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The inventory management of medical items in humanitarian operations is a challenging task due to the intermittent nature of their demand and long replenishment lead-times. An effective response to humanitarian emergencies is often associated with excess inventories, which leads to high costs. Henceforth, using accurate demand forecasts to control the inventories of these medical items is shown to be a significant lever to reduce inventory costs while keeping high service levels. This paper investigates the effectiveness of parametric and non-parametric demand forecasting methods that are commonly considered to deal with items characterised with intermittent demand patterns. Moreover, we propose a new hybrid deep learning method that combines the long short-term memory neural network and the support vector regression. Such combination enables to deal with complex and non-linear data. We conduct an empirical investigation by means of data related to 523 medical items managed in two warehouses of a major humanitarian organisation. The forecast accuracy and the inventory efficiency of the methods are analysed. The empirical results show the high performance of the deep learning methods. These insights are valuable not only in the context of medical items in humanitarian operations, but also for any items with intermittent demand patterns. [ABSTRACT FROM AUTHOR]

The ability to foresee the outbreak of epidemic disease, and to predict its course, is a highly coveted skill. Most often associated with statistical techniques, such efforts to improve the health of communities are thought to be exclusively modern. Public health more generally is often said to be categorically distinct from pre-modern medicine, which was interested above all in individual patients and rarely considered the broader health of a population. This article challenges these long-entrenched views by showcasing early modern astrologers at work on disease at the population level. Located at the intersection of medicine and mathematics, astrology was once a promising methodology for monitoring the health of the people. Astrologers made annual predictions about the diseases that would predominate in particular regions, disseminating their forecasts widely alongside advice on what should be done in response. Their ability to think in terms of relatively standardized 'populations' — alongside their attempts to correlate the incidence of disease with external factors — made astrologers prime candidates for developing novel approaches to epidemics. Before the epidemiologist there was the astrologer, who looked to the stars to find patterns between celestial configurations and major health events, using their findings to model the rise and fall of epidemics. [ABSTRACT FROM AUTHOR]

Drug-drug interaction (DDI) extraction in biomedical literature is a particular entity relation classification task, which is significant for biopharmaceutical research. Current researchers mainly concentrated on fine-tuning the Pre-trained Language Model (PLM) on medical datasets to forecast the interactions between target drugs. Despite the above approaches having shown positive results, it is still confronted with some serious limitations. First of all, ordinary fine-tuning makes it difficult to take the best advantage of prior knowledge embedded in the PLM, due to the wide gap between objective forms of pre-training and downstream tasks. Secondly, the issue of limited training data or category imbalance also poses a great challenge for real-world DDI extraction tasks. In this work, we propose a novel model using Prompt Tuning and Data Augmentation (PTDA) to extract the DDI. This method can augment training data and alleviate the adverse effects of category imbalance by properly utilizing contextual word embedding substitution to generate examples for specific relation types. Furthermore, we have tried to introduce prompt tuning into the DDI extraction process, aiming to narrow the gap between pre-training and downstream tasks to leverage the prior knowledge in the PLM fully. We conduct a series of experiments on diverse biomedical datasets to verify the performance of our model. The results show that PTDA hugely outperforms existing DDI extraction methods, achieving F1-micro of 84.9%, 79.8%, and 73.1% on benchmark datasets, DDI 2013, ChemProt, and DTIs, respectively. Therefore, we believe that the PTDA model holds considerable potential for future practical applications. [ABSTRACT FROM AUTHOR]

Meteorological conditions and air quality are important environmental factors in the occurrence and development of cardiovascular diseases (CVDs) such as hypertension. The aim of this study was to take Haikou City, located on the tropical edge, as the research area and to analyze the exposure–response relationship and lag effect between its meteorological conditions, air quality, and the number of hypertensive patients. Using the data from the hypertension outpatient department of Hainan Provincial People's Hospital from 2016 to 2018, together with meteorological data and air quality data, a distributed lag nonlinear model based on the nested generalized addition model of meteorological element base variables was established. The results showed that the impact of temperature on the risk of hypertension was mainly due to the cold effect, which was associated with high risk, with a lag of 1–10 days. When the temperature dropped to 10 °C, the cumulative effect on the risk of hypertension of relative risk (RR) reached its highest value on the day the low temperature occurred (RR was 2.30 and the 95% confidence interval was 1.723~3.061), passing the test with a significance level of 0.05. This result indicated that efforts should be made to strengthen the prevention of hypertension under low-temperature conditions and the prediction and early warning of disease risks. The impact of the air-quality effect (the environmental Air Quality Index was selected as an indicator) on the risk of hypertension was mainly characterized by a low air-quality effect, with a lag effect of 0–8 days. When the risk reached approximately 124, the RR was highest (RR was 1.63 and the 95% confidence interval was 1.104~2.408), passing the test with a significance level of 0.05. The research results can provide technical support for conducting medical meteorological forecasting, early warning, and services for hypertension. A joint work and research mechanism among multiple departments such as meteorology and medical health should be established to improve the level of medical and health care, optimize the allocation of social resources, and develop targeted prevention and control strategies to reduce the health and economic burden of hypertension. [ABSTRACT FROM AUTHOR]

Medical tourism is considered nowadays as a multi-billion industry which can promote a country's economic growth. Therefore, forecasting the scheduled tourism demand for medical services is of great importance for policy makers. Doing so, however, is not an easy task due to the following reasons: Data on medical tourism are (i) not easily accessible; (ii) not typically distinguished from tourists' non-scheduled (unintentional) use of a country's medical services; and (iii) usually not publicly available for long time periods. In this paper, we present a novel way to forecast tourism demand (intentional and unintentional) for medical services --a rough but informative proxy of medical tourism-- using limited data. To perform the analysis, we use data on the percentage of hospital discharges of non-residents for 17 European countries over the period 2008-2019 retrieved from Eurostat. Our methodological approach is based on a forecasting technique recently developed by Kyriazi, Thomakos and Guerard ; the adaptive learning forecasting. According to this method, MSE (Mean Squared Error)-performance enhancements can be achieved using any forecast as input --as long as that input is not a 'perfect' forecast-- by learning from past forecast errors. Within this context, even the most basic forecast, the no-change or naïve forecast, can be used as input to the adaptive learning procedure. Kyriazi, Thomakos and Guerard approach is very well suited to our research question because (i) the no-change forecast is a natural candidate in a short time series where models cannot be estimated with sufficient accuracy, (ii) the no-change forecast is obviously far from being the 'perfect' forecast, and (iii) the adaptive learning process can be initialized by the no-change forecast and then learn by its own past forecast errors. Our results show that adaptive learning forecasting leads to performance enhancements that range from the 5% to more than 20% relative to the no-change benchmark. This finding indicates the efficiency of the adaptive learning method in forecasting medical tourism demand; an important subcategory of tourism demand for which data are not easily accessible and freely available historical data are existing for short time periods. [ABSTRACT FROM AUTHOR]

The COVID-19 epidemic has in fact placed the whole community in a dire predicament that has led to numerous tragedies, including an economic downturn, political unrest, and job losses. Forecasting and identifying COVID-19 infection cases is crucial for the government at all levels because the pandemic grows exponentially and results in fatalities. Hence, by giving information about the spread of the epidemic, the government can move quickly at multiple levels to establish new policies and modalities in order to minimize the trajectory of the COVID-19 pandemic's effects on both public health and the economic sectors. Forecasting models for COVID-19 infection cases in the Ural region in Russia were developed using two deep Long Short-Term Memory (LSTM) learning-based approaches namely Encoder–Decoder LSTM and Attention LSTM algorithms. The models were evaluated based on five standard performance evaluation metrics which include Mean Square Error (MSE), Mean Absolute Error (MAE), Root MSE (RMSE), Relative RMSE (RRMSE), and coefficient of determination (R2). However, the Encoder–Decoder LSTM deep learning-based forecasting model achieved the best performance results (MSE=32794.09, MAE=168.56, RMSE=181.09, RRMSE=13.46, and R2=0.87) compared to the model developed with Attention LSTM models. [ABSTRACT FROM AUTHOR]

Visual electrophysiological deficits have been reported in neurodegenerative disorders as well as in mental disorders. Such alterations have been mentioned in both the retina and the cortex, notably affecting the photoreceptors, retinal ganglion cells (RGCs) and the primary visual cortex. Interestingly, such impairments emphasize the functional role of the visual system. For this purpose, the present study reviews the existing literature with the aim of identifying key alterations in electroretinograms (ERGs) and visual evoked potentials electroencephalograms (VEP-EEGs) of subjects with neurodegenerative and psychiatric disorders. We focused on psychiatric and neurodegenerative diseases due to similarities in their neuropathophysiological mechanisms. Our research focuses on decoupled and coupled ERG/VEP-EEG results obtained with black-and-white checkerboards or low-level visual stimuli. A decoupled approach means recording first the ERG, then the VEP-EEG in the same subject with the same visual stimuli. The second method means recording both ERG and VEP-EEG simultaneously in the same participant with the same visual stimuli. Both coupled and decoupled results were found, indicating deficits mainly in the N95 ERG wave and the P100 VEP-EEG wave in Parkinson's, Alzheimer's, and major depressive disorder. Such results reinforce the link between the retina and the visual cortex for the diagnosis of psychiatric and neurodegenerative diseases. With that in mind, medical devices using coupled ERG/VEP-EEG measurements are being developed in order to further investigate the relationship between the retina and the visual cortex. These new techniques outline future challenges in mental health and the use of machine learning for the diagnosis of mental disorders, which would be a crucial step toward precision psychiatry. [ABSTRACT FROM AUTHOR]

Accurate prediction is a fundamental and leading work of the emergency medicine reserve management. Given that the emergency medicine reserve demand is affected by various factors during the public health events and thus the observed data are composed of different but hard-to-distinguish components, the traditional demand forecasting method is not competent for this case. To bridge this gap, this paper proposes the EMD-ELMAN-ARIMA (ELA) model which first utilizes Empirical Mode Decomposition (EMD) to decompose the original series into various components. The Elman neural network and ARIMA models are employed to forecast the identified components and the final forecast values are generated by integrating the individual component predictions. For the purpose of validation, an empirical study is carried out based on the influenza data of Beijing from 2014 to 2018. The results clearly show the superiority of the proposed ELA algorithm over its two rivals including the ARIMA and ELMAN models. [ABSTRACT FROM AUTHOR]

Background and Aim: Treatment response to ursodeoxycholic acid may predict the prognosis of patients with primary biliary cholangitis (PBC). Recent studies have suggested the benefits of using machine learning (ML) to forecast complex medical predictions. We aimed to predict treatment response in patients with PBC using ML and pretreatment data. Methods: We conducted a single‐center retrospective study and collected data from 194 patients with PBC who were followed up for at least 12 months after treatment initiation. Patient data were analyzed with five ML models, namely random forest, extreme gradient boosting (XGB), decision tree, naïve Bayes, or logistic regression, to predict treatment response using the Paris II criteria. The established models were assessed using an out‐of‐sample validation. The area under the curve (AUC) was used to evaluate the efficacy of each algorithm. Overall survival and liver‐related deaths were analyzed using Kaplan–Meier analysis. Results: Compared to logistic regression (AUC = 0.595, P = 0.0219, 0.031 models), ML analyses showed significantly high AUC in the random forest (AUC = 0.84) and XGB (AUC = 0.83) models; however, the AUC was not significantly high for decision tree (AUC = 0.633) or naïve Bayes (AUC = 0.584) models. Kaplan–Meier analysis showed significantly improved prognoses in patients predicted to achieve the Paris II criteria by XGB (log‐rank = 0.005 and 0.007). Conclusion: ML algorithms could improve treatment response prediction using pretreatment data, which could lead to better prognoses. In addition, the ML model using XGB could predict the prognosis of patients before treatment initiation. [ABSTRACT FROM AUTHOR]

In this work we introduce the personalized online super learner (POSL), an online personalizable ensemble machine learning algorithm for streaming data. POSL optimizes predictions with respect to baseline covariates, so personalization can vary from completely individualized, that is, optimization with respect to subject ID, to many individuals, that is, optimization with respect to common baseline covariates. As an online algorithm, POSL learns in real time. As a super learner, POSL is grounded in statistical optimality theory and can leverage a diversity of candidate algorithms, including online algorithms with different training and update times, fixed/offline algorithms that are not updated during POSL's fitting procedure, pooled algorithms that learn from many individuals' time series, and individualized algorithms that learn from within a single time series. POSL's ensembling of the candidates can depend on the amount of data collected, the stationarity of the time series, and the mutual characteristics of a group of time series. Depending on the underlying data‐generating process and the information available in the data, POSL is able to adapt to learning across samples, through time, or both. For a range of simulations that reflect realistic forecasting scenarios and in a medical application, we examine the performance of POSL relative to other current ensembling and online learning methods. We show that POSL is able to provide reliable predictions for both short and long time series, and it's able to adjust to changing data‐generating environments. We further cultivate POSL's practicality by extending it to settings where time series dynamically enter and exit. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has had a catastrophic effect on the healthcare system including organ transplants worldwide. The number of living donor transplants performed in the US was affected more significantly by the pandemic with a 22.6% decrease in counts from 2019 to 2020 due to concerns of unnecessarily exposing potential living donors and living donor recipients to possible COVID-19 infection. This paper examines donor transplant counts obtained from the United Network for Organ Sharing from January 2002 to August 2021 using an intervention time series model with March 2020 as the intervention event. Specifically, donor transplant counts are analyzed across the different organs, donor types, and some major individual sociocultural factors, which are potential conditions contributing to disparities in achieving donor transplant equity such as age, ethnicity, and gender. In addition, the kidney allocation policy implemented in March 2021 is introduced as a second intervention event for kidney donor transplants. Overall, forecasts generated by our methods are more accurate than those using seasonal autoregressive integrated moving average models without interventions and seasonal naive methods. The intervention time series model provides a forecast accuracy comparable to the exponential smoothing method. [ABSTRACT FROM AUTHOR]

Since May 2022, over 64,000 Monkeypox cases have been confirmed globally up until September 2022. The United States leads the world in cases, with over 25,000 cases nationally. This recent escalation of the Monkeypox outbreak has become a severe and urgent worldwide public health concern. We aimed to develop an efficient forecasting tool that allows health experts to implement effective prevention policies for Monkeypox and shed light on the case development of diseases that share similar characteristics to Monkeypox. This research utilized five machine learning models, namely, ARIMA, LSTM, Prophet, NeuralProphet, and a stacking model, on the Monkeypox datasets from the CDC official website to forecast the next 7-day trend of Monkeypox cases in the United States. The result showed that NeuralProphet achieved the most optimal performance with a RMSE of 49.27 and R 2 of 0.76. Further, the final trained NeuralProphet was employed to forecast seven days of out-of-sample cases. On the basis of cases, our model demonstrated 95% accuracy. [ABSTRACT FROM AUTHOR]

Early circulatory failure detection is an effective way to reduce medical fatigue and improve state pre-warning ability. Instead of using 0-1 original state, a transformed state is proposed in this research, which reflects how the state is transformed. The performance of the proposed method is compared with the original method under three models, including logistic regression, AdaBoost and XGBoost. The results show that the model XGBoost generally has the best performance measured by AUC, F1 and Sensitivity with values around 0.93, 0.91 and 0.90, at the prediction gaps 5, 10 and 20 separately. Under the model XGBoost, the method with transformed response variable has significantly better performance than that with the original response variable, with the performance metrics being around 1% to 4% higher, and the t values are all significant under the level 0.01. In order to explore the model performance under different baseline information, a subgroup analysis is conducted under sex, age, weight and height. The results demonstrate that sex and age have more significant influence on the model performance especially at the higher gaps than weight and height. [ABSTRACT FROM AUTHOR]

Copyright of Radio Electronics, Computer Science, Control is the property of Zaporizhzhia National Technical University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Background: Coronavirus outbreak (SARS-CoV-2) has become a serious threat to human society all around the world. Due to the rapid rate of disease outbreaks and the severe shortages of medical resources, predicting COVID-19 disease severity continues to be a challenge for healthcare systems. Accurate prediction of severe patients plays a vital role in determining treatment priorities, effective management of medical facilities, and reducing the number of deaths. Various methods have been used in the literature to predict the severity prognosis of COVID-19 patients. Despite the different appearance of the methods, they all aim to achieve generalizable results by increasing the accuracy and reducing the errors of predictions. In other words, accuracy is considered the only effective factor in the generalizability of models. In addition to accuracy, reliability and consistency of results are other critical factors that must be considered to yield generalizable medical predictions. Since the role of reliability in medical decisions is significant, upgrading reliable medical data-driven models requires more attention. Methods: This paper presents a new modeling technique to specify and maximize the reliability of results in predicting the severity prognosis of COVID-19 patients. We use the well-known classic regression as the basic model to implement our proposed procedure on it. To assess the performance of the proposed model, it has been applied to predict the severity prognosis of COVID-19 by using a dataset including clinical information of 46 COVID-19 patients. The dataset consists of two types of patients' outcomes including mild (discharge) and severe (ICU or death). To measure the efficiency of the proposed model, we compare the accuracy of the proposed model to the classic regression model. Results: The proposed reliability-based regression model, by achieving 98.6% sensitivity, 88.2% specificity, and 93.10% accuracy, has better performance than classic accuracy-based regression model with 95.7% sensitivity, 85.5% specificity, and 90.3% accuracy. Also, graphical analysis of ROC curve showed AUC 0.93 (95% CI 0.88–0.98) and AUC 0.90 (95% CI 0.85–0.96) for classic regression models, respectively. Conclusions: Maximizing reliability in the medical forecasting models can lead to more generalizable and accurate results. The competitive results indicate that the proposed reliability-based regression model has higher performance in predicting the deterioration of COVID-19 patients compared to the classic accuracy-based regression model. The proposed framework can be used as a suitable alternative for the traditional regression method to improve the decision-making and triage processes of COVID-19 patients. [ABSTRACT FROM AUTHOR]

Forecasting medical costs is crucial for planning, budgeting, and efficient decision making in the health industry. This paper introduces a proposal to forecast costs through techniques such as a standard model of long short-term memory (LSTM); and patient grouping through k-means clustering in the Keralty group, one of Colombia's leading healthcare companies. It is important to highlight its implications for the prediction of cost time series in the health sector from a retrospective analysis of the information of services invoiced to health companies. It starts with the selection of sociodemographic variables related to the patient, such as age, gender and marital status, and it is complemented with health variables such as patient comorbidities (cohorts) and induced variables, such as service provision frequency and time elapsed since the last consultation (hereafter referred to as "recency"). Our results suggest that greater accuracy can be achieved by first clustering and then using LSTM networks. This implies that a correct segmentation of the population according to the usage of services represented in costs must be performed beforehand. Through the analysis, a cost projection from 1 to 3 months can be conducted, allowing a comparison with historical data. The reliability of the model is validated by different metrics such as RMSE and Adjusted R2. Overall, this study is intended to be useful for healthcare managers in developing a strategy for medical cost forecasting. We conclude that the use of analytical tools allows the organization to make informed decisions and to develop strategies for optimizing resources with the identified population. [ABSTRACT FROM AUTHOR]

Copyright of Electronics & Control Systems is the property of National Aviation University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This paper proposed a hybrid intelligent approach based on empirical mode decomposition (EMD), autoregressive integrated moving average (ARIMA) and Monte Carlo simulation (MCS) methods for multi-step ahead medical tourism (MT) forecasting using explanatory input variables based on two decade real-time recorded database. In the proposed hybrid model, these variables are 1st extracted then medical tourism is forecasted to perform the long term as well as the short term goal and planning in the nation. The multi-step ahead medical tourism is forecasted recursively, by utilizing the 1st forecasted value as the input variable to generate the next forecasting value and this procedure is continued till third step ahead forecasted value. The proposed approach is firstly tested and validated by using international tourism arrival (ITA) dataset then proposed approach is implemented for forecasting of medical tourism arrival in nation. In order to validate the performance and accuracy of the proposed hybrid model, a comparative analysis is performed by using Monte Carlo method and the results are compared. Obtained results show that the proposed hybrid forecasting approach for medical tourism has outperforming characteristics. [ABSTRACT FROM AUTHOR]

This paper reports the analysis of a forecasting problem based on time series. It is noted that the forecasting stage itself is preceded by the stages of selection of forecasting methods, determining the criterion for the forecast quality, and setting the optimal prehistory step. As one of the criteria for a forecast quality, its volatility has been considered. Improving the volatility of the forecast could ensure a decrease in the absolute value of the deviation of forecast values from actual data. Such a criterion should be used in forecasting in medicine and other socially important sectors. To implement the principle of competition between forecasting methods, it is proposed to categorize them based on the values of deviations in the forecast results from the exact values of the elements of the time series. The concept of dominance among forecasting methods has been introduced; rules for the selection of dominant and accurate enough predictive models have been defined. Applying the devised rules could make it possible, at the preceding stages of the analysis of the time series, to reject in advance the models that would surely fail from the list of predictive models available to use. In accordance with the devised method, after applying those rules, a system of functions is built. The functions differ in the sets of predictive models whose forecasting results are taken into consideration. Variables in the functions are the weight coefficients with which predictive models are included. Optimal values for the variables, as well as the optimal model, are selected as a result of minimizing the functions built. The devised method was experimentally verified. It has been shown that the constructed method made it possible to reduce the forecast error from 0.477 and 0.427 for basic models to 0.395 and to improve the volatility of the forecast from 1969.489 and 1974.002 to 1607.065. [ABSTRACT FROM AUTHOR]

Objectives: To determine whether a statistically derived, trend-based, deterioration index is superior to other early warning scores at predicting adverse events and whether it can be integrated into an electronic medical record to enable real-time alerts.Design: Forty-three variables and their trends from cases and controls were used to develop a logistic model and deterioration index to predict patient deterioration greater than or equal to 1 hour prior to an adverse event.Setting: Two large Australian teaching hospitals.Patients: Cases were considered as patients who suffered adverse events (unexpected death, unplanned ICU transfer, urgent surgery, and rapid-response alert) between August 1, 2016, and April 1, 2019.Interventions: The logistic model and deterioration index were tested on historical data and then integrated into an electronic medical record for a 6-month prospective "silent" validation.Measurements and Main Results: Data were acquired from 258,732 admissions. There were 8,002 adverse events. The addition of vital sign and laboratory trend values to the logistic model increased the area under the curve from 0.84 to 0.89 and the sensitivity to predict an adverse event 1-48 hours prior from 0.35 to 0.41. A 48-hour simulation showed that the logistic model had a higher area under the curve than the Modified Early Warning Score and National Early Warning Score (0.87 vs 0.74 vs 0.71). During the silently run prospective trial, the sensitivity of the deterioration index to detect adverse event any time prior to the adverse event was 0.474, 0.369 1 hour prior, and 0.327 4 hours prior, with a specificity of 0.972.Conclusions: A deterioration prediction model was developed using patient demographics, ward-based observations, laboratory values, and their trends. The model's outputs were converted to a deterioration index that was successfully integrated into a live hospital electronic medical record. The sensitivity and specificity of the tool to detect inpatient deterioration were superior to traditional early warning scores. [ABSTRACT FROM AUTHOR]

Nowadays, Artificial intelligence (AI), combined with the digitalization of healthcare, can lead to substantial improvements in Patient Care, Disease Management, Hospital Administration, and supply chain effectiveness. Among predictive analytics tools, time series forecasting represents a central task to support healthcare management in terms of bookings and medical services predictions. In this context, the development of flexible frameworks to provide robust and reliable predictions became a central point in this healthcare innovation process. This paper presents and discusses a multi-source time series fusion and forecasting framework relying on Deep Learning. By combining weather, air-quality and medical bookings time series through a feature compression stage which preserves temporal patterns, the prediction is provided through a flexible ensemble technique based on machine learning models and a hybrid neural network. The proposed system is able to predict the number of bookings related to a specific medical examination for a 7-days horizon period. To assess the proposed approach's effectiveness, we rely on time series extracted from a real dataset of administrative e-health records provided by the Campania Region health department, in Italy. • We propose a multi-source time-series fusion methodology relying on Deep Learning. • We design a robust framework for daily 7-days-horizon predictions. • We assess the proposed approach on a real medical bookings dataset. [ABSTRACT FROM AUTHOR]

Abstract Background Accurate forecasting of medical service demand is beneficial for the reasonable healthcare resource planning and allocation. The daily outpatient volume is characterized by randomness, periodicity and trend, and the time series methods, like ARIMA are often used for short-term outpatient visits forecasting. Therefore, to further enlarge the prediction horizon and improve the prediction accuracy, a hybrid prediction model integrating ARIMA and self-adaptive filtering method is proposed. Methods The ARIMA model is first used to identify the features like cyclicity and trend of the time series data and to estimate the model parameters. The parameters are then adjusted by the steepest descent algorithm in the adaptive filtering method to reduce the prediction error. The hybrid model is validated and compared with traditional ARIMA by several test sets from the Time Series Data Library (TSDL), a weekly emergency department (ED) visit case from literature study, and the real cases of prenatal examinations and B-ultrasounds in a maternal and child health care center (MCHCC) in Ningbo. Results For TSDL cases the prediction accuracy of the hybrid prediction is improved by 80–99% compared with the ARIMA model. For the weekly ED visit case, the forecasting results of the hybrid model are better than those of both traditional ARIMA and ANN model, and similar to the ANN combined data decomposition model mentioned in the literature. For the actual data of MCHCC in Ningbo, the MAPE predicted by the ARIMA model in the two departments was 18.53 and 27.69%, respectively, and the hybrid models were 2.79 and 1.25%, respectively. Conclusions The hybrid prediction model outperforms the traditional ARIMA model in both accurate predicting result with smaller average relative error and the applicability for short-term and medium-term prediction.

Understanding disease transmission is a complex problem highlighted by the COVID‐19 pandemic. These studies test whether 3‐ to 6‐year‐old children in the United States use information about social interactions to predict disease transmission. Before and during COVID‐19, children predicted illness would spread through close interactions. Older children outperformed younger children with no associations between task performance and pandemic experience. Children did not predict that being hungry or tired would similarly spread through close interactions. Participants include 196 three‐ to six‐year‐olds (53% girls, 47% boys; 68% White, 9% Black, 7% Asian, 6% Hispanic or Latinx), with medium‐sized effects (d =.6, ηp2 =.3). These findings suggest that thinking about social interaction supports young children's predictions about illness, with noted limitations regarding children's real‐world avoidance of disease‐spreading behaviors. [ABSTRACT FROM AUTHOR]

We present a prototype system, OptiLoc, that aims at analysing consequences of medical capacity restrictions under the Covid-19 regime as well as at providing medical analysts and decision makers with medical procedure relocation plans for a given area in a given time period. This is achieved by first forecasting the demand of medical procedures of different types at the selected geographical granularity level and then finding relocations which are optimal according to a well defined objective function that takes into account procedure and relocation costs, under constraints imposed by Covid-19 restrictions and hospital capacities. Allocation plans are visualized in a user friendly web-based application. We demonstrate the effectiveness of the developed system on the data on urological procedures from Poland. [ABSTRACT FROM AUTHOR]

Coronavirus disease 2019(COVID-19) has brought great disasters to humanity, and its influence continues to intensify. In response to the public health emergencies, prompt relief supplies are key to reduce the damage. This paper presents a method of emergency medical logistics to quick response to emergency epidemics. The methodology includes two recursive mechanisms: (1) the time-varying forecasting of medical relief demand according to a modified susceptible-exposed-infected- Asymptomatic- recovered (SEIAR) epidemic diffusion model, (2) the relief supplies distribution based on a multi-objective dynamic stochastic programming model. Specially, the distribution model addresses a hypothetical network of emergency medical logistics with considering emergency medical reserve centers (EMRCs), epidemic areas and e-commerce warehousing centers as the rescue points. Numerical studies are conducted. The results show that with the cooperation of different epidemic areas and e-commerce warehousing centers, the total cost is 6% lower than without considering cooperation of different epidemic areas, and 9.7% lower than without considering cooperation of e-commerce warehousing centers. Particularly, the total cost is 20% lower than without considering any cooperation. This study demonstrates the importance of cooperation in epidemic prevention, and provides the government with a new idea of emergency relief supplies dispatching, that the rescue efficiency can be improved by mutual rescue between epidemic areas in public health emergency. [ABSTRACT FROM AUTHOR]

Background: Cumulative data of case-fatality rates (CFR) of COVID-19 varied across countries. A forecasting model generated based on detailed information from three countries during the initial phase of pandemic showed that progression rates from pneumonia to ARDS (PRPA) varied by country and were highly associated with CFR. We aim to elucidate the impact of the PRPA on COVID-19 deaths in different periods of pandemic.Methods: We used the country-based, real-time global COVID-19 data through GitHub repository to estimate PRPA on the first period (January to June), second period (July to September), and third period (October to December) in 2020. PRPA was used for predicting COVID-19 deaths and assessing the reduction in deaths in subsequent two periods.Results: The estimated PRPA varied widely from 0.38% to 51.36%, with an average of 15.99% in the first period. The PRPA declined to 8.44% and 6.35% in the second and third period. The CFR declined stepwise and was 4.94%, 2.61%, and 1.96%, respectively. Some countries exhibited a decrease in the PRPA from the second to the third period whereas others showed the opposite, particularly where selected viral mutants were prevalent. Overall, the number of observed deaths was lower than that of the predicted deaths in the second and third periods, suggesting an improvement in management of COVID-19 patients. Besides, the degree of improvement depends on the extent of change in PRPA.Conclusion: PRPA is a useful indicator to facilitate decision making and assess the improvement of clinical management and medical capacity by forecasting deaths. [ABSTRACT FROM AUTHOR]

During the coronavirus disease 2019 (COVID-19) pandemic, rapid and accurate triage of patients at the emergency department is critical to inform decision-making. We propose a data-driven approach for automatic prediction of deterioration risk using a deep neural network that learns from chest X-ray images and a gradient boosting model that learns from routine clinical variables. Our AI prognosis system, trained using data from 3661 patients, achieves an area under the receiver operating characteristic curve (AUC) of 0.786 (95% CI: 0.745–0.830) when predicting deterioration within 96 hours. The deep neural network extracts informative areas of chest X-ray images to assist clinicians in interpreting the predictions and performs comparably to two radiologists in a reader study. In order to verify performance in a real clinical setting, we silently deployed a preliminary version of the deep neural network at New York University Langone Health during the first wave of the pandemic, which produced accurate predictions in real-time. In summary, our findings demonstrate the potential of the proposed system for assisting front-line physicians in the triage of COVID-19 patients. [ABSTRACT FROM AUTHOR]

Objectives. To determine the number of excess deaths (i.e., those exceeding historical trends after accounting for COVID-19 deaths) occurring in Florida during the COVID-19 pandemic. Methods. Using seasonal autoregressive integrated moving average time-series modeling and historical mortality trends in Florida, we forecasted monthly deaths from January to September of 2020 in the absence of the pandemic. We compared estimated deaths with monthly recorded total deaths (i.e., all deaths regardless of cause) during the COVID-19 pandemic and deaths only from COVID-19 to measure excess deaths in Florida. Results. Our results suggest that Florida experienced 19 241 (15.5%) excess deaths above historical trends from March to September 2020, including 14 317 COVID-19 deaths and an additional 4924 all-cause, excluding COVID-19, deaths in that period. Conclusions. Total deaths are significantly higher than historical trends in Florida even when accounting for COVID-19–related deaths. The impact of COVID-19 on mortality is significantly greater than the official COVID-19 data suggest. [ABSTRACT FROM AUTHOR]

Purpose: Research in the cognitive and neural sciences has situated predictive processing--the anticipation of upcoming percepts--as a dominant function of the brain. The purpose of this article is to argue that prediction should feature more prominently in explanatory accounts of sentence processing and comprehension deficits in developmental language disorder (DLD). Method: We evaluate behavioral and neurophysiological data relevant to the theme of prediction in early typical and atypical language acquisition and processing. Results: Poor syntactic awareness--attributable, in part, to an underlying statistical learning deficit--is likely to impede syntax-based predictive processing in children with DLD, conferring deficits in spoken sentence comprehension. Furthermore, there may be a feedback cycle in which poor syntactic awareness impedes children's ability to anticipate upcoming percepts, and this, in turn, makes children unable to improve their syntactic awareness on the basis of prediction error signals. Conclusion: This article offers a refocusing of theory on sentence processing and comprehension deficits in DLD, from a difficulty in processing and integrating perceived syntactic features to a difficulty in anticipating what is coming next. [ABSTRACT FROM AUTHOR]

Introduction/purpose: The research subject in this article is the status of the air quality in the city of Novi Sad and its impact on the health of the population. The relation between these two points will be presented using the DPSEEA model. The research aim of this article is to apply proper measures in order to mitigate negative effects on human health, based on scientific knowledge related to air quality influence on the health of the Novi Sad population. The basic hypothesis of this research implies that air quality affects Novi Sad's population health. Methods: The following methods were used in this article: modeling, analysis-synthesis, statistic method, classification method, combining method, geographic-ecological method, method ofgeographic-medical description, method of the medical geography forecast as well as cartographic method. Results: Based on the DPSEEA model and the example of the air pollution in the city of Novi Sad, the following was identified: air pollution driving forces, pressures, state of the air quality, population exposure to air pollution, effects on human health as well as measures and actions that should be conducted. The mentioned model was also used to present the relationship between these segments. Conclusion: The given model deals with a wide spectrum ofpotential forces (harmful effects) and necessary community actions, bringing together professionals, people in the field and those from laboratories as well as managers in the area of environment and public health management, in order to tackle emerging problems in a comprehensive manner. [ABSTRACT FROM AUTHOR]

Precision Medicine has emerged as a preventive, diagnostic and treatment tool to approach human diseases in a personalized manner. Since precision medicine incorporates omics data and knowledge in personal health records, people who live in industrially polluted areas have an advantage in the medicinal field. Integration of non-omics data and related biological knowledge in term omics data is a reality. The heterogenic characteristics of non-omics data and high dimensional omics data makes the integration challengeable. Hard data analytics problems create better opportunities in analytics. This review cut across the boundaries of machine learning models for the eventual development of a successful precision medicine forecast model, different strategies for the integration of non-omics data and omics data, limitations and challenges in data integration, and future directions for the precision medicine forecasts. The literature also discusses non-omics data, diseases associated with air pollutants, and omics data. This information gives insight to the integrated data analytics and their application in future project implications. It intends to motivate researchers and precision medicine forecast model developers in a global integrative analytical approach. [ABSTRACT FROM AUTHOR]

Deep learning is one of the most exciting new areas in medical imaging. This review article provides a summary of the current clinical applications of deep learning for lesion detection, progression, and prediction of musculoskeletal disease on radiographs, computed tomography (CT), magnetic resonance imaging (MRI), and nuclear medicine. Deep‐learning methods have shown success for estimating pediatric bone age, detecting fractures, and assessing the severity of osteoarthritis on radiographs. In particular, the high diagnostic performance of deep‐learning approaches for estimating pediatric bone age and detecting fractures suggests that the new technology may soon become available for use in clinical practice. Recent studies have also documented the feasibility of using deep‐learning methods for identifying a wide variety of pathologic abnormalities on CT and MRI including internal derangement, metastatic disease, infection, fractures, and joint degeneration. However, the detection of musculoskeletal disease on CT and especially MRI is challenging, as it often requires analyzing complex abnormalities on multiple slices of image datasets with different tissue contrasts. Thus, additional technical development is needed to create deep‐learning methods for reliable and repeatable interpretation of musculoskeletal CT and MRI examinations. Furthermore, the diagnostic performance of all deep‐learning methods for detecting and characterizing musculoskeletal disease must be evaluated in prospective studies using large image datasets acquired at different institutions with different imaging parameters and different imaging hardware before they can be implemented in clinical practice. Level of Evidence: 5 Technical Efficacy Stage: 2 J. MAGN. RESON. IMAGING 2020;52:1607–1619. [ABSTRACT FROM AUTHOR]

The mathematical model of Support Vector Machine (SVM) will strong foundation offers upright approach in solving data science problems. In the perspective of mathematics, the training input SVM build its mathematical model which is usage of the SVM as broadly categorized as classification, regression, novelty detection tasks, and feature reduction. Now-a-days in many medical fields the technique of SVM will be utilized to determine or predict. It resembles a Computer helped analysis framework to support radiologist to distinguish Abnormalities prior and all the more quickly. The principle goal is to ensure fidelity and replication of detailed grouping comes about when an alternate example is examined. The early forecast of medical diseases like, breast cancer, skin disease and additionally thyroid. Texture classification is play an essential part in computer aided diagnostic system; the applications incorporate medicinal picture investigation and understanding, remote detecting. In this paper the advancement of the computer aided diagnostic system by using the mathematical of the SVM for the classification of mass characterization in medical diseases in view of the model of Support Vector Machine and the results are carried out by utilizing R-Language programming. [ABSTRACT FROM AUTHOR]

Purpose: We aimed to identify if there is an association between the severity of cervical cancer at diagnosis and the pattern of recurrence.Methods: We conducted a retrospective study of recurrent cervical cancers diagnosed between 2016 and 2018. We characterized the cases according to histology, size, FIGO stage (according to 2009 and 2018 FIGO classifications) and nodal involvement at diagnosis, symptoms at the time of recurrence, interval between the end of treatment and recurrence, imaging methods used, and location of the recurrence. Statistical analysis was performed between histology, size, FIGO stage and nodal involvement at diagnosis and time to recurrence and type of recurrence (locoregional versus lymph node, distant or multiple site involvement).Results: We included 48 patients with recurrent cervical cancer. At diagnosis, mean tumor size was 5 cm and 83% of the patients had squamous cell carcinoma. The FIGO stage changed in 43.8% of patients between the 2009 and the 2018 classifications. A mean of 26 months elapsed between the end of treatment and recurrence. Recurrence was symptomatic in 64.6% of patients. Imaging identified recurrence in 97.9% of patients. The most frequent recurrence sites were locoregional and lymph node metastases. We found a statistically significant association between 2009 FIGO stage and time to recurrence (P = 0.030) and lymph node involvement at diagnosis and type of recurrence (P = 0.022). As expected patients with more advanced disease recurred sooner, though this was only observed for the 2009 FIGO classification. Absence of lymph nodes at initial diagnosis was associated with locoregional recurrence, while presence of lymph node involvement was associated with lymph node, distant or multiple site involvement of recurrence. No other significant associations were found.Conclusion: In our cohort of recurrent cervical cancer, we found an association between patients without lymph node metastases at initial diagnosis and locoregional recurrence. Further studies are needed in order to evaluate whether this association has predictive value. [ABSTRACT FROM AUTHOR]

The amount of data produced within health informatics has grown to be quite vast. The large volume of data generated by various vital sign monitoring devices needs to be analysed in real time to alert the care providers about changes in a patients condition. Data processing in real time has complex challenges for the large volume of data. The real-time system should be able to collect millions of events per seconds and handle parallel processing to extract meaningful information efficiently. In our study, we have proposed a real-time BigData and Predictive Analytical Architecture for healthcare application. The proposed architecture comprises three phases: (1) collection of data, (2) offline data management and prediction model building and (3) real-time processing and actual prediction. We have used Apache Kafka, Apache Sqoop, Hadoop, MapReduce, Storm and logistic regression to predict an emergency condition. The proposed architecture can perform early detection of emergency in real time, and can analyse structured and unstructured data like Electronic Health Record (EHR) to perform offline analysis to predict patient's risk for disease or readmission. We have evaluated prediction performance on different benchmark datasets to detect an emergency condition of any patient in real time and possibility of readmission. [ABSTRACT FROM AUTHOR]

To evaluate the performance of 2 methods predicting liver dosimetric indices for hepatocellular cancer patients treated with intensity modulated radiation therapy (IMRT). Two predicting methods were implemented to build correlation between geometric and dosimetric information of hepatocellular cancer IMRT plans. One method used Principle Component Analysis method to simplify information and Support Vector Machine (SVM) regression method to build correlation. The other method used a simple linear function to map volumes of certain regions to dosimetic indices. Thirty eight hepatocellular cancer IMRT plans were randomly selected to train the 2 methods. The effectiveness of the 2 methods was validated using another 8 plans. Liver dosimetric indices V 10 , V 20 , V 30 , and mean dose of the 8 plans in validation cohort were calculated using the predicting methods. The predicted indices were compared with the indices derived from the clinically accepted treatment plans. The absolute differences of V 10 calculated with the SVM method and the actual values of treatment plans had a mean value of 7.87%. And the values for V 20 , V 30 , and mean dose were 4.37%, 4.41%, and 4.20%, respectively. The absolute differences of V 10 calculated with the linear formulation method and the actual values of treatment plans had a mean value of 6.09%. And the values for V 20 , V 30 , and mean dose were 5.28%, 5.05%, and 5.92%, respectively. These 2 methods have similar accuracy in predicting dosimetric indices V 10 , V 20 , V 30 of livers. The predicting of V 30 and V 20 is more accurate than the predicting of V 10. The SVM method is more accurate in predicting mean liver dose. [ABSTRACT FROM AUTHOR]

The development of molecular diagnostic tools to achieve individualized medicine requires accurate estimation of individual treatment effects (ITEs). Although several effective data analytic strategies have been proposed for this purpose, they have limitations when it comes to flexibly capturing the complex relationships between clinical outcome and possibly high-dimensional covariates. In this article, we propose an effective machine learning method to estimate ITEs using the gradient boosting trees (GBT). GBT is a powerful nonparametric regression tool in machine learning, and its outstanding performance has been widely recognized for various applications. We use GBT to develop an estimation method for the ITE that is formulated under the potential outcome model framework. Our method can flexibly capture the relationship between clinical outcome and possibly high-dimensional covariates, and it would also be useful for identifying subpopulations of patients who would benefit from the treatment. Results of simulation studies and a real-data analysis of a breast cancer clinical study show that the proposed method can precisely estimate ITEs, and these estimates possibly identify the subgroup of patients who can benefit from treatment. [ABSTRACT FROM AUTHOR]

OBJECTIVES: Recognition of delirium in the emergency department (ED) is poor. Our objectives were to assess: (1) the diagnostic accuracy of the Predicting Emergency department Delirium with an Interactive Computer Tablet (PrEDICT) "serious game" to identify older ED patients with delirium compared to clinical recognition and (2) the feasibility of the PrEDICT application compared to existing tests of attention. DESIGN: Prospective observational study. SETTING: ED of a Canadian tertiary care center. PARTICIPANTS: We included ED patients, aged 70 years and older, with a minimum 4‐hour stay. We excluded anyone with critical illness, communication barriers, and visual impairment or those unable to use a computer tablet. None had prevalent delirium by ED clinicians' routine clinical assessment. MEASUREMENTS: Participants were asked to tap targets on a tablet at four difficulty levels. Time and accuracy were automatically recorded. Other measures included the Confusion Assessment Method, the Delirium Severity Index, the Digit Vigilance Test (DVT), and the Choice Reaction Test (CRT). RESULTS: We enrolled 203 patients. Their average age was 80.6 years, 49.8% were female, and their average ED length of stay was 15.9 hours. Sixteen subjects had clinically unrecognized delirium, and 14 of them completed the PrEDICT game (87.5%). We developed a threshold score with 100% sensitivity (95% confidence interval [CI] = 76.8%‐100.0%) and 59.7% specificity (95% CI = 52.3%‐66.6%) to identify patients with clinically unrecognized delirium. The area under the curve was 0.86 (95% CI = 0.77‐0.94). Completion rates were 196/203 (96.6%) for the PrEDICT serious game compared to 128/203 (63.1%) for the CRT and 51/203 (25.1%) for the DVT. CONCLUSION: Older ED patients were able to use our serious game, including 87.5% of those with clinically unrecognized delirium. The PrEDICT application has potential to act as a sensitive screening tool to identify older ED patients with clinically unrecognized delirium. J Am Geriatr Soc 67:2370–2375, 2019 [ABSTRACT FROM AUTHOR]

Taiwan is an endemic area for chronic hepatitis disease. Since the early 1980's, liver cancer has become the first cancer mortality causes among other cancers in Taiwan. Besides, liver cirrhosis and chronic liver diseases are the sixth rank and seventh rank in the causes of death, respectively. This is a serious disease affecting people's health and it brings a lot of medical cost as well. This study develops a medical cost forecasting model for the acute hepatitis patients in the emergency room. In order to consider the uncertainty and hesitation in the human being's thinking, this study employs the intuitionistic fuzzy logic (IFL) since it considers membership, non-membership, and hesitation values simultaneously. The proposed model combines the intuitionistic fuzzy neural network (IFNN) with Gaussian membership function and Yager-Generating function to enhance the performance of FNN. Furthermore, a back-propagation learning algorithm and genetic algorithm (GA) are applied in order to optimize the parameters and weights of the proposed IFNN. The proposed IFNN is applied to solve ten benchmark datasets including the nonlinear control and prediction problems. The computational results showed that the GA-IFNN is more efficient than conventional algorithms, such as an artificial neural network (ANN), a fuzzy neural network (FNN), and a support vector regression (SVR). In the real-world problem, the proposed method can really support physicians in planning medical resources and make a good decision to make the most efficient use of limited resources. [ABSTRACT FROM AUTHOR]

The tasks of predicting the possibility of changing the state of an object based on its evaluation by multiple criteria are considered. Medical forecasting tasks, for example, predicting the future occurrence of a person with a threatening condition, can be assigned to this class of tasks. This task can be formulated as a classification problem, where one class will be at high risk of the threat and the other will be at low risk. In this interpretation, the classification problem can be solved by using the Wald sequential analysis method. This method is based on Bayes' theorem and takes into account the informative nature of the features used for classification. This approach has a number of features related to defining thresholds and processing those attribute values that are close to thresholds. The article shows that if you use the Wald method for objects with feature values close to the thresholds, then you can get the opposite solution. In order to improve the classification efficiency, a fuzzy classification method is proposed. The peculiarity of the developed method is that the decision maker can specify the nature of the membership function to determine the proximity of the setpoints to the thresholds and thus set the interval of the allowed change of the thresholds. The algorithm calculates the degree of belonging of a given object to each of the classes. Experimental verification of the developed method for the problem of prediction of miscarriage is made. At the stage of formalization of medical knowledge such indicators were selected that can be used for prediction, a database of clinical material was created. Model examples demonstrate the advantages of the developed method over the Wald sequential analysis method. The results obtained in the study can be used in the construction of prognostic algorithms in medicine. [ABSTRACT FROM AUTHOR]

Background: Nomograms are graphical calculating devices used to predict risk of malignant transformation (MT) or response to treatment during cancer management. To date, a nomogram has not been used to predict clinical outcome during oral potentially malignant disorder (PMD) treatment. The aim of this study was to create a nomogram for use by clinicians to predict the probability of MT, thereby facilitating accurate assessment of risk and objective decision-making during individual patient management.Methods: Clinico-pathological data from a previously treated cohort of 590 newly presenting PMD patients were reviewed and clinical outcomes categorized as disease free, persistent PMD or MT. Multiple logistic regression was used to predict the probability of MT in the cohort using age, gender, lesion type, site and incision biopsy histopathological diagnoses. Internal validation and calibration of the model was performed using the bootstrap method (n = 1000), and bias-corrected indices of model performance were computed.Results: Potentially malignant disorders were predominantly leukoplakias (79%), presenting most frequently at floor of mouth and lateral tongue sites (51%); 99 patients (17%) developed oral squamous cell carcinoma during the study period. The nomogram performed well when MT predictions were compared with patient outcome data, demonstrating good bias-corrected discrimination and calibration (Dxy  = 0.58; C = 0.790), with a sensitivity of 87% and specificity 63%, and a positive predictive value of 32% and negative predictive value 96%.Conclusion: The "Newcastle Nomogram" has been developed to predict the probability of MT in PMD, based on an internally validated statistical model. Based upon readily available and patient-specific clinico-pathological data, it provides clinicians with a pragmatic diagrammatic aid for clinical decision-making during diagnosis and management of PMD. [ABSTRACT FROM AUTHOR]