Towards a threshold climate for emergency lower respiratory hospital admissions.

Identification of 'cut-points' or thresholds of climate factors would play a crucial role in alerting risks of climate change and providing guidance to policymakers. This study investigated a 'Climate Threshold' for emergency hospital admissions of chronic lower respiratory diseases by using a distributed lag non-linear model (DLNM). We analysed a unique longitudinal dataset (10 years, 2000-2009) on emergency hospital admissions, climate, and pollution factors for the Greater London. Our study extends existing work on this topic by considering non-linearity, lag effects between climate factors and disease exposure within the DLNM model considering B-spline as smoothing technique. The final model also considered natural cubic splines of time since exposure and 'day of the week' as confounding factors. The results of DLNM indicated a significant improvement in model fitting compared to a typical GLM model. The final model identified the thresholds of several climate factors including: high temperature (≥27°C), low relative humidity (≤ 40%), high Pm10 level (≥70-µg/m3), low wind speed (≤ 2 knots) and high rainfall (≥30mm). Beyond the threshold values, a significantly higher number of emergency admissions due to lower respiratory problems would be expected within the following 2-3 days after the climate shift in the Greater London. The approach will be useful to initiate 'region and disease specific' climate mitigation plans. It will help identify spatial hot spots and the most sensitive areas and population due to climate change, and will eventually lead towards a diversified health warning system tailored to specific climate zones and populations.

Application of job shop scheduling approach in green patient flow optimization using a hybrid swarm intelligence.

With the increasing demand for hospital services amidst the COVID-19 pandemic, allocation of limited public resources and management of healthcare services are of paramount importance. In the field of patient flow scheduling, previous research primarily focused on classical-based objective functions, while ignoring environmental-based objective functions. This study presents a flexible job shop scheduling problem to optimize patient flow and, thereby, minimize the total carbon footprint, as the sustainability-based objective function. Since flexible job shop scheduling is an NP-hard problem, a metaheuristic optimization algorithm, called Chaotic Salp Swarm Algorithm Enhanced with Opposition-Based Learning and Sine Cosine (CSSAOS), was developed. The proposed algorithm integrates the Salp Swarm Algorithm (SSA) with chaotic maps to update the position of followers, the sine cosine algorithm to update the leader position, and opposition-based learning for a better exploration of the search space. generating more accurate solutions. The proposed method was successfully applied in a real-world case study and demonstrated better performance than other well-known metaheuristic algorithms, including differential evolution, genetic algorithm, grasshopper optimization algorithm, SSA based on opposition-based learning, quantum evolutionary SSA, and whale optimization algorithm. In addition, it was found that the proposed method is scalable to different sizes and complexities.

Development and Optimization of a Machine-Learning Prediction Model for Acute Desquamation After Breast Radiation Therapy in the Multicenter REQUITE Cohort.

Purpose: Some patients with breast cancer treated by surgery and radiation therapy experience clinically significant toxicity, which may adversely affect cosmesis and quality of life. There is a paucity of validated clinical prediction models for radiation toxicity. We used machine learning (ML) algorithms to develop and optimise a clinical prediction model for acute breast desquamation after whole breast external beam radiation therapy in the prospective multicenter REQUITE cohort study. Methods and Materials: Using demographic and treatment-related features (m = 122) from patients (n = 2058) at 26 centers, we trained 8 ML algorithms with 10-fold cross-validation in a 50:50 random-split data set with class stratification to predict acute breast desquamation. Based on performance in the validation data set, the logistic model tree, random forest, and naïve Bayes models were taken forward to cost-sensitive learning optimisation. Results: One hundred and ninety-two patients experienced acute desquamation. Resampling and cost-sensitive learning optimisation facilitated an improvement in classification performance. Based on maximising sensitivity (true positives), the "hero" model was the cost-sensitive random forest algorithm with a false-negative: false-positive misclassification penalty of 90:1 containing m = 114 predictive features. Model sensitivity and specificity were 0.77 and 0.66, respectively, with an area under the curve of 0.77 in the validation cohort. Conclusions: ML algorithms with resampling and cost-sensitive learning generated clinically valid prediction models for acute desquamation using patient demographic and treatment features. Further external validation and inclusion of genomic markers in ML prediction models are worthwhile, to identify patients at increased risk of toxicity who may benefit from supportive intervention or even a change in treatment plan.

Measuring and modelling occupancy time in NHS continuing healthcare.

BACKGROUND: Due to increasing demand and financial constraints, NHS continuing healthcare systems seek to find better ways of forecasting demand and budgeting for care. This paper investigates two areas of concern, namely, how long existing patients stay in service and the number of patients that are likely to be still in care after a period of time. METHODS: An anonymised dataset containing information for all funded admissions to placement and home care in the NHS continuing healthcare system was provided by 26 (out of 31) London primary care trusts. The data related to 11289 patients staying in placement and home care between 1 April 2005 and 31 May 2008 were first analysed. Using a methodology based on length of stay (LoS) modelling, we captured the distribution of LoS of patients to estimate the probability of a patient staying in care over a period of time. Using the estimated probabilities we forecasted the number of patients that are likely to be still in care after a period of time (e.g. monthly). RESULTS: We noticed that within the NHS continuing healthcare system there are three main categories of patients. Some patients are discharged after a short stay (few days), some others staying for few months and the third category of patients staying for a long period of time (years). Some variations in proportions of discharge and transition between types of care as well as between care groups (e.g. palliative, functional mental health) were observed. A close agreement of the observed and the expected numbers of patients suggests a good prediction model. CONCLUSIONS: The model was tested for care groups within the NHS continuing healthcare system in London to support Primary Care Trusts in budget planning and improve their responsiveness to meet the increasing demand under limited availability of resources. Its applicability can be extended to other types of care, such as hospital care and re-ablement. Further work will be geared towards updating the dataset and refining the results.

A data science approach for early-stage prediction of Patient's susceptibility to acute side effects of advanced radiotherapy.

The prediction by classification of side effects incidence in a given medical treatment is a common challenge in medical research. Machine Learning (ML) methods are widely used in the areas of risk prediction and classification. The primary objective of such algorithms is to use several features to predict dichotomous responses (e.g., disease positive/negative). Similar to statistical inference modelling, ML modelling is subject to the class imbalance problem and is affected by the majority class, increasing the false-negative rate. In this study, seventy-nine ML models were built and evaluated to classify approximately 2000 participants from 26 hospitals in eight different countries into two groups of radiotherapy (RT) side effects incidence based on recorded observations from the international study of RT related toxicity "REQUITE". We also examined the effect of sampling techniques and cost-sensitive learning methods on the models when dealing with class imbalance. The combinations of such techniques used had a significant impact on the classification. They resulted in an improvement in incidence status prediction by shifting classifiers' attention to the minority group. The best classification model for RT acute toxicity prediction was identified based on domain experts' success criteria. The Area Under Receiver Operator Characteristic curve of the models tested with an isolated dataset ranged from 0.50 to 0.77. The scale of improved results is promising and will guide further development of models to predict RT acute toxicities. One model was optimised and found to be beneficial to identify patients who are at risk of developing acute RT early-stage toxicities as a result of undergoing breast RT ensuring relevant treatment interventions can be appropriately targeted. The design of the approach presented in this paper resulted in producing a preclinical-valid prediction model. The study was developed by a multi-disciplinary collaboration of data scientists, medical physicists, oncologists and surgeons in the UK Radiotherapy Machine Learning Network.

A model-based approach to the analysis of patterns of length of stay in institutional long-term care.

Understanding the pattern of length of stay in institutional long-term care has important practical implications in the management of long-term care. Furthermore, residents' attributes are believed to have significant effects on these patterns. In this paper, we present a model-based approach to extract, from a routinely gathered administrative social care dataset, high-level length-of-stay patterns of residents in long-term care. This approach extends previous work by the authors to incorporate residents' features. Two applications using data provided by a local authority in England are presented to demonstrate the potential use of this approach.

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.

Towards effective capacity planning in a perinatal network centre.

OBJECTIVE: To study the arrival pattern and length of stay (LoS) in a neonatal intensive care/high dependency unit (NICU/HDU) and special care baby unit (SCBU) and the impact of capacity shortage in a perinatal network centre, and to provide an analytical model for improving capacity planning. METHODS: The data used in this study have been collected through the South England Neonatal Database (SEND) and the North Central London Perinatal Network Transfer Audit between 1 January and 31 December 2006 for neonates admitted and refused from the neonatal unit at University College London Hospital (UCLH). Exploratory data analysis was performed. A queuing model is proposed for capacity planning of a perinatal network centre. OUTCOME MEASURES: Predicted number of cots required with existing arrival and discharge patterns; impact of reducing LoS. RESULTS: In 2006, 1002 neonates were admitted to the neonatal unit at UCLH, 144 neonates were refused admission to the NICU and 35 to the SCBU. The model shows the NICU requires seven more cots to accept 90% of neonates into the NICU. The model also shows admission acceptance can be increased by 8% if LoS can be reduced by 2 days. CONCLUSIONS: The arrival, LoS and discharge of neonates having gestational ages of <27 weeks were the key determinants of capacity. The queuing model can be used to determine the cot capacity required for a given tolerance level of admission rejection.

Emergency readmission criterion: a technique for determining the emergency readmission time window.

A frequently chosen time window in defining readmission is 28 days after discharge. Yet in the literature, shorter and longer periods such as 14 days or 90-180 days have also been suggested. In this paper, we develop a modeling approach that systematically tackles the issue surrounding the appropriate choice of a time window as a definition of readmission. The approach is based on the intuitive idea that patients who are discharged from hospital can be broadly divided in to two groups-a group that is at high risk of readmission and a group that is at low risk. Using the national data (England), we demonstrate the usefulness of the approach in the case of chronic obstructive pulmonary disease (COPD), stroke, and congestive heart failure (CHF) patients, which are known to be the leading causes of early readmission. Our findings suggest that there are marked differences in the optimal width of the time window for COPD, stroke, and CHF patients. Furthermore, time windows and the probabilities of being in the high-risk group for COPD, stroke, and CHF patients for each of the 29 acute and specialist trusts in the London area indicate wide variability between hospitals. The novelty of this modeling approach lies in its ability to define an appropriate time window based on evidence objectively derived from operational data. Therefore, it can separately provide a unique approach in examining variability between hospitals, and potentially contribute to a better definition of readmission as a performance indicator.