Towards a Personal Health Knowledge Graph Framework for Patient Monitoring

Healthcare providers face significant challenges with managing and monitoring patient data outside of clinics, particularly with limited resources and insufficient feedback on their patients' conditions. Effective management of these symptoms and exploration of larger bodies of data are vital for maintaining long-term quality of life and preventing late interventions. In this paper, we propose a framework for constructing personal health knowledge graphs from heterogeneous data sources. Our approach integrates clinical databases, relevant ontologies, and standard healthcare guidelines to support alert generation, clinicians' interpretation and querying of patient data. Through a use case focusing on monitoring Chronic Obstructive Lung Disease (COPD) patients, we demonstrate that inference and reasoning on personal health knowledge graphs built with our framework can aid in patient monitoring and enhance the efficacy and accuracy of patient data queries

Towards a Personal Health Knowledge Graph Framework for Patient Monitoring

Healthcare providers face significant challenges with monitoring and managing patient data outside of clinics, particularly with insufficient resources and limited feedback on their patients' conditions. Effective management of these symptoms and exploration of larger bodies of data are vital for maintaining long-term quality of life and preventing late interventions. In this paper, we propose a framework for constructing personal health knowledge graphs from heterogeneous data sources. Our approach integrates clinical databases, relevant ontologies and standard healthcare guidelines to support alert generation, clinician interpretation and querying of patient data. Through a use case of monitoring Chronic Obstructive Pulmonary Disease (COPD) patients, we demonstrate that inference and reasoning on personal health knowledge graphs built with our framework can aid in patient monitoring and enhance the efficacy and accuracy of patient data queries.Comment: 6 pages, 3 figures, conference proceeding

Mining health knowledge graph for health risk prediction

Nowadays classification models have been widely adopted in healthcare, aiming at supporting practitioners for disease diagnosis and human error reduction. The challenge is utilising effective methods to mine real-world data in the medical domain, as many different models have been proposed with varying results. A large number of researchers focus on the diversity problem of real-time data sets in classification models. Some previous works developed methods comprising of homogeneous graphs for knowledge representation and then knowledge discovery. However, such approaches are weak in discovering different relationships among elements. In this paper, we propose an innovative classification model for knowledge discovery from patients’ personal health repositories. The model discovers medical domain knowledge from the massive data in the National Health and Nutrition Examination Survey (NHANES). The knowledge is conceptualised in a heterogeneous knowledge graph. On the basis of the model, an innovative method is developed to help uncover potential diseases suffered by people and, furthermore, to classify patients’ health risk. The proposed model is evaluated by comparison to a baseline model also built on the NHANES data set in an empirical experiment. The performance of proposed model is promising. The paper makes significant contributions to the advancement of knowledge in data mining with an innovative classification model specifically crafted for domain-based data. In addition, by accessing the patterns of various observations, the research contributes to the work of practitioners by providing a multifaceted understanding of individual and public health

e-ESAS: Evolution of a Participatory Design-based Solution for Breast Cancer (BC) Patients in Rural Bangladesh

Healthcare facility is scarce for rural women in the developing world. The situation is worse for patients who are suffering from diseases that require long-term feedback-oriented monitoring such as breast cancer. Lack of motivation to go to the health centers on patients’ side due to sociocultural barriers, financial restrictions and transportation hazards results in inadequate data for proper assessment. Fortunately, mobile phones have penetrated the masses even in rural communities of the developing countries. In this scenario, a mobile phone-based remote symptom monitoring system (RSMS) with inspirational videos can serve the purpose of both patients and doctors. Here, we present the findings of our field study conducted on 39 breast cancer patients in rural Bangladesh. Based on the results of extensive field studies, we have categorized the challenges faced by patients in different phases of the treatment process. As a solution, we have designed, developed and deployed e-ESAS—the first mobile-based RSMS in rural context. Along with the detail need assessment of such a system, we describe the evolution of e-ESAS and the deployment results. We have included the unique and useful design lessons that we learned as e-ESAS evolved through participatory design process. The findings show how e-ESAS addresses several challenges faced by patients and doctors and positively impact their lives

Mining heterogeneous information graph for health status classification

In the medical domain, there exists a large volume of data from multiple sources such as electronic health records, general health examination results, and surveys. The data contain useful information reflecting people’s health and provides great opportunities for studies to improve the quality of healthcare. However, how to mine these data effectively and efficiently still remains a critical challenge. In this paper, we propose an innovative classification model for knowledge discovery from patients’ personal health repositories. By based on analytics of massive data in the National Health and Nutrition Examination Survey, the study builds a classification model to classify patients’health status and reveal the specific disease potentially suffered by the patient. This paper makes significant contributions to the advancement of knowledge in data mining with an innovative classification model specifically crafted for domain-based data. Moreover, this research contributes to the healthcare community by providing a deep understanding of people’s health with accessibility to the patterns in various observations

Improving Heart Failure Education Prior to Discharge: An Emmi Implementation Project

Background: Heart failure is one of the leading causes of hospitalizations and increased health care costs in the United States. Many patients are readmitted within 30 days of discharge, resulting in increased costs. Purpose of Capstone Project: The purpose of this capstone project was to improve heart failure education for patients admitted with heart failure to Mercy Medical Center by utilizing Emmi educational videos in order to decrease the risk of hospital readmissions, improve quality of life, and decrease costs. Methods: The project focused on educating nurses about the importance of utilizing the video. It was asked of the nurses that each patient admitted with a diagnosis of heart failure have the opportunity to watch the educational video prior to discharge. The number of patients with heart failure that were given the opportunity to watch the Emmi educational video was collected. The evaluation of this project was a comparison of the number of Emmi educational videos utilized before the implementation of the Capstone Project to the numbers of videos utilized after the implementation. Results: When comparing the ordering of the Emmi videos after implementation to before the implementation, there was a noticeable increase in Emmi usage. This included the ordering for all categories of the heart failure Emmi and the general heart failure Emmi. Unfortunately, ordering the Emmi did not mean that the video was utilized. Recommendations: It was recommend that the utilization of Emmi videos be continued. Nurses need continued encouragement to utilize the video and not just order it. It was also recommended that APRNs and PAs focus on ordering and implementing the Emmi videos. Lastly, making Emmi utilization a function of case managers, cardiac rehab nurses, and discharge nurses was recommended

Electronic Report Generation Web Service evaluated within a Telemedicine System

This work presents a generic tool based on a client-server architecture that generates electronic reports helping the evaluation process of any information system. For the specific evaluation of telemedicine systems the defined reports cover four dimensions: auditory of the system; evolution of clinical protocols; results from the questionnaires for user acceptance and quality of life; and surveillance of clinical variables. The use of a Web Service approach allows multiplatform use of the developed electronic report service and the modularity followed in the implementation enables easy system evolution and scalability