Giving more insight for automatic risk prediction during pregnancy with interpretable machine learning

Maternal mortality rate (MMR) in Indonesia intercensal population survey (SUPAS) was considered high. For pregnancy risk detection, the public health center (puskesmas) applies a Poedji Rochjati screening card (KSPR) demonstrating 20 features. In addition to KSPR, pregnancy risk monitoring has been assisted with a pregnancy control card. Because of the differences in the number of features between the two control cards, it is necessary to make agreements between them. Our objectives are determining the most influential features, exploring the links among features on the KSPR and pregnancy control cards, and building a machine learning model for predicting pregnancy risk. For the first objective, we use correlation-based feature selection (CFS) and C5.0 algorithm. The next objective was answered by the union operation in the features produced by the two techniques. By performing the machine learning experiment on these features, the accuracy of the XGBoost algorithm demonstrated the hightest results of 94% followed by random forest, Naïve Bayes, and k-Nearest neighbor algorithms, 87%, 66%, and 60% respectively. Interpretability aspects are implemented with SHAP and LIME to provide more insight for classification model. In conclusion, the similarity feature generated in the two interpretation approaches confirmed that Cesar was dominant in determining pregnancy risk

Data Informed Health Simulation Modeling

Combining reliable data with dynamic models can enhance the understanding of health-related phenomena. Smartphone sensor data characterizing discrete states is often suitable for analysis with machine learning classifiers. For dynamic models with continuous states, high-velocity data also serves an important role in model parameterization and calibration. Particle filtering (PF), combined with dynamic models, can support accurate recurrent estimation of continuous system state. This thesis explored these and related ideas with several case studies. The first employed multivariate Hidden Markov models (HMMs) to identify smoking intervals, using time-series of smartphone-based sensor data. Findings demonstrated that multivariate HMMs can achieve notable accuracy in classifying smoking state, with performance being strongly elevated by appropriate data conditioning. Reflecting the advantages of dynamic simulation models, this thesis has contributed two applications of articulated dynamic models: An agent-based model (ABM) of smoking and E-Cigarette use and a hybrid multi-scale model of diabetes in pregnancy (DIP). The ABM of smoking and E-Cigarette use, informed by cross-sectional data, supports investigations of smoking behavior change in light of the influence of social networks and E-Cigarette use. The DIP model was evidenced by both longitudinal and cross-sectional data, and is notable for its use of interwoven ABM, system dynamics (SD), and discrete event simulation elements to explore the interaction of risk factors, coupled dynamics of glycemia regulation, and intervention tradeoffs to address the growing incidence of DIP in the Australia Capital Territory. The final study applied PF with an SD model of mosquito development to estimate the underlying Culex mosquito population using various direct observations, including time series of weather-related factors and mosquito trap counts. The results demonstrate the effectiveness of PF in regrounding the states and evolving model parameters based on incoming observations. Using PF in the context of automated model calibration allows optimization of the values of parameters to markedly reduce model discrepancy. Collectively, the thesis demonstrates how characteristics and availability of data can influence model structure and scope, how dynamic model structure directly affects the ways that data can be used, and how advanced analysis methods for calibration and filtering can enhance model accuracy and versatility

Low Back Pain (LBP)

Low back pain (LBP) is a major public health problem, being the most commonly reported musculoskeletal disorder (MSD) and the leading cause of compromised quality of life and work absenteeism. Indeed, LBP is the leading worldwide cause of years lost to disability, and its burden is growing alongside the increasing and aging population. The etiology, pathogenesis, and occupational risk factors of LBP are still not fully understood. It is crucial to give a stronger focus to reducing the consequences of LBP, as well as preventing its onset. Primary prevention at the occupational level remains important for highly exposed groups. Therefore, it is essential to identify which treatment options and workplace-based intervention strategies are effective in increasing participation at work and encouraging early return-to-work to reduce the consequences of LBP. The present Special Issue offers a unique opportunity to update many of the recent advances and perspectives of this health problem. A number of topics will be covered in order to attract high-quality research papers, including the following major areas: prevalence and epidemiological data, etiology, prevention, assessment and treatment approaches, and health promotion strategies for LBP. We have received a wide range of submissions, including research on the physical, psychosocial, environmental, and occupational perspectives, also focused on workplace interventions

Development and use of methods to estimate chronic disease prevalence in small populations

Introduction National data on the prevalence of chronic diseases on general practice registers is now available. The aim of this PhD was to develop and validate epidemiological models for the expected prevalence of chronic obstructive pulmonary disease (COPD), coronary heart disease (CHD), stroke, hypertension, overall cardiovascular disease (CVD) and high CVD risk at general practice and small area level, and to explore the extent of undiagnosed disease, factors associated with it, and its impact on population health. Methods Multinomial logistic regression models were fitted to pooled Health Survey for England data to derive odds ratios for disease risk factors. These were applied to general practice and small area level population data, split by age, sex, ethnicity, deprivation, rurality and smoking status, to estimate expected disease prevalence at these levels. Validation was carried out using external data, including population-based epidemiological research and case-finding initiatives. Practice-level undiagnosed disease prevalence i.e. expected minus registered disease prevalence, and hospital admission rates for these conditions, were evaluated as outcome indicators of the quality and supply of primary health care services, using ordinary least squares (OLS) regression, geographically-weighted regression (GWR), and other spatial analytic methods. Results Risk factors, odds of disease and expected prevalence were consistent with external data sources. Spatial analysis showed strong evidence of spatial non-stationarity of undiagnosed disease prevalence, with high levels of undiagnosed disease in London and other conurbations, and associations with low supply of primary health care services. Higher hospital admission rates were associated with population deprivation, poorer quality and supply of primary health care services and poorer access to them, and for COPD, with higher levels of undiagnosed disease. Conclusion The epidemiologic prevalence models have been implemented in national data sources such as NHS Comparators, the Association of Public Health Observatories website, and a number of national reports. Early experience suggests that they are useful for guiding case-finding at practice level and improving and regulating the quality of primary health care. Comparisons with external data, in particular prevalence of disease detected by general practices, suggest that model predictions are valid. Practice-level spatial analyses of undiagnosed disease prevalence and hospital admission rates failed to demonstrate superiority of GWR over OLS methods. Disease modellers should be encouraged to collaborate more effectively, and to validate and compare modelling methods using an agreed framework. National leadership is needed to further develop and implement disease models. It is likely that prevalence models will prove to be most useful for identifying undiagnosed diseases with a slow and insidious onset, such as COPD, diabetes and hypertension

Epilepsy

With the vision of including authors from different parts of the world, different educational backgrounds, and offering open-access to their published work, InTech proudly presents the latest edited book in epilepsy research, Epilepsy: Histological, electroencephalographic, and psychological aspects. Here are twelve interesting and inspiring chapters dealing with basic molecular and cellular mechanisms underlying epileptic seizures, electroencephalographic findings, and neuropsychological, psychological, and psychiatric aspects of epileptic seizures, but non-epileptic as well

Untangling hotel industry’s inefficiency: An SFA approach applied to a renowned Portuguese hotel chain

The present paper explores the technical efficiency of four hotels from Teixeira Duarte Group - a renowned Portuguese hotel chain. An efficiency ranking is established from these four hotel units located in Portugal using Stochastic Frontier Analysis. This methodology allows to discriminate between measurement error and systematic inefficiencies in the estimation process enabling to investigate the main inefficiency causes. Several suggestions concerning efficiency improvement are undertaken for each hotel studied.info:eu-repo/semantics/publishedVersio