The Impact of Information Technology on Emergency Health Care Outcomes

This paper analyzes the productivity of technology and job design in emergency response systems, or 911 systems.' During the 1990s, many 911 systems adopted Enhanced 911' (E911), where information technology is used to link automatic caller identification to a database of address and location information. A potential benefit to E911 is improved timeliness of the emergency response. We evaluate the returns to E911 in the context of a panel dataset of Pennsylvania counties during 1994-1996, when almost half of the 67 counties experienced a change in technology. We measure productivity using an index of health status of cardiac patients at the time of ambulance arrival, where the index should be improved by timely response. We also consider the direct effect of E911 on several patient outcomes, including mortality within the first hours following the incident and the total hospital charges incurred by the patient. Our main finding is that E911 increases the short-term survival rates for patients with cardiac diagnoses by about 1%, from a level of 96.2%. We also provide evidence that E911 reduces hospital charges. Finally, we analyze the effect of job design, in particular the use of Emergency Medical Dispatching' (EMD), where call-takers gather medical information, provide medical instructions over the telephone, and prioritize the allocation of ambulance and paramedic services. Controlling for EMD adoption does not affect our results about E911, and we find that EMD and E911 do not have significant interactions in determining outcomes (that is, they are neither substitutes nor complements).

A Hybrid Model to Analyze Air Pollution Spread Scales in Xi\u27 an and Surrounding Cities

Air quality analysis and prediction are very important in environmental research as airborne pollution has become a significant health threat, especially in Chinese urban agglomerations. Most previous analysis systems have been based on direct factors, such as pollutant concentrations, wind speeds and direction, relative humidity, and temperature; however, the air quality in a city is also affected by the air quality conditions in surrounding areas. This paper proposes a novel strategy for the analysis and forecast of air quality levels, for which Artificial Neural Networks (ANNs) are employed to elucidate the complex relationships between air quality and meteorological predictor variables. The experimental results in the study demonstrated that the normalized EEMD-ANN model outperformed other models in terms of the Precise, MAE and MAPE. The proposed model, therefore, demonstrated its potential as an administrative tool for issuing air pollution forecasts and for designing suitable abatement strategies

Emergent Medical Data: Health Information Inferred by Artificial Intelligence

Artificial intelligence (AI) can infer health data from people’s behavior even when their behavior has no apparent connection to their health. AI can monitor one’s location to track the spread of infectious disease, scrutinize retail purchases to identify pregnant customers, and analyze social media to predict who might attempt suicide. These feats are possible because, in modern societies, people continuously interact with internet-enabled software and devices. Smartphones, wearables, and online platforms monitor people’s actions and produce digital traces, the electronic remnants of their behavior. In their raw form, digital traces might not be very interesting or useful; one’s location, retail purchases, and internet browsing habits are relatively mundane data points. However, AI can enhance the value of digital traces by transforming them into something more useful—emergent medical data (EMD). EMD is health information inferred by artificial intelligence from otherwise trivial digital traces. This Article describes how EMD-based profiling is increasingly promoted as a solution to public health crises such as the COVID-19 pandemic, gun violence, and the opioid crisis. However, there is little evidence to show that EMD-based profiling works. Even worse, it can cause significant harm, and current privacy and data protection laws contain loopholes that allow public and private entities to mine EMD without people’s knowledge or consent. After describing the risks and benefits of EMD mining and profiling, the Article proposes six different ways of conceptualizing these practices. It concludes with preliminary recommendations for effective regulation. Potential options include banning or restricting the collection of digital traces, regulating EMD mining algorithms, and restricting how EMD can be used once it is produced

Input Parameters Comparison on NARX Neural Network to Increase the Accuracy of Stock Prediction

The trading of stocks is one of the activities carried out all over the world. To make the most profit, analysis is required, so the trader could determine whether to buy or sell stocks at the right moment and at the right price. Traditionally, technical analysis which is mathematically processed based on historical price data can be used. Parallel to technological development, the analysis of stock price and its forecasting can also be accomplished by using computer algorithms e.g. machine learning. In this study, Nonlinear Auto Regressive network with eXogenous inputs (NARX) neural network simulations were performed to predict the stock index prices. Experiments were implemented using various configurations of input parameters consisting of Open, High, Low, Closed prices in conjunction with several technical indicators for maximum accuracy. The simulations were carried out by using stock index data sets namely JKSE (Indonesia Jakarta index) and N225 (Japan Nikkei index). This work showed that the best input configurations can predict the future 13 days Close prices with 0.016 and 0.064 mean absolute error (MAE) for JKSE and N225 respectively.Â

An analysis of ensemble empirical mode decomposition applied to trend prediction on financial time series

Orientador : Luiz Eduardo S. OliveiraCoorientador : David MenottiDissertação (mestrado) - Universidade Federal do Paraná, Setor de Ciências Exatas, Programa de Pós-Graduação em Informática. Defesa: Curitiba, 20/07/2017Inclui referências : f. 63-72Resumo: As séries temporais financeiras são notoriamente difíceis de analisar e prever dada sua natureza não estacionária e altamente oscilatória. Nesta tese, a eficácia da técnica de decomposição não-paramétrica Ensemble Empirical Mode Decomposition (EEMD) é avaliada como uma técnica de extração de característica de séries temporais provenientes de índices de mercado e taxas de câmbio, características estas usadas na classificação, juntamente com diferentes modelos de aprendizado de máquina, de tendências de curto prazo. Os resultados obtidos em dois datasets de dados financeiros distintos sugerem que os resultados promissores relatados na literatura foram obtidos com a adição, inadvertida, de lookahead bias (viés) proveniente da aplicação desta técnica como parte do pré-processamento das séries temporais. Em contraste com as conclusões encontradas na literatura, nossos resultados indicam que a aplicação do EEMD com o objetivo de gerar uma melhor representação dos dados financeiração, por si só, não é suficiente para melhorar substancialmente a precisão e retorno cumulativo obtidos por modelos preditivos em comparação aos resultados obtidos com a utilização de series temporais de mudanças percentuais. Palavras-chave: Predição de Tendencias, Aprendizado de Máquina, Séries Temporais Financeiras.Abstract: Financial time series are notoriously difficult to analyse and predict, given their nonstationary, highly oscillatory nature. In this thesis, the effectiveness of the Ensemble Empirical Mode Decomposition (EEMD) is evaluated at generating a representation for market indexes and exchange rates that improves short-term trend prediction for these financial instruments. The results obtained in two different financial datasets suggest that the promising results reported using EEMD on financial time series in other studies were obtained by inadvertently adding look-ahead bias to the testing protocol via pre-processing the entire series with EEMD, which do affect the predictive results. In contrast to conclusions found in the literature, our results indicate that the application of EEMD with the objective of generating a better representation for financial time series is not sufficient, by itself, to substantially improve the accuracy and cumulative return obtained by the same models using the raw data. Keywords: Trend Prediction, Machine Learning, Financial Time Series

DEK-Forecaster: A Novel Deep Learning Model Integrated with EMD-KNN for Traffic Prediction

Internet traffic volume estimation has a significant impact on the business policies of the ISP (Internet Service Provider) industry and business successions. Forecasting the internet traffic demand helps to shed light on the future traffic trend, which is often helpful for ISPs decision-making in network planning activities and investments. Besides, the capability to understand future trend contributes to managing regular and long-term operations. This study aims to predict the network traffic volume demand using deep sequence methods that incorporate Empirical Mode Decomposition (EMD) based noise reduction, Empirical rule based outlier detection, and $K$-Nearest Neighbour (KNN) based outlier mitigation. In contrast to the former studies, the proposed model does not rely on a particular EMD decomposed component called Intrinsic Mode Function (IMF) for signal denoising. In our proposed traffic prediction model, we used an average of all IMFs components for signal denoising. Moreover, the abnormal data points are replaced by $K$ nearest data points average, and the value for $K$ has been optimized based on the KNN regressor prediction error measured in Root Mean Squared Error (RMSE). Finally, we selected the best time-lagged feature subset for our prediction model based on AutoRegressive Integrated Moving Average (ARIMA) and Akaike Information Criterion (AIC) value. Our experiments are conducted on real-world internet traffic datasets from industry, and the proposed method is compared with various traditional deep sequence baseline models. Our results show that the proposed EMD-KNN integrated prediction models outperform comparative models.Comment: 13 pages, 9 figure

Short-Term Forecasting of Passenger Demand under On-Demand Ride Services: A Spatio-Temporal Deep Learning Approach

Short-term passenger demand forecasting is of great importance to the on-demand ride service platform, which can incentivize vacant cars moving from over-supply regions to over-demand regions. The spatial dependences, temporal dependences, and exogenous dependences need to be considered simultaneously, however, which makes short-term passenger demand forecasting challenging. We propose a novel deep learning (DL) approach, named the fusion convolutional long short-term memory network (FCL-Net), to address these three dependences within one end-to-end learning architecture. The model is stacked and fused by multiple convolutional long short-term memory (LSTM) layers, standard LSTM layers, and convolutional layers. The fusion of convolutional techniques and the LSTM network enables the proposed DL approach to better capture the spatio-temporal characteristics and correlations of explanatory variables. A tailored spatially aggregated random forest is employed to rank the importance of the explanatory variables. The ranking is then used for feature selection. The proposed DL approach is applied to the short-term forecasting of passenger demand under an on-demand ride service platform in Hangzhou, China. Experimental results, validated on real-world data provided by DiDi Chuxing, show that the FCL-Net achieves better predictive performance than traditional approaches including both classical time-series prediction models and neural network based algorithms (e.g., artificial neural network and LSTM). This paper is one of the first DL studies to forecast the short-term passenger demand of an on-demand ride service platform by examining the spatio-temporal correlations.Comment: 39 pages, 10 figure