Clinical Validation of M-TONX: A Novel Combo Rebound Tonometer and Pachymeter.

Purpose: This study aims to perform a clinical investigation of an innovative rebound technology-based device, the M-TONX, to simultaneously measure intraocular pressure (IOP) and central corneal thickness (CCT).

Methods: The IOP and CCT of the patients were first measured by the M-TONX. Then, the measurements were repeated by the Goldman applanation (GAT) and the Pentacam corneal topographer, as the standard devices.

A review on deep learning methods for heart sound signal analysis.

Introduction: Application of Deep Learning (DL) methods is being increasingly appreciated by researchers from the biomedical engineering domain in which heart sound analysis is an important topic of study. Diversity in methodology, results, and complexity causes uncertainties in obtaining a realistic picture of the methodological performance from the reported methods.

Methods: This survey paper provides the results of a broad retrospective study on the recent advances in heart sound analysis using DL methods.

Information Platform for Antibiotics.

Antibiotic stewardship is continuously evolving to incorporate results from novel research, clinical findings, and specialist recommendations. Numerous dedicated information sources, including web-based solutions, are available to keep medical practitioners informed. However, the provided information is often extensive, requiring users to extract the relevant facts.

Patient Self-Monitoring Using Intelligent Phonocardiography on a Mobile Platform.

The paper presents the design and high-fidelity prototype of the remote patient self-monitoring system using a combination of intelligent phonocardiography, mobile and web-based platforms. The advantage of self-monitoring is patient awareness about potential changes, the convenience of performing the measurement often, and the saving of the findings. A mobile platform enables a physician to see the data, get a summary of patient recordings, and as well as saving the data.

User Needs of Young Czech Adults with Multiple Sclerosis in a Lifestyle App Design.

This paper presents a study that examined desired functionality, content, and design of a mobile application for young Czech adults living with Multiple Sclerosis (MS). The study was structured around a high-fidelity prototype developed for the corresponding user group in Norway. Both groups were active on social media and willing to contribute to designing an application promoting a healthy lifestyle and well-being.

Recurrent vs Non-Recurrent Convolutional Neural Networks for Heart Sound Classification.

Convolutional Neural Network (CNN) has been widely proposed for different tasks of heart sound analysis. This paper presents the results of a novel study on the performance of a conventional CNN in comparison to the different architectures of recurrent neural networks combined with CNN for the classification task of abnormal-normal heart sounds. The study considers various combinations of parallel and cascaded integration of CNN with Gated Recurrent Network (GRN) as well as Long- Short Term Memory (LSTM) and explores the accuracy and sensitivity of each integration independently, using the Physionet dataset of heart sound recordings.

Parralel Recurrent Convolutional Neural Network for Abnormal Heart Sound Classification.

This paper presents the results of a study performed on Parallel Convolutional Neural Network (PCNN) toward detecting heart abnormalities from the heart sound signals. The PCNN preserves dynamic contents of the signal in a parallel combination of the recurrent neural network and a Convolutional Neural Network (CNN). The performance of the PCNN is evaluated and compared to the one obtained from a Serial form of the Convolutional Neural Network (SCNN) as well as two other baseline studies: a Long- and Short-Term Memory (LSTM) neural network and a Conventional CNN (CCNN).

Deep Time Growing Neural Network vs Convolutional Neural Network for Intelligent Phonocardiography.

This paper explores the capabilities of a sophisticated deep learning method, named Deep Time Growing Neural Network (DTGNN), and compares its possibilities against a generally well-known method, Convolutional Neural network (CNN). The comparison is performed by using time series of the heart sound signal, so-called Phonocardiography (PCG). The classification objective is to discriminate between healthy and patients with cardiac diseases by applying a deep machine learning method to PCGs.

Designing a Dashboard for HIV-data Reporting Performance by Facilities: Case Study of Kenya.

Health management information systems implemented in low-and middle-income countries (LMICs) have provided availability of HIV-data. As such, dashboards have become increasingly popular as they provide a potentially powerful avenue for deriving insights at glance. This promotes use of data for decision-making by various stakeholders such as Ministries of Health as well as international donor organizations.

Information Technologies for Cognitive Decline.

Information technology (IT) is used to establish diagnosis and provide treatments for people with cognitive decline. The condition affects many before it becomes clear that more permanent changes, like dementia, could be noticed. Those who search for information are exposed to lots of information and different technologies which they need to make sense of and eventually use to help themselves.

Impact of Electronic Medical Records Systems in Reporting HIV Health Data Indicators in Kenya.

Electronic Medical Records Systems (EMRs) improve the quality of patient care and reduce medical errors. Nevertheless, their role in health data indicator reporting performance is unclear. We assessed reporting completeness and timeliness of HIV indicator data to the national aggregate reporting system, District Health Information Software 2 (DHIS2) in Kenya.

Intellicor: Mobile Design for Monitoring Phonographic Signals.

A mobile and web-based prototype was developed to explore utility of heart sound data in the context of patient self-monitoring. There are not many applications available despite measurement equipment that can be purchased. This research aimed at developing an application that could help patients understand and use phonocardiography.

A-Test Method for Quantifying Structural Risk and Learning Capacity of Supervised Machine Learning Methods.

This paper presents an original method for studying the performance of the supervised Machine Learning (ML) methods, the A-Test method. The method offers the possibility of investigating the structural risk as well as the learning capacity of ML methods in a quantitating manner. A-Test provides a powerful validation method for the learning methods with small or medium size of the learning data, where overfitting is regarded as a common problem of learning.

Users' perception on factors contributing to electronic medical records systems use: a focus group discussion study in healthcare facilities setting in Kenya.

Background: Electronic medical records systems (EMRs) adoption in healthcare to facilitate work processes have become common in many countries. Although EMRs are associated with quality patient care, patient safety, and cost reduction, their adoption rates are comparatively low. Understanding factors associated with the use of the implemented EMRs are critical for advancing successful implementations and scale-up sustainable initiatives.

A multivariate statistical evaluation of actual use of electronic health record systems implementations in Kenya.

Background: Health facilities in developing countries are increasingly adopting Electronic Health Records systems (EHRs) to support healthcare processes. However, only limited studies are available that assess the actual use of the EHRs once adopted in these settings. We assessed the state of the 376 KenyaEMR system (national EHRs) implementations in healthcare facilities offering HIV services in Kenya.

Identifying barriers and facilitators in HIV-indicator reporting for different health facility performances: A qualitative case study.

Identifying barriers and facilitators in HIV-indicator reporting contributes to strengthening HIV monitoring and evaluation efforts by acknowledging contributors to success, as well as identifying weaknesses within the system that require improvement. Nonetheless, there is paucity in identifying and comparing barriers and facilitators in HIV-indicator data reporting among facilities that perform well and those that perform poorly at meeting reporting completeness and timeliness requirements. Therefore, this study aims to use a qualitative approach in identifying and comparing the current state of barriers and facilitators in routine reporting of HIV-indicators by facilities performing well, and those performing poorly in meeting facility reporting completeness and timeliness requirements to District Health Information Software2 (DHIS2).

Development of standard indicators to assess use of electronic health record systems implemented in low-and medium-income countries.

Background: Electronic Health Record Systems (EHRs) are being rolled out nationally in many low- and middle-income countries (LMICs) yet assessing actual system usage remains a challenge. We employed a nominal group technique (NGT) process to systematically develop high-quality indicators for evaluating actual usage of EHRs in LMICs.

Methods: An initial set of 14 candidate indicators were developed by the study team adapting the Human Immunodeficiency Virus (HIV) Monitoring, Evaluation, and Reporting indicators format.

Evaluating performance of health care facilities at meeting HIV-indicator reporting requirements in Kenya: an application of K-means clustering algorithm.

Background: The ability to report complete, accurate and timely data by HIV care providers and other entities is a key aspect in monitoring trends in HIV prevention, treatment and care, hence contributing to its eradication. In many low-middle-income-countries (LMICs), aggregate HIV data reporting is done through the District Health Information Software 2 (DHIS2). Nevertheless, despite a long-standing requirement to report HIV-indicator data to DHIS2 in LMICs, few rigorous evaluations exist to evaluate adequacy of health facility reporting at meeting completeness and timeliness requirements over time.

Data cleaning process for HIV-indicator data extracted from DHIS2 national reporting system: a case study of Kenya.

Background: The District Health Information Software-2 (DHIS2) is widely used by countries for national-level aggregate reporting of health-data. To best leverage DHIS2 data for decision-making, countries need to ensure that data within their systems are of the highest quality. Comprehensive, systematic, and transparent data cleaning approaches form a core component of preparing DHIS2 data for analyses.

Integrating Socially Assistive Robots into Japanese Nursing Care.

This paper presents experiences of integrating assistive robots in Japanese nursing care through semi-structured interviews and site observations at three nursing homes in Japan during the year 2019. The study looked at experiences with the robots Paro, Pepper, and Qoobo. The goal was to investigate and evaluate the current state of using robots within the nursing care context, which involved: firsthand experiences with intended and real users; and response from the elderly, and nursing staff.

Assessment of HIV Data Reporting Performance by Facilities During EMR Systems Implementations in Kenya.

There is little evidence that implementations of Electronic Medical Record Systems (EMRs) are associated with better reporting completeness and timeliness of HIV routine data to the national aggregate system. We analyzed the reporting completeness and timeliness of HIV reports to Kenya's national aggregate reporting system from District Health Information Software 2 (DHIS2) for the period 2011 to 2018. On average, reporting completeness improved to 97% whilst timeliness increased to 83% in 2017 with similar performance for the facilities under study that implemented either KenyaEMR or IQCare.

K-Means Clustering in Monitoring Facility Reporting of HIV Indicator Data: Case of Kenya.

Health management information systems (HMISs) in low- and middle-income countries have been used to collect large amounts of data after years of implementation, especially in support of HIV care services. National-level aggregate reporting data derived from HMISs are essential for informed decision-making. However, the optimal statistical approaches and algorithms for deriving key insights from these data are yet to be fully and adequately utilized.

Integrating Socially Assistive Robots into Japanese Nursing Care.

This paper presents experiences of integrating assistive robots in Japanese nursing care through semi-structured interviews and site observations at three nursing homes in Japan during the spring of 2019. The study looked at experiences with the robots Paro, Pepper, and Qoobo. The goal was to investigate and evaluate the current state of using robots in the nursing care context, firsthand experiences with intended and real use, as well as response from the elderly and nursing staff.

Health Facility Ownership Type and Performance on HIV Indicator Data Reporting in Kenya.

In low- and middle-income countries, private and public facilities tend to have highly variable characteristics, which might affect their performance in meeting reporting requirements mandated by ministries of health. There is conflicting evidence on which facility type performs better across various care dimensions, and only few studies exist to evaluate relative performance around nationally-mandated indicator reporting to Ministries of Health. In this study, we evaluated the relationship between facility ownership type and performance on HIV indicator data reporting, using the case of Kenya.

Distinguishing Septal Heart Defects from the Valvular Regurgitation Using Intelligent Phonocardiography.

This paper presents an original machine learning method for extracting diagnostic medical information from heart sound recordings. The method is proposed to be integrated with an intelligent phonocardiography in order to enhance diagnostic value of this technology. The method is tailored to diagnose children with heart septal defects, the pathological condition which can bring irreversible and sometimes fatal consequences to the children.