Sentiments prediction and thematic analysis for diabetes mobile apps using Embedded Deep Neural Networks and Latent Dirichlet Allocation.

The increasing reliance on mobile health for managing disease conditions has opened a new frontier in digital health, thus, the need for understanding what constitutes positive and negative sentiments of the various apps. This paper relies on Embedded Deep Neural Networks (E-DNN), Kmeans, and Latent Dirichlet Allocation (LDA) for predicting the sentiments of diabetes mobile apps users and identifying the themes and sub-themes of positive and negative sentimental users. A total of 38,640 comments from 39 diabetes mobile apps obtained from the google play store are analyzed and accuracy of 87.

Crowdsourcing Operational and Design Flaws of Diabetes Mobile Apps.

Unfortunately, many of the diabetes mobile apps have operational and design flaws that are debarring users from maximizing from the self-management paradigm. We, therefore, aim to identify the markers of operational and design flaws of diabetes mobile apps to facilitate a better user-centred design. e crowdsourced negative user review comments (rating score: 1-3) of 47 diabetes mobile apps from the google play store.

Automatic user sentiments extraction from diabetes mobile apps - An evaluation of reviews with machine learning.

Using diabetes mobile apps for self-management of diabetes is one of the emerging strategies for controlling blood sugar levels and maintaining the wellness of patients with diabetes. This study aims to develop a strategy for thematically extracting user comments from diabetes mobile apps to understand the concern of patients with diabetes. Hence, 2678 user comments obtained from the Google Play Store are thematically analyzed with Non-negative Matrix Factorization (NMF) to identify the themes for describing positive, neutral, and negative sentiments.

Preadmission assessment of extended length of hospital stay with RFECV-ETC and hospital-specific data.

Background: Patients who exceed their expected length of stay in the hospital come at a cost to stakeholders in the healthcare sector as bed spaces are limited for new patients, nosocomial infections increase and the outcome for many patients is hampered due to multimorbidity after hospitalization.

Objectives: This paper develops a technique for predicting Extended Length of Hospital Stay (ELOHS) at preadmission and their risk factors using hospital data.

Methods: A total of 91,468 records of patient's hospital information from a private acute teaching hospital were used for developing a machine learning algorithm relaying on Recursive Feature Elimination with Cross-Validation and Extra Tree Classifier (RFECV-ETC).

A hybrid approach for risk stratification and predictive modelling of 30-days unplanned readmission of comorbid patients with diabetes.

Objectives: When comorbid patients with diabetes have 30-days Unplanned Readmission (URA), they attract more burdens to the healthcare system due to increased cost of treatment, insurance penalties to hospitals, and unavailable bed spaces for new patients. This paper, therefore, aims to develop a risk stratification and a predictive model for identifying patients at various risk severities of 30-days URA.

Methods: Patients records of comorbid patients with diabetes treated with different medications were collected from different hospitals and analysed with Principal Component Analysis (PCA) and Multivariate Logistic Regression (MLR) to determine the probability of 30-days URA, which is classified into very low, low, moderate, high, and very high.

Intelligent decision support with machine learning for efficient management of mechanical ventilation in the intensive care unit - A critical overview.

Background: Effective management of Mechanical Ventilation (MV) is vital for reducing morbidity, mortality, and cost of healthcare.

Objective: This study aims to synthesize evidence for effective MV management through Intelligent decision support (IDS) with Machine Learning (ML).

Method: Databases that include EBSCO, IEEEXplore, Google Scholar, SCOPUS, and the Web of Science were systematically searched to identify studies on IDS for effective MV management regarding Tidal Volume (TV), asynchrony, weaning, and other outcomes such as the risk of Prolonged Mechanical ventilation (PMV).

Intelligent therapeutic decision support for 30 days readmission of diabetic patients with different comorbidities.

The significance of medication therapy in managing comorbid diabetes is vital for maintaining the overall wellness of patients and reducing the cost of healthcare. Thus, using appropriate medication or medication combinations will be necessary for improved person-centred care and reduce complications associated with diagnosis and treatment. This study explains an intelligent decision support framework for managing 30 days unplanned readmission (30_URD) of comorbid diabetes using the Random Forest (RF) algorithm and Bayesian Network (BN) model.