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

BACKGROUND: Predicting the risk of glycated hemoglobin (HbA1c) 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 HbA1c elevation levels by employing several machine learning models. We also examined the use of patient electronic health record longitudinal data in the performance of the predictive models. Explainable methods were employed to interpret the decisions made by the black box models. METHODS: This study employed multiple logistic regression, random forest, support vector machine, and logistic regression models, as well as a deep learning model (multilayer perceptron) to classify patients with normal (<5.7%) and elevated (≥5.7%) levels of HbA1c. We also integrated current visit data with historical (longitudinal) data from previous visits. Explainable machine learning methods were used to interrogate the models and provide an understanding of the reasons behind the decisions made by the models. All models were trained and tested using a large data set from Saudi Arabia with 18,844 unique patient records. RESULTS: The machine learning models achieved promising results for predicting current HbA1c elevation risk. When coupled with longitudinal data, the machine learning models outperformed the multiple logistic regression model used in the comparative study. The multilayer perceptron model achieved an accuracy of 83.22% for the area under receiver operating characteristic curve when used with historical data. All models showed a close level of agreement on the contribution of random blood sugar and age variables with and without longitudinal data. CONCLUSIONS: This study shows that machine learning models can provide promising results for the task of predicting current HbA1c levels (≥5.7% or less). Using patients' longitudinal data improved the performance and affected the relative importance for the predictors used. The models showed results that are consistent with comparable studies.

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. METHODS: Data sets were retrieved between Januarys and June 2019 from Prince Sultan Military Medical City, Riyadh, KSA, for all patients who visited the clinics or discharged with pharmacy prescriptions. Included (1048575) instances and composed of (11) attributes. The evaluation criteria to compare the four algorithms were based on precision, Recall, True Positive Rate, False Negative Rate, F-measure, and Area under the curve. RESULTS: Overall, 94.88% of patient's shows at the pharmacy, female represents 58.89% of the data set while male represents 41.1%. RT gives the highest accuracy, with 97.22% in comparison to the other algorithms. CONCLUSION: The suggestion to change the pharmacy workflow is worth increasing patient satisfaction and overall the quality of the care.

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. This study aims to create a machine learning model that is capable of predicting diabetes with high performance. The following study used the BigML platform to train four machine learning algorithms, namely, Deepnet, Models (decision tree), Ensemble and Logistic Regression, on data sets collected from the Ministry of National Guard Hospital Affairs (MNGHA) in Saudi Arabia between the years of 2013 and 2015. The comparative evaluation criteria for the four algorithms examined included; Accuracy, Precision, Recall, F-measure and PhiCoefficient. Results show that the Deepnet algorithm achieved higher performance compared to other machine learning algorithms based on various evaluation matrices.

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 (HbA1c) 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. OBJECTIVE: The aim of this study is to perform a replication study to validate, evaluate, and identify the strengths and weaknesses of replicating a predictive model that employed multiple logistic regression with EHR data to forecast the levels of HbA1c. The original study used data from a population in the United States and this differentiated replication used a population in Saudi Arabia. METHODS: A total of 3 models were developed and compared with the model created in the original study. The models were trained and tested using a larger dataset from Saudi Arabia with 36,378 records. The 10-fold cross-validation approach was used for measuring the performance of the models. RESULTS: Applying the method employed in the original study achieved an accuracy of 74% to 75% when using the dataset collected from Saudi Arabia, compared with 77% obtained from using the population from the United States. The results also show a different ranking of importance for the predictors between the original study and the replication. The order of importance for the predictors with our population, from the most to the least importance, is age, random blood sugar, estimated glomerular filtration rate, total cholesterol, non-high-density lipoprotein, and body mass index. CONCLUSIONS: This replication study shows that direct use of the models (calculators) created using multiple logistic regression to predict the level of HbA1c may not be appropriate for all populations. This study reveals that the weighting of the predictors needs to be calibrated to the population used. However, the study does confirm that replicating the original study using a different population can help with predicting the levels of HbA1c by using the predictors that are routinely collected and stored in hospital EHR systems.

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. However, there is an area of support for patients and caregivers to assist daily living. Technological devices and applications are increasingly advancing, exploiting sensory data for dementia patients and homecare using smartphones to permit monitoring of their activities. AIM: This paper uses the labeled dataset besides comparing the 3-classification algorithm to evaluate whether or not the algorithms deployed can classify the activities with high accuracy. RESULTS: A public data is used to classify human activities into one of the six activities, BigML platform is used to build machine learning models. Results show that machine learning algorithms can achieve high accuracy. The activity recognition algorithms are highly accurate using ridged regression and deep neural networks, with almost all activities being recognized correctly over 98% of the time. CONCLUSION: An application of smartphones can be utilized for human activities monitoring by proposing a high level for dementia patients and homecare monitoring services. Using this service, the patients only need to carry the smartphone, and their caregivers simply need to use the application that monitors their patients' activities.

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. We also outline several strategies to improve the culture of innovation and entrepreneurship within the health informatics scientific community such as: (I) incorporating innovation and entrepreneurship in health informatics education; (II) creating strong linkages with industry and healthcare organizations; (III) supporting national health innovation and entrepreneurship competitions; (IV) creating a culture of innovation and entrepreneurship within healthcare organizations; (V) developing health informatics policies that support innovation and entrepreneurship based on internationally recognized standards; and (VI) develop an health informatics entrepreneurship ecosystem. With these changes, we conclude that embracing health innovation and entrepreneurship may be more readily accepted over the long-term within the health informatics scientific community.