Jamil Hussain

H2RM: A Hybrid Rough Set Reasoning Model for Prediction and Management of Diabetes Mellitus.

Diabetes is a chronic disease characterized by high blood glucose level that results either from a deficiency of insulin produced by the body, or the body’s resistance to the effects of insulin. Accurate and precise reasoning and prediction models greatly help physicians to improve diagnosis, prognosis and treatment procedures of different diseases. Though numerous models have been proposed to solve issues of diagnosis and management of diabetes, they have the following drawbacks: (1) restricted one type of diabetes; (2) lack understandability and explanatory power of the techniques and decision; (3) limited either to prediction purpose or management over the structured contents; and (4) lack competence for dimensionality and vagueness of patient’s data. To overcome these issues, this paper proposes a novel hybrid rough set reasoning model (H2RM) that resolves problems of inaccurate prediction and management of type-1 diabetes mellitus (T1DM) and type-2 diabetes mellitus (T2DM). For verification of the proposed model, experimental data from fifty patients, acquired from a local hospital in semi-structured format, is used. First, the data is transformed into structured format and then used for mining prediction rules. Rough set theory (RST) based techniques and algorithms are used to mine the prediction rules. During the online execution phase of the model, these rules are used to predict T1DM and T2DM for new patients. Furthermore, the proposed model assists physicians to manage diabetes using knowledge extracted from online diabetes guidelines. Correlation-based trend analysis techniques are used to manage diabetic observations. Experimental results demonstrate that the proposed model outperforms the existing methods with 95.9% average and balanced accuracies.

An innovative platform for person-centric health and wellness support

Modern digital technologies are paving the path to a revolutionary new concept of health and wellness care. Nowadays, many new solutions are being released and put at the reach of most consumers for promoting their health and wellness self-management. However, most of these applications are of very limited use, arguable accuracy and scarce interoperability with other similar systems. Accordingly, frameworks that may orchestrate, and intelligently leverage, all the data, information and knowledge generated through these systems are particularly required. This work introduces Mining Minds, an innovative framework that builds on some of the most prominent modern digital technologies, such as Big Data, Cloud Computing, and Internet of Things, to enable the provision of personalized healthcare and wellness support. This paper aims at describing the efficient and rational combination and interoperation of these technologies, as well as their integration with current and future personalized health and wellness services and business.

SNS Based Predictive Model for Depression

Worldwide the Mental illness is a primary cause of disability. It affects millions of people each year and whom of few receives cure. We found that social networking sites (SNS) can be used as a screening tool for discovering an affective mental illness in individuals. SNS posting truly depicts user’s current behavior, thinking style, and mood. We consider a set of behavioral attributes concerning to socialization, socioeconomics, familial, marital status, feeling, language use, and references of antidepressant treatments. We take advantage of these behavioral attributes to envision a tool that can provide prior alerts to an individual based on their SNS data regarding Major Depression Disorder (MDD). We propose a method, to automatically classify individuals into displayer and non-displayer depression using ensemble learning techniquefrom theirFacebook profile. Our developed tool is used for MDD diagnosis of individuals in additional to questioner techniques such as Beck Depression Inventory (BDI) and CESD-R.

Knowledge-Based Query Construction Using the CDSS Knowledge Base for Efficient Evidence Retrieval

Finding appropriate evidence to support clinical practices is always challenging, and the construction of a query to retrieve such evidence is a fundamental step. Typically, evidence is found using manual or semi-automatic methods, which are time-consuming and sometimes make it difficult to construct knowledge-based complex queries. To overcome the difficulty in constructing knowledge-based complex queries, we utilized the knowledge base (KB) of the clinical decision support system (CDSS), which has the potential to provide sufficient contextual information. To automatically construct knowledge-based complex queries, we designed methods to parse rule structure in KB of CDSS in order to determine an executable path and extract the terms by parsing the control structures and logic connectives used in the logic. The automatically constructed knowledge-based complex queries were executed on the PubMed search service to evaluate the results on the reduction of retrieved citations with high relevance. The average number of citations was reduced from 56,249 citations to 330 citations with the knowledge-based query construction approach, and relevance increased from 1 term to 6 terms on average. The ability to automatically retrieve relevant evidence maximizes efficiency for clinicians in terms of time, based on feedback collected from clinicians. This approach is generally useful in evidence-based medicine, especially in ambient assisted living environments where automation is highly important.

Adaptive User Interface and User Experience Based Authoring Tool for Recommendation Systems

User preferences and contextual changes impact the duration of adaptation of user interface (UI) for a particular system, specifically recommendation systems. Static UIs lack reflection of these behavioral changes which lead to bottleneck in the fulfillment of user needs and satisfaction. Therefore, a mechanism to incorporate User Experience (UX) for embedded customization in the UI is required for longer adaptation of the system. We propose an Adaptive UI / UX Authoring Tool that adapts the UI with the help of the information extracted from the UX. UI is provided to the user in recommendation systems based on personal profile and contextual information. Continuous involvement of the user using feedback, web monitoring and gamification to measure his satisfaction and evolution of the personal and contextual information, adapts the UI with the help of UX. UX controls the evolutionary process of the adaptation of the user interfaces and also maintains the personalization aspect. The proposed system guarantees the longer duration of utilization of the services provided by the recommendation systems due to provision of personalized UI.

An interactive case-based flip learning tool for medical education

Legacy Case-Based Learning (CBL) medical educational systems aim to boost the learning and educational process but lacks the support of Systematized Nomenclature of Medicine (SNOMED) and flip learning concepts. Integrating these vocabularies can exploit the learning outcomes and build confidence in students while making decision to rehearsal in advance before attending the actual CBL. The scope of this research covers delivering of medical education in interactive and intelligent way, efficient knowledge sharing, promoting team work environments, and building a knowledge-base for future to support automated computerized feedback. To achieve these goals, we propose a tool called Interactive Case-Based Flip Learning Tool (ICBFLT) that covers formulation of CBL case summaries, getting standard computerized help from both SNOMED vocabulary and state of the art solutions, and finally getting feedback from concerned tutor. In order to evaluate the ICBFLT, a scenario from the School of Medicine, University of Tasmania, Australia has been considered. This is an ongoing work and this paper gives an overview of the ICBFLT architecture with some intermediate results. The evaluation shows that the system has satisfied its users in term of interaction upto 70%.

Model-based adaptive user interface based on context and user experience evaluation

Personalized services have greater impact on user experience to effect the level of user satisfaction. Many approaches provide personalized services in the form of an adaptive user interface. The focus of these approaches is limited to specific domains rather than a generalized approach applicable to every domain. In this paper, we proposed a domain and device-independent model-based adaptive user interfacing methodology. Unlike state-of-the-art approaches, the proposed methodology is dependent on the evaluation of user context and user experience (UX). The proposed methodology is implemented as an adaptive UI/UX authoring (A-UI/UX-A) tool; a system capable of adapting user interface based on the utilization of contextual factors, such as user disabilities, environmental factors (e.g. light level, noise level, and location) and device use, at runtime using the adaptation rules devised for rendering the adapted interface. To validate effectiveness of the proposed A-UI/UX-A tool and methodology, user-centric and statistical evaluation methods are used. The results show that the proposed methodology outperforms the existing approaches in adapting user interfaces by utilizing the users context and experience.

A Multimodal Deep Log-Based User Experience (UX) Platform for UX Evaluation

The user experience (UX) is an emerging field in user research and design, and the development of UX evaluation methods presents a challenge for both researchers and practitioners. Different UX evaluation methods have been developed to extract accurate UX data. Among UX evaluation methods, the mixed-method approach of triangulation has gained importance. It provides more accurate and precise information about the user while interacting with the product. However, this approach requires skilled UX researchers and developers to integrate multiple devices, synchronize them, analyze the data, and ultimately produce an informed decision. In this paper, a method and system for measuring the overall UX over time using a triangulation method are proposed. The proposed platform incorporates observational and physiological measurements in addition to traditional ones. The platform reduces the subjective bias and validates the user’s perceptions, which are measured by different sensors through objectification of the subjective nature of the user in the UX assessment. The platform additionally offers plug-and-play support for different devices and powerful analytics for obtaining insight on the UX in terms of multiple participants.

Mining User Experience Dimensions from Mental Illness Apps

Mental illness is prevalent, the primary cause of disability worldwide, and regardless of the extensive treatment choices. Mobile apps provide greater support for depression treatment that eliminates the communication barriers. This perspective can be dropped with poor application design. Our goal is to mining the user experience (UX) dimensions from top-n mental illness apps reviews that will help to design the better application for persons with severe mental illness (SMI) and cognitive deficits. In this paper, we extracted the key UX dimension from a huge corpus of mental illness apps reviews using unsupervised Latent Dirichlet Analysis (LDA). Finally, LDA uncovered 20 UX dimensions that need to consider for mental illness app design in order to promote the positive UX by reducing the cognitive load of app end users.

Medical Semantic Question Answering Framework on RDF Data Cubes

In this paper, we have proposed a framework to support the semantic question answering over the RDF data cube that is published according to the Linked Open Data (LOD) principles. As statistical data published all over the Internet there is a need to empowers the non-experts to query in the form of the natural language. But, the existing question answering system unable to support query on the statistical data in the form of the RDF cube. The current research is motivated by the need of the clinical organizations, who wish to develop a platform for analyzing the clinical data across multiple clinical sites. Linked open data (LOD) provides a support to published statistical data in the form of the RDF cube. Our proposed framework will provide a support to interact in the form of the natural language question answering that will produce the SPARQL query to extract the answer from the RDF data cube. In future, we will develop the benchmark to calculate the accuracy of the answer.