Mental Health Clinical Decision Support Exploiting Big Data

The complexity of clinical decision-making is immensely increasing with the advent of big data with a clinical relevance. Clinical decision systems represent useful e-health tools applicable to various tasks within the clinical decision-making process. This chapter is devoted to basic principles of clinical decision support systems and their benefits for healthcare and patient safety. Big data is crucial input for clinical decision support systems and is helpful in the task to find the diagnosis, prognosis, and therapy. Statistical challenges of analyzing big data in psychiatry are overviewed, with a particular interest for psychiatry. Various barriers preventing telemedicine tools from expanding to the field of mental health are discussed. The development of decision support systems is claimed here to play a key role in the development of information-based medicine, particularly in psychiatry. Information technology will be ultimately able to combine various information sources including big data to present and enforce a holistic information-based approach to psychiatric care.

Exploring Diseases Relationships

Developing an efficient algorithm for traversing large ontologies is a key challenge for many semantic-based applications. This chapter introduces an approach, spreading activation over ontology (SAOO), to explore the relationship between two human diseases using an ontology-based spreading activation approach. SAOO comprises two phases: semantic matching and diseases relatedness detection. In the semantic matching phase, user-submitted diseases are semantically identified in the ontology graph using the proposed matching algorithm. Semantic matching conducts more analysis in the matching process, which comprises term normalization; phrase analysis, and word sense disambiguation. In the diseases relatedness detection phase, the URIs of these diseases are passed to the relatedness detector to detect the relationship connecting them. SAOO improves healthcare systems by considering semantic domain knowledge and a set of SWRL rules to infer diseases relatedness. We present a use case that outlines how SAOO can be used to explore relationships between vaccines in the vaccine ontology.

Feasibility of Using Smart Devices and Digital Technologies in the Assessment of Human Cognitive Abilities

The chapter covers topics concerning the use of smart devices such as smartphones, tablet devices, computer-based testing, and digital technology such as virtual reality, for the use of neuropsychological assessments. Several research results show promise for the use of the aforementioned technologies for the benefit of assessments for discriminating between patients and normal controls, and the increasing comfort levels of participants. Key advantages and disadvantages to using these technologies and future directions in terms of adopting newer technologies are discussed in the light of current developments. A specific emphasis is also laid on countries such as India that is ready to adopt such technologies in the healthcare sector.

Predicting Patient Turnover

Predicting patient turnover within health services is beneficial for resource planning. In this chapter, patient turnover is viewed as a form of customer churn. As such, the authors examine whether free-form notes are useful for solving the classification problem typically associated with customer churn. The authors show that classifiers which incorporate free-form notes, using natural language processing techniques, are up to 11% more accurate than classifiers that are solely developed using structured data. In addition, the authors show that free-form notes aggregated for each account perform better than treating each note separately. It is suggested that hospitals and chronic disease management clinics can use structured data and free-form notes from electronic health records to predict which patients are likely to cease receiving care from their facilities. Classification tools for predicting patient churn are of interest to hospital administrators; such information can aid in resource planning and facilitates smoother handoffs between care providers.

Algorithmic Analysis of Clinical, Neuropsychological, and Imaging Data in Localization-Related Epilepsy

The current study examines algorithmic approaches for analysis of multimodal attributes in localization-related epilepsy (LRE), specifically, their impact on the selection of patients for surgical consideration. Invasive electrographic data is excluded here to concentrate upon the localized anatomical landmarks and identified/initialized brain structures in volumetric MR images as well as initial clinical presentation and the varied elements of the seizure history, ictal semiology, risk and seizure-precipitating factors and physical findings in addition to several features of the neuropsychological profile including various parameters of cognition and both speech and memory lateralization. First, the imaging modality data is excluded and just clinical, electrographic and neuropsychological data are investigated. Afterward, the imaging data are investigated and a comparison between the prediction results of the two types of data is done. In the case of using non-imaging multimodal data, 56% and using imaging features, about 71% of correct outcome prediction was obtained.

Consideration of Stress Coping Support System for Prevention of Mental Disease Using Smart Heart Rate Monitor

In recent years, there has been a significant increase in the number of patients suffering from mood disorders in Japan, reaching 1.041 million in 2008. In 2014, the number of patients reached 1.116 million, the highest number recorded in the past. In modern society with the fourth industrial revolution, with its multitude of stressors that we encounter on a daily life, a characteristic of mental disorders is that there is a risk to increase them at the unconscious level, and even if the patient were to detect the condition, they are difficult to treat. Therefore, in everyday life, it is desirable to always measure the stress and detect the early signature before they get worse. Thus, this study aims to develop stress coping support system using smart finger plethysmogram measurement, visualizing the internal signals through the heart rate which is affected by stress.

Glycemic Monitoring and Prediction With Response Improvement via Psyllium

Fibers are proven to provide health benefits in preventing metabolic diseases. This chapter first presents the existing blood glucose monitoring sensors and a prediction model for blood glucose concentration. It also aims at analyzing the efficacy of a functional fiber, psyllium on the glycemic control function. Three studies included suggesting psyllium supplementation would significantly improve glycemic response while two studies included showed no effects. Advantages and limitations of each study were evaluated. Overall, it is generally believed that psyllium might give glycemic response improvement effect, especially in Type II Diabetes Mellitus patients.

An Empirical Study of Information and Communications Technology (ICT) in the Nigerian Health Sector

This study focuses on the assessment of different ICT tools used by Nigerian health workers. Structured questionnaires were used to collect information from 106 respondents. The questionnaire consists of 5 sections, namely: demographics, extent of use of ICT, tasks and activities carried out with ICT, the year of adoption of ICT. Descriptive statistics tools were used for data summarization and visualization. The results showed that the ICT devices were more commonly used among females than male medical personnel which were most common among the age group of 26-30 years and were nurses and doctors with less than 5 years' experience. The earliest ICT tool adopted was the PC in 1994 followed by mobile phones and search engines in 1996 and the projector in 2001. A majority of the health workers used ICT for administrative functions followed by research and personal work. ICT majorly impacted ICT by promoting collaboration among physicians, quicker medical diagnoses of diseases, increased efficiency and facilitated remote consultation, diagnoses and treatment.

Health Information Technology

Healthcare reform and health information technology (HIT) are transforming physicians' roles in delivering healthcare. New technologies present physicians with exciting new opportunities and challenges to enhance medical practice, reduce costs, and improve patient experiences, as well as opportunities to develop new competencies and standards of professionalism. The Dreyfus model for skills acquisition may provide a helpful framework. Within the competency context, understanding and leveraging drivers of and the barriers to HIT adoption can promote a learning culture that may more readily assimilate new HIT. Involving physicians in designing and implementing HIT systems could result in increasing physician satisfaction. Supportive staffing and technical assistance may aid physicians in successfully implementing the systems without increasing workload or decreasing professional satisfaction. Understanding the needs of 21st century physicians related to HIT solutions should greatly increase the successful integration of HIT into the 21st century healthcare workplace.