Showing papers by "Mirza Mansoor Baig published in 2018"

Falls management framework for supporting an independent lifestyle for older adults: a systematic review.

Abstract: Falls are one of the common health and well-being issues among the older adults Internet of things (IoT)-based health monitoring systems have been developed over the past two decades for improving healthcare services for older adults to support an independent lifestyle This research systematically reviews technological applications related to falls detection and falls management The systematic review was conducted in accordance to the preferred reporting items for systematic reviews and meta-analysis statement (PRISMA) Twenty-four studies out of 806 articles published between 2015 and 2017 were identified and included in this review Selected studies were related to pre-fall and post-fall applications using motion sensors (10; 4167%), environment sensors (10; 4167%) and few studies used the combination of these types of sensors (4; 1667%) As an outcome of this review, we postulated a falls management framework (FMF) FMF considered pre- and post-fall strategies to support older adults live independently A part of this approach involved active analysis of sensor data with the aim of helping the older adults manage their risk of fall and stay safe in their home FMF aimed to serve the researchers, developers, clinicians and policy makers with pre- and post-falls management strategies to enhance the older adults' independent living and well-being

Clinical Decision Support for Early Detection of Prediabetes and Type 2 Diabetes Mellitus Using Wearable Technology

Abstract: Worldwide spending on long-term and chronic care conditions is increasing to a point that requires immediate interventions and advancements to reduce the burden of the healthcare cost. This research is focused on early detection of prediabetes and type 2 diabetes mellitus (T2DM) using wearable technology. An artificial intelligence model was developed based on adaptive-neuro fuzzy interference to detect prediabetes and T2DM via individualized monitoring. The key contributing factors to the proposed model include heart rate, heart rate variability, breathing rate, breathing volume, and activity data (steps, cadence and calories). The data was collected using an advanced wearable body vest. The real-time data was combined with manual recordings of blood glucose, height, weight, age and sex. The model analyzed the data alongside a clinical knowledge-base. Fuzzy rules were used to establish baseline values via existing interventions, clinical guidelines and protocols. The proposed model was tested and validated using Kappa analysis and achieved an overall agreement of 91%.

Evaluation of Patients at Risk of Hospital Readmission (PARR) and LACE Risk Score for New Zealand Context.

Abstract: Identification and prediction of patients who are at risk of hospital readmission is a critical step towards the reduction of the potential avoidable costs for healthcare organisations. This research was focused on the evaluation of LACE Index for Readmission - Length of stay (days), Acute (emergent) admission, Charlson Comorbidity Index and number of ED visits within six months (LACE) and Patients At Risk of Hospital Readmission (PARR) using New Zealand hospital admissions. This research estimates the risk for all readmissions rather than only those in a subset of referenced conditions. In total, 213,440 admissions between 1 Jan 2015 and 31 Dec 2016 were selected after appropriate ethics approvals and permissions from the three hospitals. The evaluation method used is the Receiver Operating Characteristics (ROC) analysis to evaluate the accuracy of both the LACE and PARR models. As a result, The LACE index achieved an Area Under the Curve (AUC) score of 0.658 in predicting 30-day readmissions. The optimal cut-off for the LACE index is a score of 7 or more with sensitivity of 0.752 and specificity of 0.564. Whereas, the PARR algorithm achieved an AUC score of 0.628 in predicting 30-day readmissions. The optimal cut-off for the PARR index is a score of 0.34 or more with sensitivity of 0.714 and specificity of 0.542.

Cuffless Blood Pressure Estimation Using Pulse Transit Time and Photoplethysmogram Intensity Ratio.

Abstract: High blood pressure (BP) is one of the common risk factors for heart disease, stroke, congestive heart failure, and kidney disease. An accurate, continuous and cuffless BP monitoring technique could help clinicians improve the rate of prevention, detection, and treatment of hypertension and related diseases. Pulse transit time (PTT) has attracted interest as an index of BP changes for cuffless BP measurement techniques. Currently, PPT-based BP measurement approaches have improved and are able to relieve the discomfort associated with an inflated cuff such as that used in auscultatory and oscillometric BP measurement techniques. However, PTT can only track the BP variation in high frequency (HF) which limits the true representation of BP changes. This paper presents a continuous and cuffless BP monitoring method based on multi-parameter fusion. We used photoplethysmogram (PPG) and a two-lead electrocardiogram (ECG) and employed an algorithm based on PTT and the PPG intensity ratio (PIR) to continuously track BP in both high and low frequencies and estimate systolic and diastolic BP.

An Agent-based Modelling Approach for Scheduling and Management of Elective Surgeries

Abstract: Elective Surgery (ES) is any planned surgical procedure that aims to improve an individual’s quality of life psychologically and/or physically, reinstate independence and in particular cases prolongs life [1]. ES enables the person to do activities that are important to lead a normal and independent life, but not considered as an hospital emergency which requires immediate treatment [1]. For example, a cataract surgery is not obligatory but the individual who is treated will be able to read and drive like a normal person emphasizing immense lifestyle benefits. Similarly, a grommet procedure can restore the ability to hear with a glue ear. A hip replacement can relieve pain and enhance mobility to carry out day to day tasks [2].

Medical Device Integrated Vital Signs Monitoring Application with Real-Time Clinical Decision Support

Abstract: This research involves the design and development of a novel Android smartphone application for real-time vital signs monitoring and decision support. The proposed application integrates market available, wireless and Bluetooth connected medical devices for collecting vital signs. The medical device data collected by the app includes heart rate, oxygen saturation and electrocardiograph (ECG). The collated data is streamed/displayed on the smartphone in real-time. This application was designed by adopting six screens approach (6S) mobile development framework and focused on user-centered approach and considered clinicians-as-a-user. The clinical engagement, consultations, feedback and usability of the application in the everyday practices were considered critical from the initial phase of the design and development. Furthermore, the proposed application is capable to deliver rich clinical decision support in real-time using the integrated medical device data.

Improving Post-Hospital Discharge Management by Implementing the Discharge Summary on a Mobile Application

Abstract: Patients often rely on a printed discharge summary for clinical information, post-discharge treatment, medications and other health activities, non-adherence to which may lead to readmission. With the active involvement of clinicians, medical informatics professionals and technical advisors a mobile prototype application has been designed and developed to provide a user-friendly presentation of clinical information aiming to increase the adherence of medical advice. We conducted a task-based usability and accuracy test and found that the average accuracy was 97%, time taken for each task to complete was 7.5s (average) and the overall navigation was termed as ‘easy to understand’ by the users.