Health Informatics Journal

For this project, we have used new technologies to create a new channel of communication between doctors and patients in the treatment of mental disorders. We have created a web application using an adaptable design accessible from any mobile device, which allows doctors to adapt their patients’ therapy to real-time knowledge of their current condition. In turn, patients can express their mood state with respect to the component elements of their therapy.

In this work, the authors present two eHealth platforms that are examples of how health systems are migrating from client-server architecture to the web-based and ubiquitous paradigm. These two platforms were modeled, designed, developed and implemented with positive results. First, using ambient-assisted living and ubiquitous computing, the authors enhance how palliative care is being provided to the elderly patients and patients with terminal illness, making the work of doctors, nurses and other health actors easier. Second, applying machine learning methods and a data-centered, ubiquitous, patient’s results’ repository, the authors intent to improve the Down’s syndrome risk estimation process with more accurate predictions based on local woman patients’ parameters. These two eHealth platforms can improve the quality of life, not only physically but also psychologically, of the patients and their families in the country of Panama.

Understanding the information-seeking preferences and Internet access habits of the target audiences for a patient portal is essential for successful uptake. The resource must deliver culturally and educationally appropriate information via technology that is accessible to the intended users and be designed to meet their needs and preferences. Providers must consider multiple perspectives when launching a portal and make any needed adjustments once the launch is underway. We report results of a study of 270 parents and caregivers of paediatric patients in a major health system during the process of implementing a patient portal. Through a 26-question paper-and-pencil survey, data were collected on participant demographics, Internet access and use, health information–seeking behaviours, health literacy, and potential use of a patient portal. Results indicate a positive attitude towards portal use but also suggest that low health literacy may be a key issue to portal adoption.

As a chronic disease, diabetes mellitus has emerged as a worldwide epidemic. The aim of this study is to classify diabetes disease by developing an intelligence system using machine learning techniques. Our method is developed through clustering, noise removal and classification approaches. Accordingly, we use expectation maximization, principal component analysis and support vector machine for clustering, noise removal and classification tasks, respectively. We also develop the proposed method for incremental situation by applying the incremental principal component analysis and incremental support vector machine for incremental learning of data. Experimental results on Pima Indian Diabetes dataset show that proposed method remarkably improves the accuracy of prediction and reduces computation time in relation to the non-incremental approaches. The hybrid intelligent system can assist medical practitioners in the healthcare practice as a decision support system.

Chronic obstructive pulmonary disease is a heterogeneous disease. In this retrospective study, we hypothesize that it is possible to identify clinically relevant phenotypes by applying clustering methods to electronic medical records. We included all the patients >40 years with a diagnosis of chronic obstructive pulmonary disease admitted to the University of New Mexico Hospital between 1 January 2011 and 1 May 2014. We collected admissions, demographics, comorbidities, severity markers and treatments. A total of 3144 patients met the inclusion criteria: 46 percent were >65 years and 52 percent were males. The median Charlson score was 2 (interquartile range: 1–4) and the most frequent comorbidities were depression (36%), congestive heart failure (25%), obesity (19%), cancer (19%) and mild liver disease (18%). Using the sphere exclusion method, nine clusters were obtained: depression–chronic obstructive pulmonary disease, coronary artery disease–chronic obstructive pulmonary disease, cerebrovascular disease–chronic obstructive pulmonary disease, malignancy–chronic obstructive pulmonary disease, advanced malignancy–chronic obstructive pulmonary disease, diabetes mellitus–chronic kidney disease–chronic obstructive pulmonary disease, young age–few comorbidities–high readmission rates–chronic obstructive pulmonary disease, atopy–chronic obstructive pulmonary disease, and advanced disease–chronic obstructive pulmonary disease. These clusters will need to be validated prospectively.

Falls play a critical role in older people’s life as it is an important source of morbidity and mortality in elders. In this article, elders fall risk is predicted based on a physiological profile approach using a multilayer neural network with back-propagation learning algorithm. The personal physiological profile of 200 elders was collected through a questionnaire and used as the experimental data for learning and testing the neural network. The profile contains a series of simple factors putting elders at risk for falls such as vision abilities, muscle forces, and some other daily activities and grouped into two sets: psychological factors and public factors. The experimental data were investigated to select factors with high impact using principal component analysis. The experimental results show an accuracy of ≈90 percent and ≈87.5 percent for fall prediction among the psychological and public factors, respectively. Furthermore, combining these two datasets yield an accuracy of ≈91 percent that is better than the accuracy of single datasets. The proposed method suggests a set of valid and reliable measurements that can be employed in a range of health care systems and physical therapy to distinguish people who are at risk for falls.

Bringing virtual environments into cancer support may offer a particular potential to engage patients and increase adherence to treatment. Developing and pilot-testing an online real-time multi-user three-dimensional platform, this study tested the use of an early prototype of the platform among adolescent and young adult cancer patients. Data were collected with an online questionnaire and using ethnographic methods of participant observation. The adolescent and young adult patients tested basic features of the virtual environment and some conducted brief in-world interactions with fellow patients during hospitalization. They had no reservations about using the technology and shared their ideas about its use. Our pilot test pointed to a number of areas of development for virtual environment applications as potential platforms for medical or behavioral interventions in cancer care. Overall, the results demonstrate the need for high user involvement in the development of such interventions and early testing of intervention designs.

The objective of this research is to identify major subject areas of medical informatics and explore the time-variant changes therein. As such it can inform the field about where medical informatics research has been and where it is heading. Furthermore, by identifying subject areas, this study identifies the development trends and the boundaries of medical informatics as an academic field. To conduct the study, first we identified 26,307 articles in PubMed archives which were published in the top medical informatics journals within the timeframe of 2002 to 2013. And then, employing a text mining -based semi-automated analytic approach, we clustered major research topics by analyzing the most frequently appearing subject terms extracted from the abstracts of these articles. The results indicated that some subject areas, such as biomedical, are declining, while other research areas such as health information technology (HIT), Internet-enabled research, and electronic medical/health records (EMR/EHR), are growing. The changes within the research subject areas can largely be attributed to the increasing capabilities and use of HIT. The Internet, for example, has changed the way medical research is conducted in the health care field. While discovering new medical knowledge through clinical and biological experiments is important, the utilization of EMR/EHR enabled the researchers to discover novel medical insight buried deep inside massive data sets, and hence, data analytics research has become a common complement in the medical field, rapidly growing in popularity.