Deep learning architecture to predict daily hospital admissions

Deep learning modelling to forecast emergency department visits using calendar, meteorological, internet search data and stock market price

BACKGROUND

Accurate prediction of hospital emergency department (ED) patient visits and acuity levels have potential to improve resource allocation including manpower planning and hospital bed allocation. Internet search data have been used in medical applications like disease pattern prediction and forecasting ED volume. Past studies have also found stock market price positively correlated with ED volume.

OBJECTIVE

To determine whether incorporating Internet search data and stock market price to calendar and meteorological data can improve deep learning prediction of ED patient volumes, and whether hybrid deep learning architectures are better in prediction.

METHODS

Permutations of various input variables namely calendar, meteorological, Google Trends online search data, Standard and Poor's (S&P) 500 index, and Straits Times Index (STI) data were incorporated into deep learning models long short-term memory (LSTM), one-dimensional convolutional neural network (1D CNN), stacked 1D CNN-LSTM, and five CNN-LSTM hybrid modules to predict daily Singapore General Hospital ED patient volume from 2010-2012.

RESULTS

Incorporating STI to calendar and meteorological data improved performance of CNN-LSTM hybrid models. Addition of queried absolute Google Trends search terms to calendar and meteorological data improved performance of two out of five hybrid models. The best LSTM model across all predictor permutations had mean absolute percentage error of 4.8672 %.

CONCLUSION

LSTM provides strong predictive ability for daily ED patient volume. Local stock market index has potential to predict ED visits. Amongst predictors evaluated, calendar and meteorological data was sufficient for a relatively accurate prediction.

Modeling impacts of traffic, air pollution, and weather conditions on cardiopulmonary disease mortality

AIMS

Cardiopulmonary disease (CPD) is a leading cause of death worldwide. Increasing evidence shows that air pollution and exposure to weather conditions have important contributory roles. Understanding the interaction of these factors is difficult due to the complexity of the relationship between CPD, air pollution, and environmental factors.

METHODS

This paper uses regression models and machine learning approaches to explore these relationships, and investigate whether meteorological factors and air pollution have a synergistic effect on CPD. We use daily data from 2009-2018 from four cities representing the heterogenous climate conditions in Norway: the far north, the west coast, mid-Norway, and the south-east.

RESULTS

We demonstrate the importance of the interaction between weather and air pollution associated with higher CPD mortality, as is exposure to air pollution in the form of NOx and particulate matter. This impact is seasonal. Traffic is also positively related to CPD mortality, which may be caused indirectly through increased pollution. We demonstrate that machine learning outperforms regression models in terms of the accuracy of predicting CPD mortality.

CONCLUSIONS

The inclusion of rich lagged structures and interactions between environmental factors are both important but can lead to overfitting of traditional models; since these cities are not large cities by international standards, it is surprising that environmental factors have such obvious impacts on CPD mortality. CPD mortality shows a clear negative trend, implying an improvement in the public health situation.

Exploring the correlation between airborne pollen levels and respiratory conditions in Jaipur, India

Airborne pollen, a significant natural pollutant, restricts outdoor activities and impacts quality of life for sensitive individuals with pulmonary disorders. This study examines trends in airborne pollen concentrations and explores whether air pollution, pollen concentration, or both impact patient counts. The annual pollen trend in Jaipur shows peaks in pollen concentration in March (due to trees, 66%), September (due to weeds, 45%), and December (due to grass, 50%). Among the fifteen taxa examined, Holoptelea integrifolia is the largest pollen emitter in Jaipur, followed by Poaceae, among others. The count of patients arriving for clinical consultations in a hospital shows a strong and positive correlation with weed (Asteraceae spp. and Argemone mexicana) and grass pollen. A linear regression equation is developed (R2 value = 0.835) for forecasting consulting patient counts based on Cassia siamea pollen concentration. This can assist hospital administration in resource management, especially during peak allergy seasons.

Imbalanced prediction of emergency department admission using natural language processing and deep neural network

The emergency department (ED) plays a very significant role in the hospital. Owing to the rising number of ED visits, medical service points, and ED market, overcrowding of EDs has become serious worldwide. Overcrowding has long been recognized as a vital issue that increases the risk to patients and negative emotions of medical personnel and impacts hospital cost management. For the past years, many researchers have been applying artificial intelligence to reduce crowding situations in the ED. Nevertheless, the datasets in ED hospital admission are naturally inherent with the high-class imbalance in the real world. Previous studies have not considered the imbalance of the datasets, particularly addressing the imbalance. This study purposes to develop a natural language processing model of a deep neural network with an attention mechanism to solve the imbalanced problem in ED admission. The proposed framework is used for predicting hospital admission so that the hospitals can arrange beds early and solve the problem of congestion in the ED. Furthermore, the study compares a variety of methods and obtains the best composition that has the best performance for forecasting hospitalization in ED. The study used the data from a specific hospital in Taiwan as an empirical study. The experimental result demonstrates that almost all imbalanced methods can improve the model's performance. In addition, the natural language processing model of Bi-directional Long Short-Term Memory with attention mechanism has the best results in all-natural language processing methods.

AI Models for Predicting Readmission of Pneumonia Patients within 30 Days after Discharge

A model with capability for precisely predicting readmission is a target being pursued worldwide. The objective of this study is to design predictive models using artificial intelligence methods and data retrieved from the National Health Insurance Research Database of Taiwan for identifying high-risk pneumonia patients with 30-day all-cause readmissions. An integrated genetic algorithm (GA) and support vector machine (SVM), namely IGS, were used to design predictive models optimized with three objective functions. In IGS, GA was used for selecting salient features and optimal SVM parameters, while SVM was used for constructing the models. For comparison, logistic regression (LR) and deep neural network (DNN) were also applied for model construction. The IGS model with AUC used as the objective function achieved an accuracy, sensitivity, specificity, and area under ROC curve (AUC) of 70.11%, 73.46%, 69.26%, and 0.7758, respectively, outperforming the models designed with LR (65.77%, 78.44%, 62.54%, and 0.7689, respectively) and DNN (61.50%, 79.34%, 56.95%, and 0.7547, respectively), as well as previously reported models constructed using thedata of electronic health records with an AUC of 0.71–0.74. It can be used for automatically detecting pneumonia patients with a risk of all-cause readmissions within 30 days after discharge so as to administer suitable interventions to reduce readmission and healthcare costs.

Effectiveness of cascading time series models based on meteorological factors in improving health risk prediction

Meteorological factors, which are periodic and regular in a long run, have an unignorable impact on human health. Accurate health risk prediction based on meteorological factors is essential for optimal allocation of resource in healthcare units. However, due to the non-stationary and non-linear nature of the original hospitalization sequence, traditional methods are less robust in predicting it. This study aims to investigate hospital admission prediction models using time series pre-processing algorithms and deep learning approach based on meteorological factors. Using the electronic medical record data from Panyu Central Hospital and meteorological data of Panyu district from 2003 to 2019, 46,089 eligible patients with lower respiratory tract infections (LRTIs) and four meteorological factors were identified to build and evaluate the prediction models. A novel hybrid model, Cascade GAM-CEEMDAN-LSTM Model (CGCLM), was established in combination with generalized additive model (GAM), complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), and long-short term memory (LSTM) networks for predicting daily admissions of patients with LRTIs. The experimental results show that CGCLM multistep method proposed in this paper outperforms single LSTM model in the prediction of health risk time series at different time window sizes. Moreover, our results also indicate that CGCLM has the best prediction performance when the time window is set to 61 days (RMSE = 1.12, MAE = 0.87, R2 = 0.93). Adequate extraction of exposure-response relationships between meteorological factors and diseases and suitable handling of sequence pre-processing have an important role in time series prediction. This hybrid climate-based model for predicting LRTIs disease can also be extended to time series prediction of other epidemic disease.

Performance evaluation of Emergency Department patient arrivals forecasting models by including meteorological and calendar information: A comparative study

The volume of daily patient arrivals at Emergency Departments (EDs) is unpredictable and is a significant reason of ED crowding in hospitals worldwide. Timely forecast of patients arriving at ED can help the hospital management in early planning and avoiding of overcrowding. Many different ED patient arrivals forecasting models have previously been proposed by using time series analysis methods. Even though the time series methods such as Linear and Logistic Regression, Autoregressive Integrated Moving Average (ARIMA), Seasonal ARIMA (SARIMA), Exponential Smoothing (ES), and Artificial Neural Network (ANN) have been explored extensively for the ED forecasting model development, the few significant limitations of these methods associated in the analysis of time series data make the models inadequate in many practical situations. Therefore, in this paper, Machine Learning (ML)-based Random Forest (RF) regressor, and Deep Neural Network (DNN)-based Long Short-Term Memory (LSTM) and Convolutional Neural network (CNN) methods, which have not been explored to the same extent as the other time series techniques, are implemented by incorporating meteorological and calendar parameters for the development of forecasting models. The performances of the developed three models in forecasting ED patient arrivals are evaluated. Among the three models, CNN outperformed for short-term (3 days in advance) patient arrivals prediction with Mean Absolute Percentage Error (MAPE) of 9.24% and LSTM performed better for moderate-term (7 days in advance) patient arrivals prediction with MAPE of 8.91% using weather forecast information. Whereas, LSTM model outperformed with MAPE of 8.04% compared to 9.53% by CNN and 10.10% by RF model for current day prediction of patient arrivals using 3 days past weather information. Thus, for short-term ED patient arrivals forecasting, DNN-based model performed better compared to RF regressor ML-based model.

Big data and new information technology: what cardiologists need to know

Technological progress in medicine is constantly garnering pace, requiring that physicians constantly update their knowledge. The new wave of technologies breaking through into clinical practice includes the following: a) mHealth, which allows constant monitoring of biological parameters, anytime, anyplace, of hundreds of patients at the same time; b) artificial intelligence, which, powered by new deep learning techniques, are starting to beat human experts at their own game: diagnosis by imaging or electrocardiography; c) 3-dimensional printing, which may lead to patient-specific prostheses; d) systems medicine, which has arisen from big data, and which will open the way to personalized medicine by bringing together genetic, epigenetic, environmental, clinical and social data into complex integral mathematical models to design highly personalized therapies. This state-of-the-art review aims to summarize in a single document the most recent and most important technological trends that are being applied to cardiology, and to provide an overall view that will allow readers to discern at a glance the direction of cardiology in the next few years.