Preventive Healthcare: A Neural Network Analysis of Behavioral Habits and Chronic Diseases

The research aims to explore the association between behavioral habits and chronic diseases, and to identify a portfolio of risk factors for preventive healthcare. The data is taken from the Behavioral Risk Factor Surveillance System (BRFSS) database of the Centers for Disease Control and Prevention, for the year 2012. Using SPSS Modeler, we deploy neural networks to identify strong positive and negative associations between certain chronic diseases and behavioral habits. The data for 475,687 records from BRFS database included behavioral habit variables of consumption of soda and fruits/vegetables, alcohol, smoking, weekly working hours, and exercise; chronic disease variables of heart attack, stroke, asthma, and diabetes; and demographic variables of marital status, income, and age. Our findings indicate that with chronic conditions, behavioral habits of physical activity and fruit and vegetable consumption are negatively associated; soda, alcohol, and smoking are positively associated; and income and age are positively associated. We contribute to individual and national preventive healthcare by offering a portfolio of significant behavioral risk factors that enable individuals to make lifestyle changes and governments to frame campaigns and policies countering chronic conditions and promoting public health

Trajectory Data Mining in Mouse Models of Stroke

Contains fulltext : 273912.pdf (Publisher’s version ) (Open Access)Radboud University, 04 oktober 2022Promotor : Kiliaan, A.J. Co-promotor : Wiesmann, M.167 p

Computational neurorehabilitation: modeling plasticity and learning to predict recovery

Despite progress in using computational approaches to inform medicine and neuroscience in the last 30 years, there have been few attempts to model the mechanisms underlying sensorimotor rehabilitation. We argue that a fundamental understanding of neurologic recovery, and as a result accurate predictions at the individual level, will be facilitated by developing computational models of the salient neural processes, including plasticity and learning systems of the brain, and integrating them into a context specific to rehabilitation. Here, we therefore discuss Computational Neurorehabilitation, a newly emerging field aimed at modeling plasticity and motor learning to understand and improve movement recovery of individuals with neurologic impairment. We first explain how the emergence of robotics and wearable sensors for rehabilitation is providing data that make development and testing of such models increasingly feasible. We then review key aspects of plasticity and motor learning that such models will incorporate. We proceed by discussing how computational neurorehabilitation models relate to the current benchmark in rehabilitation modeling – regression-based, prognostic modeling. We then critically discuss the first computational neurorehabilitation models, which have primarily focused on modeling rehabilitation of the upper extremity after stroke, and show how even simple models have produced novel ideas for future investigation. Finally, we conclude with key directions for future research, anticipating that soon we will see the emergence of mechanistic models of motor recovery that are informed by clinical imaging results and driven by the actual movement content of rehabilitation therapy as well as wearable sensor-based records of daily activity

Recurrent Stroke Prediction using Machine Learning Algorithms with Clinical Public Datasets: An Empirical Performance Evaluation

غالبًا ما تكون السكتة الدماغية المتكررة مدمرة وقادرة على التسبب في إعاقة شديدة أو الوفاة. ومع ذلك ، فإن ما يقرب من 90 ٪ من أسباب السكتة الدماغية المتكررة قابلة للتغير ، مما يعني أنه يمكن تجنب السكتات الدماغية المتكررة عن طريق التحكم في عوامل الخطر ، والتي هي في الأساس سلوكية واستقلابية بطبيعتها. وبالتالي ، يتضح من الأعمال السابقة أن نموذج التنبؤ بالسكتة الدماغية المتكررة يمكن أن يساعد في تقليل احتمالية الإصابة بسكتة دماغية متكررة. أظهرت الأعمال السابقة نتائج واعدة في التنبؤ بحالات السكتة الدماغية لأول مرة باستخدام أساليب التعلم الآلي. ومع ذلك ، هناك أعمال محدودة للتنبؤ بالسكتة الدماغية المتكررة باستخدام أساليب التعلم الآلي. ومن ثم ، تم اقتراح هذا العمل لإجراء تحليل تجريبي والتحقيق في خوارزميات التعلم الآلي المطبقة في نماذج التنبؤ بالسكتة الدماغية المتكررة. يهدف هذا البحث إلى التحقيق في أداء خوارزميات التعلم الآلي ومقارنتها باستخدام مجموعات البيانات السريرية العامة للسكتة الدماغية المتكررة. في هذه الدراسة ، تم استخدام الشبكة العصبية الاصطناعية (ANN) وآلة المتجهات الداعمة (SVM) وقائمة قواعد بايزي (BRL) ومقارنة أدائها في مجال نموذج التنبؤ بالسكتة الدماغية المتكررة. تظهر نتيجة التجارب التجريبية أن ANN سجلت أعلى دقة عند 80.00٪ ، تليها BRL بنسبة 75.91٪ و SVM بنسبة 60.45٪.Recurrent strokes can be devastating, often resulting in severe disability or death. However, nearly 90% of the causes of recurrent stroke are modifiable, which means recurrent strokes can be averted by controlling risk factors, which are mainly behavioral and metabolic in nature. Thus, it shows that from the previous works that recurrent stroke prediction model could help in minimizing the possibility of getting recurrent stroke. Previous works have shown promising results in predicting first-time stroke cases with machine learning approaches. However, there are limited works on recurrent stroke prediction using machine learning methods. Hence, this work is proposed to perform an empirical analysis and to investigate machine learning algorithms implementation in the recurrent stroke prediction models. This research aims to investigate and compare the performance of machine learning algorithms using recurrent stroke clinical public datasets. In this study, Artificial Neural Network (ANN), Support Vector Machine (SVM) and Bayesian Rule List (BRL) are used and compared their performance in the domain of recurrent stroke prediction model. The result of the empirical experiments shows that ANN scores the highest accuracy at 80.00%, follows by BRL with 75.91% and SVM with 60.45%

Social and behavioral determinants of health in the era of artificial intelligence with electronic health records: A scoping review

Background: There is growing evidence that social and behavioral determinants of health (SBDH) play a substantial effect in a wide range of health outcomes. Electronic health records (EHRs) have been widely employed to conduct observational studies in the age of artificial intelligence (AI). However, there has been little research into how to make the most of SBDH information from EHRs. Methods: A systematic search was conducted in six databases to find relevant peer-reviewed publications that had recently been published. Relevance was determined by screening and evaluating the articles. Based on selected relevant studies, a methodological analysis of AI algorithms leveraging SBDH information in EHR data was provided. Results: Our synthesis was driven by an analysis of SBDH categories, the relationship between SBDH and healthcare-related statuses, and several NLP approaches for extracting SDOH from clinical literature. Discussion: The associations between SBDH and health outcomes are complicated and diverse; several pathways may be involved. Using Natural Language Processing (NLP) technology to support the extraction of SBDH and other clinical ideas simplifies the identification and extraction of essential concepts from clinical data, efficiently unlocks unstructured data, and aids in the resolution of unstructured data-related issues. Conclusion: Despite known associations between SBDH and disease, SBDH factors are rarely investigated as interventions to improve patient outcomes. Gaining knowledge about SBDH and how SBDH data can be collected from EHRs using NLP approaches and predictive models improves the chances of influencing health policy change for patient wellness, and ultimately promoting health and health equity. Keywords: Social and Behavioral Determinants of Health, Artificial Intelligence, Electronic Health Records, Natural Language Processing, Predictive ModelComment: 32 pages, 5 figure

Health State Estimation

Life's most valuable asset is health. Continuously understanding the state of our health and modeling how it evolves is essential if we wish to improve it. Given the opportunity that people live with more data about their life today than any other time in history, the challenge rests in interweaving this data with the growing body of knowledge to compute and model the health state of an individual continually. This dissertation presents an approach to build a personal model and dynamically estimate the health state of an individual by fusing multi-modal data and domain knowledge. The system is stitched together from four essential abstraction elements: 1. the events in our life, 2. the layers of our biological systems (from molecular to an organism), 3. the functional utilities that arise from biological underpinnings, and 4. how we interact with these utilities in the reality of daily life. Connecting these four elements via graph network blocks forms the backbone by which we instantiate a digital twin of an individual. Edges and nodes in this graph structure are then regularly updated with learning techniques as data is continuously digested. Experiments demonstrate the use of dense and heterogeneous real-world data from a variety of personal and environmental sensors to monitor individual cardiovascular health state. State estimation and individual modeling is the fundamental basis to depart from disease-oriented approaches to a total health continuum paradigm. Precision in predicting health requires understanding state trajectory. By encasing this estimation within a navigational approach, a systematic guidance framework can plan actions to transition a current state towards a desired one. This work concludes by presenting this framework of combining the health state and personal graph model to perpetually plan and assist us in living life towards our goals.Comment: Ph.D. Dissertation @ University of California, Irvin

Doctor of Philosophy

dissertationFamily health history (FHH) is an independent risk factor for predicting an individual's chance of developing selected chronic diseases. Though various FHH tools have been developed, many research questions remain to be addressed. Before FHH can be used as an effective risk assessment tool in public health screenings or population-based research, it is important to understand the quality of collected data and evaluate risk prediction models. No literature has been identified whereby risks are predicted by applying machine learning solely on FHH. This dissertation addressed several questions. First, using mixed methods, we defined 50 requirements for documenting FHH for a population-based study. Second, we examined the accuracy of self- and proxy-reported FHH data in the Health Family Tree database, by comparing the disease and risk factor rates generated from this database with rates recorded in a cancer registry and standard public health surveys. The rates generated from the Health Family Tree were statistically lower than those from public sources (exceptions: stroke rates were the same, exercise rates were higher). Third, we validated the Health Family Tree risk predictive algorithm. The very high risk (≥2) predicted the risk of all concerned diseases for adult population (20 ~ 99 years of age), and the predictability remained when using disease rates from public sources as the reference in the relative risk model. The referent population used to establish the expected rate of disease impacted risk classification: the lower expected disease rates generated by the Health Family Tree, in comparison to the rates from public iv sources, caused more persons to be classified at high risk. Finally, we constructed and evaluated new predictive models using three machine learning classifiers (logistic regression, Bayesian networks, and support vector machine). A limited set of information about first-degree relatives was used to predict future disease. In summary, combining FHH with valid risk algorithms provide a low cost tool for identifying persons at risk for common diseases. These findings may be especially useful when developing strategies to screen populations for common diseases and identifying those at highest risk for public health interventions or population-based research

Predictive modelling for health and health-care utilisation : an observational study for Australians aged 45 and up

The burden of chronic disease is growing at a fast pace, leading to poor quality of life and high healthcare expenditures in a large portion of the Australian population. Much of the burden is borne by hospitals, and therefore there is an ever-increasing interest in preventative interventions that can keep people out of hospitals and healthier for longer periods. There is a wide range of potential interventions that may be able to achieve this goal, and policy makers need to decide which one should be funded and implemented. This task is difficult for two reasons: first it is often not clear what is the short-term effectiveness of an intervention, and how it varies in specific sub-populations, and second it is also not clear what the long-term intended and unintended consequences might be. In this thesis I make contributions to address both these difficulties. On the short-term side I focus on the use of physical activity to prevent the development of chronic disease and to reduce hospital costs. Increasing physical activity has been long heralded as a way to achieve these goals but evidence of its effectiveness has been elusive. In this thesis I provide data driven evidence to justify policies that encourage higher levels of physical activity (PA) in middle age and older Australian population. I use data from the “45 and up” and the Social, Economic and Environmental Factors (SEEF) study, linked with the Admitted Patient Data Collection (APDC), to identify and study the cost and health trajectories of individuals with different levels of physical activity. The results show a clear statistically significant association between PA and lower hospitalisation cost, as well as between PA and reduced risk of heart disease, diabetes and stroke. On the long-term side of the analysis, I placed this thesis in the context of a larger program of work performed at Western Sydney University that aims to build a microsimulation model for the analysis of health policy interventions. In this framework I studied predictive models that use survey and/or administrative data to predict hospital costs and resource utilisation. I placed particular emphasis on the application of methods borrowed from Natural Language Processing to understand how to use the thousands of diagnosis and procedure codes found in administrative data as input to predictive models. The methods developed in this thesis go beyond the application to hospital data and can be used in any predictive model that relies on complex coding of healthcare information

Risk factors affecting the ability for earned value management to accurately assess the performance of infrastructure projects in Australia

Purpose – The purpose of this paper is to investigate a set of risk-related factors influencing the earned value management (EVM) concept as an assessment technique in evaluating the progress of modern sustainable infrastructure construction projects. Design/methodology/approach – A qualitative research approach has been adopted for identifying risk-related factors influencing EVM concept from a literature review and through interviewing industry personnel, followed by an inductive process to form sets of key factors and their measuring items. Findings – EVM is a common method for assessing project performance. A weakness of this approach is that EVM assessment in its current form does not measure the impact of a number of project performance factors that result from the complexity of modern infrastructure construction projects, and thus does not accurately assess their impact in this performance. This paper discusses and explains a range of potential risk factors to evaluating project performance such as sustainability, stakeholder requirements, communication, procurement strategy, weather, experience of staff, site condition, design issues, financial risk, subcontractor, government requirements and material. In addition, their measuring items were identified. Practical implications – This research assists projects managers to improve the evaluation process of infrastructure construction performance by incorporating a range of factors likely to impact on that performance and which are not included in current EVM calculations. Originality/value – This research addresses the need to include in the EVM calculation a range of risk factors affecting the performance of infrastructure projects in Australia and therefore makes this calculation a more reliable tool for assessing project performance

Prognosis Prediction of Stroke based on Machine Learning and Explanation Model

The prognosis prediction of stroke is of great significance to its prevention and treatment. This paper used machine learning to predict stroke prognosis, and use SHAP method to make feature importance and single sample analysis. Firstly, feature engineering, use Borderline-SMOTE algorithm to deal with data imbalance, use Support Vector Machine(SVM) to build a prognostic prediction model, and use Random Forest(RF), Decision Tree(DT), Logistic Regression(LR) for comparative analysis, and find the performance of SVM after feature engineering better than other models, the accuracy, specificity, F1 score, AUC value reach 0.8306, 0.8356, 0.8415 and 0.9140. Then, the model was further analyzed for explainability, and it was found that the top three causes of the disease were Glasgow Coma Score, NIHSS and atrial fibrillation. Finally, try to analysis a single sample, which is performed to determine that the patient is a low-risk patient, and suffering from atrial fibrillation is the largest potential risk factor for the patient