Bayes pulmonary embolism assisted-diagnosis: a new expert system for clinical use

Background: The diagnosis of pulmonary embolism demands flexible decision models, both for the presence of clinical confounders and for the variability of local diagnostic resources. As Bayesian networks fully meet this requirement, Bayes Pulmonary embolism Assisted Diagnosis (BayPAD), a probabilistic expert systems focused on pulmonary embolism, was developed. Methods: To quantitatively validate and improve BayPAD, the system was applied to 750 patients from a prospective study done in an Italian tertiary hospital where the true pulmonary embolism status was confirmed using pulmonary angiography or ruled out with a lung scan. The proportion of correct diagnoses made by BayPAD (accuracy) and the correctness of the pulmonary embolism probabilities predicted by the model (calibration) were calculated. The calibration was evaluated according to the Cox regression-calibration model. Results: Before refining the model, accuracy was 88.6%. Once refined, accuracy was 97.2% and 98%, respectively, in the training and validation samples. According to Cox analysis, calibration was satisfactory, despite a tendency to exaggerate the effect of the findings on the probability of pulmonary embolism. The lack of some investigations (like Spiral computed tomographic scan and Lower limbs doppler ultrasounds) in the pool of available data often prevents BayPAD from reaching the diagnosis without invasive procedures. Conclusions: BayPAD offers clinicians a flexible and accurate strategy to diagnose pulmonary embolism. Simple to use, the system performs case-based reasoning to optimise the use of resources available within a particular hospital. Bayesian networks are expected to have a prominent role in the clinical management of complex diagnostic problems in the near future

Relationship between employability and graduate’s competencies based on programme learning outcomes analysis

Graduate employability pertains to issues that are related to the person’s character or quality of being employable such as the knowledge and skills they possess to the labour market is a crucial issue in higher education institutions (HEIs). Accordingly, in line with the aspirations and mission of the National Graduate Employability Blueprint Malaysia 2012-2017, they expect to produce competent graduates with 75% of the graduates working in their related fields within six months after graduation. Throughout the literature, tracer study is commonly used and has been adopted by the Ministry of Higher Education to trace graduate employability (GE) information and evaluation on study programmes have helped in improving the transition of graduates from education to the labour market. However, two issues arise;- (1) a lack in predictive capability, and (2) the lack of inclusive graduate data where data and analysis from a tracer study have not been well communicate to other stakeholders. Furthermore, there have been little discussion on predicting the duration of a graduate’s employment after graduation. Therefore, this study intends to investigate the relationship between employability duration and the graduate’s competencies based on programme learning outcomes (PLO) among Computer Science or IT engineering domain. The outcome-based education (OBE) contributes to the learning outcomes attainment which is the PLO that helps the learners to succeed especially in professional life and education. Thus, this study used a modified version of the predictive analytic process that started with problem definition and obtained a clean dataset before the model formulation and evaluation took place. There are two data sources that have been used in this study, institutional academic database (PTMK UMP) and an online feedback from the graduates. This study received 47 responses out of 164 graduates from 2014/2015 Faculty of Computing (FK) batch, with a response rate of 29%. A simple linear regression was used to measure the correlation between the category of PLO and the duration of graduate to get employed as well as to formulate the prediction model. The findings from this study found that PLO6 (problem solving and scientific skills) was the most sensitive PLO on the duration for a graduate to get employed (r = -0.2515, p = 0.0882, p < 0.25, N = 47). Thus, the model was formulated based on the linear equation of PLO6 which is Duration = -9.549x + 73.497. This prediction model was validated through error rate analysis with acceptable result and evaluated by error rate frequency analysis. The evaluation through ranking method based on the frequency analysis of error rate also found that PLO6 was at the first rank followed by PLO3, PLO1, PLO4, PLO5, PLO2, PLO8, PLO7. This study reported the potential of outcome-based education data to predict graduate employability performance within the time frame (six months) as determined by the Ministry of Higher Education. With prediction capacity from the formulated model, more intervention programme can be strategically planned to assure that graduates can be employed in time and in-field

Protein function and inhibitor prediction by statistical learning approach

Ph.DDOCTOR OF PHILOSOPH