Improving Current Glycated Hemoglobin Prediction in Adults: Use of Machine Learning Algorithms With Electronic Health Records.

Background: Predicting the risk of glycated hemoglobin (HbA) elevation can help identify patients with the potential for developing serious chronic health problems, such as diabetes. Early preventive interventions based upon advanced predictive models using electronic health records data for identifying such patients can ultimately help provide better health outcomes.

Objective: Our study investigated the performance of predictive models to forecast HbA elevation levels by employing several machine learning models.

Using Machine Learning to Predict Early Preparation of Pharmacy Prescriptions at PSMMC - a Comparison of Four Machine Learning Algorithms.

Background: Patient satisfaction is one of the primary Key Performance Indicator (KPI) goal of health care service, and it creates many reasons for implementing research, plans, and innovations to achieve it for a better quality of life. Cutting Patient waiting time would increase patient satisfaction.

Objective: A healthcare framework has been constructed utilizing a machine learning approach to construct an early predicting preparation model of pharmacy prescriptions and the worthiness of changing the outpatient pharmacy workflow.

Utilizing Machine Learning to Recognize Human Activities for Elderly and Homecare.

Introduction: Dementia is a progressive disorder associated with age, which is characterized by deterioration of individuals' cognitive functions such as the ability to perform routine tasks. With the increase of human life expectancy, the prevalence of dementia patients will reach 152 million in 2050. Unfortunately, there is no treatment available to cure dementia or alter the course of its progression.

Improving Accuracy for Diabetes Mellitus Prediction by Using Deepnet.

Diabetes is a salient issue and a significant health care concern for many nations. The forecast for the prevalence of diabetes is on the rise. Hence, building a prediction machine learning model to assist in the identification of diabetic patients is of great interest.

Predicting Current Glycated Hemoglobin Levels in Adults From Electronic Health Records: Validation of Multiple Logistic Regression Algorithm.

Background: Electronic health record (EHR) systems generate large datasets that can significantly enrich the development of medical predictive models. Several attempts have been made to investigate the effect of glycated hemoglobin (HbA) elevation on the prediction of diabetes onset. However, there is still a need for validation of these models using EHR data collected from different populations.

Building a Culture of Health Informatics Innovation and Entrepreneurship: A New Frontier.

Entrepreneurship and innovation within the health informatics (HI) scientific community are relatively sluggish when compared to other disciplines such as computer science and engineering. Healthcare in general, and specifically, the health informatics scientific community needs to embrace more innovative and entrepreneurial practices. In this paper, we explore the concepts of innovation and entrepreneurship as they apply to the health informatics scientific community.