Objectives: Hand, foot and mouth disease (HFMD) is a widespread infectious disease that causes a significant disease burden on society. To achieve early intervention and to prevent outbreaks of disease, we propose a novel warning model that can accurately predict the incidence of HFMD. Methods: We propose a spatial-temporal graph convolutional network (STGCN) that combines spatial factors for surrounding cities with historical incidence over a certain time period to predict the future occurrence of HFMD in Guangdong and Shandong between 2011 and 2019. The 2011-2018 data served as the training and verification set, while data from 2019 served as the prediction set. Six important parameters were selected and verified in this model and the deviation was displayed by the root mean square error and the mean absolute error. Results: As the first application using a STGCN for disease forecasting, we succeeded in accurately predicting the incidence of HFMD over a 12-week period at the prefecture level, especially for cities of significant concern. Conclusions This model provides a novel approach for infectious disease prediction and may help health administrative departments implement effective control measures up to 3 months in advance, which may significantly reduce the morbidity associated with HFMD in the future.
China/epidemiology* ; Cities/epidemiology* ; Data Visualization ; Disease Outbreaks/statistics & numerical data* ; Forecasting/methods* ; Hand, Foot and Mouth Disease/prevention & control* ; Humans ; Incidence ; Neural Networks, Computer ; Reproducibility of Results ; Spatio-Temporal Analysis ; Time Factors

Minimally invasive surgery represented by laparoscopic technique has been carried out in China for more than 30 years. Gastrointestinal minimally invasive surgery has been widely recognized and popularized. Today, when the development of minimally invasive technology has reached the ceiling, the authors, who have experienced the innovation of minimally invasive gastrointestinal surgery for more than 30 years, review the gradual, unpredictable but inevitable characteristics of the innovation and development of minimally invasive surgery; figure out that standardized promotion and systematic training are the main reasons for the success of minimally invasive surgery in gastrointestinal surgery; realize that the application and promotion of new medical technology are inseparable from the support of solid clinical and basic evidence; recognize that the re-innovation after the popularization and standardization of gastrointestinal minimally invasive surgery and how to avoid involution are the driving force to seize the development momentum of minimally invasive technology. We make a multidimensional thinking on the development of gastrointestinal minimally invasive surgery, and objectively analyze its development track, in order to calmly rise to the challenges of future technological development.
Digestive System Surgical Procedures/methods* ; Forecasting ; Gastrointestinal Tract/surgery* ; Humans ; Laparoscopy/methods* ; Minimally Invasive Surgical Procedures/methods*

PURPOSE: The pollen calendar is the simplest forecasting method for pollen concentrations. As pollen concentrations are liable to seasonal variations due to alterations in climate and land-use, it is necessary to update the pollen calendar using recent data. To attenuate the impact of considerable temporal and spatial variability in pollen concentrations on the pollen calendar, it is essential to employ a new methodology for its creation.METHODS: A pollen calendar was produced in Korea using data from recent observations, and a new method for creating the calendar was proposed, considering both risk levels and temporal resolution of pollen concentrations. A probability distribution was used for smoothing concentrations and determining risk levels. Airborne pollen grains were collected between 2007 and 2017 at 8 stations; 13 allergenic pollens, including those of alder, Japanese cedar, birch, hazelnut, oak, elm, pine, ginkgo, chestnut, grasses, ragweed, mugwort and Japanese hop, were identified from the collected grains.RESULTS: The concentrations of each pollen depend on locations and seasons due to large variability in species distribution and their environmental condition. In the descending order of concentration, pine, oak and Japanese hop pollens were found to be the most common in Korea. The pollen concentrations were high in spring and autumn, and those of oak and Japanese hop were probably the most common cause of allergy symptoms in spring and autumn, respectively. High Japanese cedar pollen counts were observed in Jeju, while moderate concentrations were in Jeonju, Gwangju and Busan.CONCLUSIONS: A new methodology for the creation of a pollen calendar was developed to attenuate the impact of large temporal and spatial variability in pollen concentrations. This revised calendar should be available to the public and allergic patients to prevent aggravation of pollen allergy.
Alnus ; Ambrosia ; Artemisia ; Asian Continental Ancestry Group ; Betula ; Busan ; Climate ; Corylus ; Cryptomeria ; Forecasting ; Ginkgo biloba ; Gwangju ; Humans ; Hypersensitivity ; Jeollabuk-do ; Korea ; Methods ; Poaceae ; Pollen ; Rhinitis, Allergic, Seasonal ; Seasons

China ; Deep Learning ; Emergency Service, Hospital/statistics & numerical data* ; Forecasting/methods* ; Humans ; Triage ; Weather

BACKGROUND: People who were born in different years, that is, different birth cohorts, grow in varying socio-historical and dynamic contexts, which result in differences in social dispositions and physical abilities. METHODS: This study used age-period-cohort analysis method to establish explanatory models on healthcare expenditure in Korea reflecting birth cohort factor using intrinsic estimator. Based on these models, we tried to investigate the effects of ageing population on future healthcare expenditure through simulation by scenarios. RESULTS: Coefficient of cohort effect was not as high as that of age effect, but greater than that of period effect. The cohort effect can be interpreted to show ‘healthy ageing’ phenomenon. Healthy ageing effect shows annual average decrease of −1.74% to 1.57% in healthcare expenditure. Controlling age, period, and birth cohort effects, pure demographic effect of population ageing due to increase in life expectancy shows annual average increase of 1.61%–1.80% in healthcare expenditure. CONCLUSION: First, since the influence of population factor itself on healthcare expenditure increase is not as big as expected. Second, ‘healthy ageing effect’ suggests that there is a need of paradigm shift to prevention centered-healthcare services. Third, forecasting of health expenditure needs to reflect social change factors by considering birth cohort effect.
Cohort Effect ; Cohort Studies ; Delivery of Health Care ; Forecasting ; Health Expenditures ; Korea ; Life Expectancy ; Methods ; Parturition ; Population Dynamics ; Social Change

OBJECTIVES: Accurate prediction of patients' length of stay is highly important. This study compared the performance of artificial neural network and adaptive neuro-fuzzy system algorithms to predict patients' length of stay in intensive care units (ICU) after cardiac surgery. METHODS: A cross-sectional, analytical, and applied study was conducted. The required data were collected from 311 cardiac patients admitted to intensive care units after surgery at three hospitals of Shiraz, Iran, through a non-random convenience sampling method during the second quarter of 2016. Following the initial processing of influential factors, models were created and evaluated. RESULTS: The results showed that the adaptive neuro-fuzzy algorithm (with mean squared error [MSE] = 7 and R = 0.88) resulted in the creation of a more precise model than the artificial neural network (with MSE = 21 and R = 0.60). CONCLUSIONS: The adaptive neuro-fuzzy algorithm produces a more accurate model as it applies both the capabilities of a neural network architecture and experts' knowledge as a hybrid algorithm. It identifies nonlinear components, yielding remarkable results for prediction the length of stay, which is a useful calculation output to support ICU management, enabling higher quality of administration and cost reduction.
Cardiac Surgical Procedures ; Critical Care* ; Decision Support Techniques ; Forecasting ; Heart Diseases ; Humans ; Intensive Care Units* ; Iran ; Length of Stay* ; Methods ; Thoracic Surgery*

Head and neck cancers become a severe threat to human's health nowadays and represent the sixth most common cancer worldwide. Surgery remains the first-line choice for head and neck cancer patients. Limited resectable tissue mass and complicated anatomy structures in the head and neck region put the surgeons in a dilemma between the extensive resection and a better quality of life for the patients. Early diagnosis and treatment of the pre-malignancies, as well as real-time in vivo detection of surgical margins during en bloc resection, could be leveraged to minimize the resection of normal tissues. With the understanding of the head and neck oncology, recent advances in optical hardware and reagents have provided unique opportunities for real-time pre-malignancies and cancer imaging in the clinic or operating room. Optical imaging in the head and neck has been reported using autofluorescence imaging, targeted fluorescence imaging, high-resolution microendoscopy, narrow band imaging and the Raman spectroscopy. In this study, we reviewed the basic theories and clinical applications of optical imaging for the diagnosis and treatment in the field of head and neck oncology with the goal of identifying limitations and facilitating future advancements in the field.
Forecasting ; Head and Neck Neoplasms ; diagnostic imaging ; Humans ; Optical Imaging ; methods

As a newly-identified protein post-translational modification, malonylation is involved in a variety of biological functions. Recognizing malonylation sites in substrates represents an initial but crucial step in elucidating the molecular mechanisms underlying protein malonylation. In this study, we constructed a deep learning (DL) network classifier based on long short-term memory (LSTM) with word embedding (LSTM) for the prediction of mammalian malonylation sites. LSTM performs better than traditional classifiers developed with common pre-defined feature encodings or a DL classifier based on LSTM with a one-hot vector. The performance of LSTM is sensitive to the size of the training set, but this limitation can be overcome by integration with a traditional machine learning (ML) classifier. Accordingly, an integrated approach called LEMP was developed, which includes LSTM and the random forest classifier with a novel encoding of enhanced amino acid content. LEMP performs not only better than the individual classifiers but also superior to the currently-available malonylation predictors. Additionally, it demonstrates a promising performance with a low false positive rate, which is highly useful in the prediction application. Overall, LEMP is a useful tool for easily identifying malonylation sites with high confidence. LEMP is available at http://www.bioinfogo.org/lemp.
Amino Acid Sequence ; genetics ; Amino Acids ; Animals ; Deep Learning ; Forecasting ; methods ; Lysine ; chemistry ; Machine Learning ; Malonates ; chemistry ; Protein Processing, Post-Translational ; genetics

OBJECTIVES: Controlling hospital high length of stay outliers can provide significant benefits to hospital management resources and lead to cost reduction. The strongest predictive factors influencing high length of stay outliers should be identified to build a high-performance prediction model for hospital outliers. METHODS: We highlight the application of the hierarchical genetic algorithm to provide the main predictive factors and to define the optimal structure of the prediction model fuzzy radial basis function neural network. To establish the prediction model, we used a data set of 26,897 admissions from five different intensive care units with discharges between 2001 and 2012. We selected and analyzed the high length of stay outliers using the trimming method geometric mean plus two standard deviations. A total of 28 predictive factors were extracted from the collected data set and investigated. RESULTS: High length of stay outliers comprised 5.07% of the collected data set. The results indicate that the prediction model can provide effective forecasting. We found 10 common predictive factors within the studied intensive care units. The obtained main predictive factors include patient demographic characteristics, hospital characteristics, medical events, and comorbidities. CONCLUSIONS: The main initial predictive factors available at the time of admission are useful in evaluating high length of stay outliers. The proposed approach can provide a practical tool for healthcare providers, and its application can be extended to other hospital predictions, such as readmissions and cost.
Comorbidity ; Data Mining ; Dataset ; Forecasting ; Health Personnel ; Humans ; Intensive Care Units ; Length of Stay* ; Machine Learning ; Medical Informatics ; Methods

Penile implant usage dates to the 16th century yet penile implants to treat erectile dysfunction did not occur until nearly four centuries later. The modern era of penile implants has progressed rapidly over the past 50 years as physicians' knowledge of effective materials for penile prostheses and surgical techniques has improved. Herein, we describe the history of penile prosthetics and the constant quest to improve the technology. Elements of the design from the first inflatable penile prosthesis by Scott and colleagues and the Small-Carrion malleable penile prosthesis are still found in present iterations of these devices. While there have been significant improvements in penile prosthesis design, the promise of an ideal prosthetic device remains elusive. As other erectile dysfunction therapies emerge, penile prostheses will have to continue to demonstrate a competitive advantage. A particular strength of penile prostheses is their efficacy regardless of etiology, thus allowing treatment of even the most refractory cases.
Biomedical Technology ; Erectile Dysfunction/*surgery/*therapy ; Forecasting ; Humans ; Male ; Penile Implantation/*methods ; Penile Prosthesis/*trends ; Penis/*surgery