Warfarin Dose Estimation on High-dimensional and Incomplete Data

Warfarin is a widely used oral anticoagulant worldwide. However, due to the complex relationship between individual factors, it is challenging to estimate the optimal warfarin dose to give full play to its ideal efficacy. Currently, there are plenty of studies using machine learning or deep learning techniques to help with the optimal warfarin dose selection. But few of them can resolve missing values and high-dimensional data naturally, that are two main concerns when analyzing clinical real world data. In this work, we propose to regard each patient’s record as a set of observed individual factors, and represent them in an embedding space, that enables our method can learn from the incomplete date directly and avoid the negative impact from the high-dimensional feature set. Then, a novel neural network is proposed to combine the set of embedded vectors non-linearly, that are capable of capturing their correlations and locating the informative ones for prediction. After comparing with the baseline models on the open source data from International Warfarin Pharmacogenetics Consortium, the experimental results demonstrate that our proposed method outperform others by a significant margin. After further analyzing the model performance in different dosing subgroups, we can conclude that the proposed method has the high application value in clinical, especially for the patients in high-dose and medium-dose subgroups

Logic Synthesis as an Efficient Means of Minimal Model Discovery from Multivariable Medical Datasets

In this paper we review the application of logic synthesis methods for uncovering minimal structures in observational/medical datasets. Traditionally used in digital circuit design, logic synthesis has taken major strides in the past few decades and forms the foundation of some of the most powerful concepts in computer science and data mining. Here we provide a review of current state of research in application of logic synthesis methods for data analysis and provide a demonstrative example for systematic application and reasoning based on these methods

Configurational Approach to Identify Concept Networks in selected Clinical Safety Incident Classes

Classifying clinical safety incidents (CSI) in their correct classes depends on the multiple concepts used to describe them. Machine learning based classification case study presented in this paper shows that it fails to identify the underlying complex concepts associations between the CSI classes. Two pairs of classes, each having high and low confused classes (as determined by the classifier), were further investigated by applying the set-theoretic-based logical synthesis methodology. The aim is to identify the relationships between concept networks for selected classes. The concept networks were identified using a set of 117 terms and measures taken included degree-centrality and in-betweenness centrality. In this study, using deterministic configurational approach, it is feasible to draw a meaningful relationship between concepts using the complex medical dataset sourced from the Incident Information Management System. The study is proof of concept that it is possible to identify concept networks and concept configuration rules for CSI classes

Does project governance lead to successful projects?

Project governance can potentially provide the top management support needed for projects to succeed. However, the awareness and adoption of project governance guidelines has been sporadic. This research seeks to overcome any credibility gaps that may exist by developing a theoretical model to explain why project governance should work and testing the model against industry data. The research found five of six theoretical constructs for project governance correlated significantly with project success and different constructs were more important at different at different stages in the project lifecycle. The contribution of the research is firstly to show project governance can be explained by agency theory and theories of planned change. The second major contribution is to provide evidence that project governance leads to project success

The Economic Contribution of Software: An Alternative Perspective on the Productivity Paradox

The complex relationships between information technology investments and business value have been the focus of intensive research in recent years. There appears to be a discernible trend toward a more nuanced view in which the differential effects of the various categories of IT capital such as hardware, software, and their interactions with organizational factors are systematically investigated. As well, there is emerging evidence of accelerating investments in software and a greater shift toward “softwarization” (Langdon 2003) in which value addition is linked to combining flexible software with increasingly commoditized hardware. In this paper, we focus on the differential contributions of hardware and software capital and their interactions with labor capital. We use industry-level data to extend previous studies in three ways: (1) by using more recent data (1990 to 2002), (2) by focusing on IT-using industries in the private sector, and (3) by treating IT hardware and IT software as two distinct classes of IT capital. We adopt the commonly used log-linear Cobb Douglas production function approach. Our findings indicate that the impacts of software are significantly different in comparison with hardware and that the productivity benefits attributable to IT are largely due to the interactions between software and labor inputs. We conclude that software is the key to productivity growth in the IT-using world, and show that it can be used as the closest surrogate to represent business complementarities to IT in macro-level studies

Guidelines and an Example of Applying ELeRS - A Framework for Scoping E-Learning Research in Healthcare

Healthcare is a large, complex industry, involving many stakeholders, involving issues at individual, organizational, inter-organizational, and/or international levels. The ELeRS framework was recently formulated to help scope e-learning research in the healthcare industry. In this paper, we describe some practical guidelines to assist researchers use this framework. These guidelines assist researchers to either formulate an independent research study or a series of related studies, using the defined framework. A summary of the framework is first presented, followed by the guidelines, and then a concrete example of how it can be applied. Our experience shows ELeRS systematize the scoping of new research in e-learning. Some lessons learnt are discussed also

A Method to Estimate High-Dimensional Synergistic Interactions: A Case Study on Information Technology Business Value

In describing cause and effect relationships, difficulties may arise when two or more factors act as causes of a particular outcome. The complication results from the possibility that any one or more of the factors modify the extent to which others provide an effect. Such complex multi-factor interactions imply that multiple causal chains have at least some part in common; thus the evaluation of the synergistic effect of two or more causes is pertinent to the study of the causal mechanisms involved. The aim of this paper is to propose a new approach to systematically analyse combinations of interacting causal factors that might lead to good outcomes. Our approach was demonstrated on data from a highly complex field; a large dataset about Information Technology impact on business value collected by the Australian Department of Communication, IT and Art. Experimental evaluation confirms that this approach is able to statistically estimate the magnitude of higher-order interactions from multiple causal factors. Hence, synergistic interactions can be hypothesised and tested between any number of factors