicity can be defined as the patterns embedded in the data other than seasonality, and it is crucial to understand the underlying periodic dynamics of air pollutants to better fight pollution. The method we use successfully captures and accounts for the periodicities, which could otherwise be mixed with seasonality under an alternative methodology. Upon the unit root test based on periodograms, it is revealed that the investigated data has periodicities of 1 year and 20 years, so harmonic regression is utilized as an alternative to Box-Jenkins methodology. As the harmonic regression displayed a better performance both in and out-of-sample forecasts, it can be considered as a powerful alternative to model and forecast time series with a periodic structure.


Assuntos
Poluentes Atmosféricos , Poluição do Ar , Poluentes Atmosféricos/análise , Poluição do Ar/análise , Monitoramento Ambiental , Previsões , Material Particulado/análise
15.
BMC Med Ethics ; 22(1): 121, 2021 09 08.
Artigo em Inglês | MEDLINE | ID: mdl-34496854

RESUMO

BACKGROUND: With the increased use of implanted medical devices follows a large number of explantations. Implants are removed for a wide range of reasons, including manufacturing defects, recovery making the device unnecessary, battery depletion, availability of new and better models, and patients asking for a removal. Explantation gives rise to a wide range of ethical issues, but the discussion of these problems is scattered over many clinical disciplines. METHODS: Information from multiple clinical disciplines was synthesized and analysed in order to provide a comprehensive approach to the ethical issues involved in the explantation of medical implants. RESULTS: Discussions and recommendations are offered on pre-implantation information about a possible future explantation, risk-benefit assessments of explantation, elective explantations demanded by the patient, explantation of implants inserted for a clinical trial, patient registers, quality assurance, routines for investigating explanted implants, and demands on manufacturers to prioritize increased service time in battery-driven implants and to market fewer but more thoroughly tested models of implants. CONCLUSION: Special emphasis is given to the issue of control or ownership over implants, which underlies many of the ethical problems concerning explantation. It is proposed that just like transplants, implants that fulfil functions normally carried out by biological organs should be counted as supplemented body parts. This means that the patient has a strong and inalienable right to the implant, but upon explantation it loses that status.


Assuntos
Remoção de Dispositivo , Procedimentos Cirúrgicos Eletivos , Previsões , Humanos , Estudos Retrospectivos , Medição de Risco
16.
J Health Soc Behav ; 62(3): 255-270, 2021 09.
Artigo em Inglês | MEDLINE | ID: mdl-34528486

RESUMO

From 1940 to 1980, studies of medical education were foundational to sociology, but attention shifted away from medical training in the late 1980s. Recently, there has been a marked return to this once pivotal topic, reflecting new questions and stakes. This article traces this resurgence by reviewing recent substantive research trends and setting the agenda for future research. We summarize four current research foci that reflect and critically map onto earlier projects in this subfield while driving theoretical development elsewhere in the larger discipline: (1) professional socialization, (2) knowledge regimes, (3) stratification within the profession, and (4) sociology of the field of medical education. We then offer six potential future directions where more research is needed: (1) inequalities in medical education, (2) socialization across the life course and new institutional forms of gatekeeping, (3) provider well-being, (4) globalization, (5) medical education as knowledge-based work, and (6) effects of the COVID-19 pandemic.


Assuntos
Educação Médica , Sociologia , Educação Médica/métodos , Educação Médica/organização & administração , Previsões , História do Século XX , História do Século XXI , Humanos , Modelos Educacionais , Profissionalismo , Racismo , Sexismo , Fatores Socioeconômicos , Sociologia/história , Sociologia/métodos , Sociologia/tendências
18.
Lab Anim (NY) ; 50(10): 273-276, 2021 Oct.
Artigo em Inglês | MEDLINE | ID: mdl-34561679

Assuntos
Previsões
20.
Sensors (Basel) ; 21(18)2021 Sep 17.
Artigo em Inglês | MEDLINE | ID: mdl-34577447

RESUMO

Power system planning and expansion start with forecasting the anticipated future load requirement. Load forecasting is essential for the engineering perspective and a financial perspective. It effectively plays a vital role in the conventional monopolistic operation and electrical utility planning to enhance power system operation, security, stability, minimization of operation cost, and zero emissions. Two Well-developed cases are discussed here to quantify the benefits of additional models, observation, resolution, data type, and how data are necessary for the perception and evolution of the electrical load forecasting in Jordan. Actual load data for more than a year is obtained from the leading electricity company in Jordan. These cases are based on total daily demand and hourly daily demand. This work's main aim is for easy and accurate computation of week ahead electrical system load forecasting based on Jordan's current load measurements. The uncertainties in forecasting have the potential to waste money and resources. This research proposes an optimized multi-layered feed-forward neural network using the recent Grey Wolf Optimizer (GWO). The problem of power forecasting is formulated as a minimization problem. The experimental results are compared with popular optimization methods and show that the proposed method provides very competitive forecasting results.


Assuntos
Eletricidade , Redes Neurais de Computação , Previsões , Jordânia , Incerteza
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