Design and implementation of a deep recurrent model for prediction of readmission in urgent care using electronic health records

There has been a steady growth in machine learning research in healthcare, however, progress is difficult to measure because of the use of different cohorts, task definitions and input variables. To take the advantage of the availability and value of digital health data, we aim to predict unplanned readmissions to the intensive care unit (ICU) from a publicly available Critical Care dataset called Medical Information Mart for Intensive Care (MIMIC-III). In this research, we formulate a heterogeneous LSTM and CNN architecture specifically to create a model of readmission risk. Our proposed predictive framework outperformed all the benchmark classifiers such as support vector machine, random forest and logistic regression models on all performance measures (AUC, accuracy and precision) except on recall where random forest performed slightly better. Predictions from these models will help in resource planning and decrease mortality or length of stay in clinical care settings

A deep learning approach for length of stay prediction in clinical settings from medical records

Deep neural networks are becoming an increasingly popular solution for predictive modeling using electronic health records because of their capability of learning complex patterns and behaviors from large volumes of patient records. In this paper, we have applied an autoencoded deep neural network algorithm aimed at identifying short(0-7 days) and long stays (>7 days) in hospital based on patient admission records, demographics, diagnosis codes and chart events. We validated our approach using the de-identified MIMIC-III dataset. This proposed autoencoder+DNN model shows that the two classes are separable with 73.2% accuracy based upon ICD-9 and demographics features. Once vital chart events data such as body temperature, blood pressure, heart rate information available after 24 hour of admission is added to the model, the classification accuracy is increased up to 77.7%. Our results showed a better performance when compared to a baseline random forest model

Discovering Drug-Drug Interactions Using Association Rule Mining from Electronic Health Records

In this paper, we propose utilising Electronic Health Records (EHR) to discover previously unknown drug-drug interactions (DDI) that may result in high rates of hospital readmissions. We used association rule mining and categorised drug combinations as high or low risk based on the adverse events they caused. We demonstrate that the drug combinations in the high-risk group contain significantly more drug-drug interactions than those in the low-risk group. This approach is efficient for discovering potential drug interactions that lead to negative outcomes, thus should be given priority and evaluated in clinical trials. In fact, severe drug interactions can have life-threatening consequences and result in adverse clinical outcomes. Our findings were achieved using a new association rule metric, which better accounts for the adverse drug events caused by DDI

A deep learning approach for length of stay prediction in clinical settings from medical records

Deep neural networks are becoming an increasingly popular solution for predictive modeling using electronic health records because of their capability of learning complex patterns and behaviors from large volumes of patient records. In this paper, we have applied an autoencoded deep neural network algorithm aimed at identifying short(0-7 days) and long stays (>7 days) in hospital based on patient admission records, demographics, diagnosis codes and chart events. We validated our approach using the de-identified MIMIC-III dataset. This proposed autoencoder+DNN model shows that the two classes are separable with 73.2% accuracy based upon ICD-9 and demographics features. Once vital chart events data such as body temperature, blood pressure, heart rate information available after 24 hour of admission is added to the model, the classification accuracy is increased up to 77.7%. Our results showed a better performance when compared to a baseline random forest model

A structured review of long-term care demand modelling

Long-term care (LTC) represents a significant and substantial proportion of healthcare spends across the globe. Its main aim is to assist individuals suffering with more or more chronic illnesses, disabilities or cognitive impairments, to carry out activities associated with daily living. Shifts in several economic, demographic and social factors have raised concerns surrounding the sustainability of current systems of LTC. Substantial effort has been put into modelling the LTC demand process itself so as to increase understanding of the factors driving demand for LTC and its related services. Furthermore, such modeling efforts have also been used to plan the operation and future composition of the LTC system itself. The main aim of this paper is to provide a structured review of the literature surrounding LTC demand modeling and any such industrial application, whilst highlighting any potential direction for future researchers

Carbon Footprints in Emergency Departments: A Simulation-Optimization Analysis

It is globally accepted to act against global warming through the reduction of carbon dioxide. Carbon footprint is historically defined as the total emissions caused by an individual, event, organization, or product, expressed as carbon dioxide equivalent. Healthcare system consumes large amount of energy in order to provide health services to patients who have to pass a series of treatment processes at each care unit. These treatments require different medical equipment that consume electrical power, and the more electrical power consumption is, the more greenhouse gases specifically CO2 emissions are. The discrete-event simulation has been applied to develop the model of the treatment process and the estimation of carbon dioxide in the treatment process. By the knowledge that the simulation is not an optimization method in itself, the OptQuest optimization method has been applied to reduce greenhouse gases and carbon footprint in the patients′ flow in the emergency department by considering leveling off the waiting time and length of stay as constraints to leveling up patient′s satisfaction. The numerical results provided by simulation and OptQuest show the efficiency of OptQuest as a technique for patient flow optimization

A Grid implementation for profiling hospitals based on patient readmissions

Generally, high level of readmission is associated with poor patient care, hence its relation to the quality of care is plausible. Frequent patient readmissions have personal, financial and organisational consequences. This has motivated healthcare commissioners in England to use emergency readmission as an indicator in the performance rating framework. A statistical model, known as the multilevel transition model was previously developed, where individual hospitals propensity for first readmission, second readmission, third (and so on) were considered to be measures of performance. Using these measures, we defined a new performance index. During the period 1997 and 2004, the national (England) hospital episodes statistics dataset comprise more than 5 million patient readmissions. Implementing a statistical model using the complete population dataset could possibly take weeks to estimate the parameters. Moreover, it is not statistically sound to utilise the full population dataset. To resolve the problem, we extract 1000 random samples from the original data, where each random sample is likely to lead to differing hospital performance measures. For computational efficiency a Grid implementation of the model is developed. Using a stand-alone computer, it would take approximately 500 hours to estimate 1000 samples, whereas in the Grid implementation, the full 1000 samples were analysed in less than 24 hours. Analysing the output from the full 1000 sample, we noticed that 4 out of the 5 worst performing hospitals treating cancer patients were in London

Balancing the NHS balanced scorecard!

In the UK, the split between opposition and supporters views of the National Health Service (NHS) performance ratings system is growing. Objective argument and consensus would be facilitated if a methodology was developed which showed the cause and effect relationships between the components of the performance rating system. The NHS hospital trust performance ratings data used in 2002 and 2003 were downloaded from the Department of Health performance rating website. Structural equation modelling was used to construct a causal-loop diagram showing the cause and effect relationships between the 16 common performance indicators in the two years. Scenario testing suggests that indicators of delayed transfer of care and of data quality are compromised if emergency readmissions performance is improved