Detecting Outliers for Improving the Quality of Incident Duration Prediction

To circumvent the needs of domain expertise and the excessive data for developing a knowledge-based prediction system such as the I-95 incident duration estimation model, this study has developed an efficient transferability analysis method to assess the applicability of adopting the prediction rules from an existing well-developed model to a different highway. The proposed analysis method has considered the common nature of incident response operations and local-specific incident characteristics in assessing the transferability of available knowledge-based rules for estimating the required clearance duration of different types of incidents. Evaluation of the proposed method with the I-695 incident records clearly shows that the prediction model developed with such an effective transferring method can achieve the same level of performance as with the original rule-searching and refinement method.Since most incident records for model development are collected on-line during the emergency incident response process, some of the key data are likely to be misrecorded which inevitably causes many existing models to yield undesirable performance, especially with respect to those incidents with insufficient records or excessive long duration. As such, this study has also developed a two-phase outlier detection process for identifying outliers and removing those viewed as faulty records from the dataset for model calibration and model evaluation. Using the I-695 incident records for a case study, the resulting performance of the proposed two-phase outlier detection process has proved its promising property for filtering faculty data from the incident records prior to the use for model development

Cardiovascular Disease in Inflammatory Joint Disorders:The interplay between risk factors, inflammation and therapy

Scope of this thesis In this thesis the cardiovascular disease (CVD) risk in inflammatory joint diseases (IJD) was investigated, focusing on rheumatoid arthritis (RA) and psoriatic arthritis (PsA). We aimed to create awareness of this high CVD risk by investigating the magnitude of CVD prevalence in IJD patients over the last decades, also considering new treatment modalities such as biological disease modifying antirheumatic drugs (bDMARD) and imaging techniques such as 18-fluorodeoxyglucose positron emission tomography combined with computed tomography (18F-FDG-PET/CT). In addition, traditional and novel risk factors for CVD and the effects of anti-inflammatory therapy on these risk factors were assessed. Lastly, we revised new EULAR recommendations for CVD risk management in IJD and proposed ideas for future research. Conclusion Atherosclerosis is an inflammatory process, which is further enhanced by the chronic inflammation inherent to IJD. This has implications for health care professionals, especially rheumatologists and cardiologists, but IJD patients should also be aware of their increased CVD risk and take timely precautions. Acknowledging this high risk is an important step preceding the implementation of strategies for prediction, prevention and management of CVD in IJD. There lies an evidence gap which needs to be filled in the future and general practice guidelines need to be developed to reduce CVD risk in these patients

Dynamic and Interpretable Hazard-Based Models of Traffic Incident Durations

Understanding and predicting the duration or “return-to-normal” time of traffic incidents is important for system-level management and optimization of road transportation networks. Increasing real-time availability of multiple data sources characterizing the state of urban traffic networks, together with advances in machine learning offer the opportunity for new and improved approaches to this problem that go beyond static statistical analyses of incident duration. In this paper we consider two such improvements: dynamic update of incident duration predictions as new information about incidents becomes available and automated interpretation of the factors responsible for these predictions. For our use case, we take one year of incident data and traffic state time-series data from the M25 motorway in London. We use it to train models that predict the probability distribution of incident durations, utilizing both time-invariant and time-varying features of the data. The latter allow predictions to be updated as an incident progresses, and more information becomes available. For dynamic predictions, time-series features are fed into the Match-Net algorithm, a temporal convolutional hitting-time network, recently developed for dynamical survival analysis in clinical applications. The predictions are benchmarked against static regression models for survival analysis and against an established dynamic technique known as landmarking and found to perform favourably by several standard comparison measures. To provide interpretability, we utilize the concept of Shapley values recently developed in the domain of interpretable artificial intelligence to rank the features most relevant to the model predictions at different time horizons. For example, the time of day is always a significantly influential time-invariant feature, whereas the time-series features strongly influence predictions at 5 and 60-min horizons. Although we focus here on traffic incidents, the methodology we describe can be applied to many survival analysis problems where time-series data is to be combined with time-invariant features

Developing clinical prediction models for diabetes classification and progression

Patients with type 1 and type 2 diabetes have very different treatment and care requirements. Overlapping phenotypes and lack of clear classification guidelines make it difficult for clinicians to differentiate between type 1 and type 2 diabetes at diagnosis. The rate of glycaemic deterioration is highly variable in patients with type 2 diabetes but there is no single test to accurately identify which patients will progress rapidly to requiring insulin therapy. Incorrect treatment and care decisions in diabetes can have life-threatening consequences. The aim of this thesis is to develop clinical prediction models that can be incorporated into routine clinical practice to assist clinicians with the classification and care of patient diagnosed with diabetes. We addressed the problem first by integrating features previously associated with classification of type 1 and type 2 diabetes to develop a diagnostic model using logistic regression to identify, at diagnosis, patients with type 1 diabetes. The high performance achieved by this model was comparable to that of machine learning algorithms. In patients diagnosed with type 2 diabetes, we found that patients who were GADA positive and had genetic susceptibility to type 1 diabetes progressed more rapidly to requiring insulin therapy. We built upon this finding to develop a prognostic model integrating predictive features of glycaemic deterioration to predict early insulin requirement in adults diagnosed with type 2 diabetes. The three main findings of this thesis have the potential to change the way that patients with diabetes are managed in clinical practice. Use of the diagnostic model developed to identify patients with type 1 diabetes has the potential to reduce misclassification. Classifying patients according to the model has the benefit of being more akin to the treatment needs of the patient rather than the aetiopathological definitions used in current clinical guidelines. The design of the model lends itself to implementing a triage-based approach to diabetes subtype diagnosis. Our second main finding alters the clinical implications of a positive GADA test in patients diagnosed with type 2 diabetes. For identifying patients likely to progress rapidly to insulin, genetic testing is only beneficial in patients who test positive for GADA. In clinical practice, a two-step screening process could be implemented - only patients who test positive for GADA in the first step would go on for genetic testing. The prognostic model can be used in clinical practice to predict a patient’s rate of glycaemic deterioration leading to a requirement for insulin. The availability of this data will enable clinical practices to more effectively manage their patient lists, prioritising more intensive follow up for those patients who are at high risk of rapid progression. Patients are likely to benefit from tailored treatment. Another key clinical use of the prognostic model is the identification of patients who would benefit most from GADA testing saving both inconvenience to the patient and a cost-benefit to the health service

The dynamic prediction of company failure - the influence of time, the economy and non-linearity

Dynamic forecasts of financial distress have received far less attention than static forecasts, particularly in Australia. This thesis, therefore, investigates dynamic probability forecasts for Australian firms. Novel features of the modelling are the use of time-varying variables in forecasts from a Cox model and allowing for nonlinearity between financial distress and predictor variables. Cox regression models with time-varying variables are used to estimate the survival probabilities of a large sample of Australian listed companies. Not only is this one of relatively few studies to apply dynamic variables in forecasting financial distress, but to the author’s knowledge it is the first to provide forecasts of survival probabilities using the Cox model with time-varying variables. Forecast accuracy is evaluated using receiver operating characteristics curves and the Brier Score. It was found that the models had predictive power in out-of-sample forecast. Allowing for non-linearity between the predictor variables and financial distress risk substantially improved out-of-sample accuracy in discriminating between distressed and nondistressed firms. However, variables capturing the state of the economy did not substantively improve the predictive power of the model

Epigenetic scores for the circulating proteome as tools for disease prediction

Protein biomarkers have been identified across many age-related morbidities. However, characterising epigenetic influences could further inform disease predictions. Here, we leverage epigenome-wide data to study links between the DNA methylation (DNAm) signatures of the circulating proteome and incident diseases. Using data from four cohorts, we trained and tested epigenetic scores (EpiScores) for 953 plasma proteins, identifying 109 scores that explained between 1% and 58% of the variance in protein levels after adjusting for known protein quantitative trait loci (pQTL) genetic effects. By projecting these EpiScores into an independent sample (Generation Scotland; n = 9537) and relating them to incident morbidities over a follow-up of 14 years, we uncovered 137 EpiScore-disease associations. These associations were largely independent of immune cell proportions, common lifestyle and health factors, and biological aging. Notably, we found that our diabetes-associated EpiScores highlighted previous top biomarker associations from proteome-wide assessments of diabetes. These EpiScores for protein levels can therefore be a valuable resource for disease prediction and risk stratification

New Framework and Decision Support Tool to Warrant Detour Operations During Freeway Corridor Incident Management

As reported in the literature, the mobility and reliability of the highway systems in the United States have been significantly undermined by traffic delays on freeway corridors due to non-recurrent traffic congestion. Many of those delays are caused by the reduced capacity and overwhelming demand on critical metropolitan corridors coupled with long incident durations. In most scenarios, if proper detour strategies could be implemented in time, motorists could circumvent the congested segments by detouring through parallel arterials, which will significantly improve the mobility of all vehicles in the corridor system. Nevertheless, prior to implementation of any detour strategy, traffic managers need a set of well-justified warrants, as implementing detour operations usually demand substantial amount of resources and manpower. To contend with the aforementioned issues, this study is focused on developing a new multi-criteria framework along with an advanced and computation-friendly tool for traffic managers to decide whether or not and when to implement corridor detour operations. The expected contributions of this study are: * Proposing a well-calibrated corridor simulation network and a comprehensive set of experimental scenarios to take into account many potential affecting factors on traffic manager\u27s decision making process and ensure the effectiveness of the proposed detour warrant tool; * Developing detour decision models, including a two-choice model and a multi-choice model, based on generated optima detour traffic flow rates for each scenario from a diversion control model to allow responsible traffic managers to make best detour decisions during real-time incident management; and * Estimating the resulting benefits for comparison with the operational costs using the output from the diversion control model to further validate the developed detour decision model from the overall societal perspective

Predicting Pilot Misperception of Runway Excursion Risk Through Machine Learning Algorithms of Recorded Flight Data

The research used predictive models to determine pilot misperception of runway excursion risk associated with unstable approaches. The Federal Aviation Administration defined runway excursion as a veer-off or overrun of the runway surface. The Federal Aviation Administration also defined a stable approach as an aircraft meeting the following criteria: (a) on target approach airspeed, (b) correct attitude, (c) landing configuration, (d) nominal descent angle/rate, and (e) on a straight flight path to the runway touchdown zone. Continuing an unstable approach to landing was defined as Unstable Approach Risk Misperception in this research. A review of the literature revealed that an unstable approach followed by the failure to execute a rejected landing was a common contributing factor in runway excursions. Flight Data Recorder data were archived and made available by the National Aeronautics and Space Administration for public use. These data were collected over a four-year period from the flight data recorders of a fleet of 35 regional jets operating in the National Airspace System. The archived data were processed and explored for evidence of unstable approaches and to determine whether or not a rejected landing was executed. Once identified, those data revealing evidence of unstable approaches were processed for the purposes of building predictive models. SAS™ Enterprise MinerR was used to explore the data, as well as to build and assess predictive models. The advanced machine learning algorithms utilized included: (a) support vector machine, (b) random forest, (c) gradient boosting, (d) decision tree, (e) logistic regression, and (f) neural network. The models were evaluated and compared to determine the best prediction model. Based on the model comparison, the decision tree model was determined to have the highest predictive value. The Flight Data Recorder data were then analyzed to determine predictive accuracy of the target variable and to determine important predictors of the target variable, Unstable Approach Risk Misperception. Results of the study indicated that the predictive accuracy of the best performing model, decision tree, was 99%. Findings indicated that six variables stood out in the prediction of Unstable Approach Risk Misperception: (1) glideslope deviation, (2) selected approach speed deviation (3) localizer deviation, (4) flaps not extended, (5) drift angle, and (6) approach speed deviation. These variables were listed in order of importance based on results of the decision tree predictive model analysis. The results of the study are of interest to aviation researchers as well as airline pilot training managers. It is suggested that the ability to predict the probability of pilot misperception of runway excursion risk could influence the development of new pilot simulator training scenarios and strategies. The research aids avionics providers in the development of predictive runway excursion alerting display technologies

CAROTID ARTERY INTIMA-MEDIA THICKNESS AND 10-YEAR RISK OF HEART DISEASE IN DIABETIC PATIENTS: A COMPARATIVE STUDY

Objective: The aim of the present study was to assess the factors affecting carotid artery intima-media thickness (CIMT) and 10-year risk of heart disease in diabetic patients classified according to CIMT. Methods: This was an analytical cross-sectional study conducted on 92 patients for 1 year. 10-year risk of heart disease was calculated using the American College of Cardiology/American Heart Association Guideline on the Assessment of Cardiovascular Risk. Based on CIMT, the subjects were classified into two groups. Group 1 contains subjects with CIMT <0.9 and Group 2 contains subjects with CIMT ≥0.9. The Mann–Whitney U-test, Pearson’s correlation, and descriptive statistics were used to compare and describe the data. The level of statistical significance was taken at p<0.05. Results: Patients with 51–60 years of age group are high in number. Males were predominantly high than their counterparts. There is a statistically significant association between total cholesterol (p=0.001), high-density lipoproteins (p=0.000), low-density lipoproteins (p=0.001), postprandial blood sugar (p=0.000), and hemoglobin 1Ac (p=0.035) with CIMT. The mean 10-year risk of heart disease in Groups 1 and 2 is 13.13±15.40 and 23.63±17.57, respectively. There is statistically highly significant association (p=0.000) of 10-year risk of heart disease between two groups. There is a positive correlation (r=0.45, p<0.0001) between CIMT and risk of heart disease. Conclusion: Our study found that greater the CIMT, greater the risk of the heart of disease