Multimorbidity in neurodegenerative diseases: a network analysis.

The socioeconomic costs of neurodegenerative diseases (NDs) are highly affected by comorbidities. This study aims to enhance our understanding of the prevalent complications of NDs through the lens of network analysis. A multimorbidity network (MN) was constructed based on a longitudinal EHR dataset of 93,647,498 diagnoses of 824,847 patients.

A study of "left against medical advice" emergency department patients: an optimized explainable artificial intelligence framework.

The issue of left against medical advice (LAMA) patients is common in today's emergency departments (EDs). This issue represents a medico-legal risk and may result in potential readmission, mortality, or revenue loss. Thus, understanding the factors that cause patients to "leave against medical advice" is vital to mitigate and potentially eliminate these adverse outcomes.

A novel hyperparameter search approach for accuracy and simplicity in disease prediction risk scoring.

Objective: Develop a novel technique to identify an optimal number of regression units corresponding to a single risk point, while creating risk scoring systems from logistic regression-based disease predictive models. The optimal value of this hyperparameter balances simplicity and accuracy, yielding risk scores of small scale and high accuracy for patient risk stratification.

Materials And Methods: The proposed technique applies an adapted line search across all potential hyperparameter values.

Evaluation of food waste treatment techniques using the complex q-rung orthopair fuzzy generalized TODIM method with weighted power geometric operator.

Food waste has received wide attention due to its hazardous environmental effects, such as soil, water, and air pollution. Evaluating food waste treatment techniques is imperative to realize environmental sustainability. This study proposes an integrated framework, the complex q-rung orthopair fuzzy-generalized TODIM (an acronym in Portuguese for interactive and multi-criteria decision-making) method with weighted power geometric operator, to assess the appropriate technique for food waste.

An explanatory analytics framework for early detection of chronic risk factors in pandemics.

Timely decision-making in national and global health emergencies such as pandemics is critically important from various aspects. Especially, early identification of risk factors of contagious viral diseases can lead to efficient management of limited healthcare resources and saving lives by prioritizing at-risk patients. In this study, we propose a hybrid artificial intelligence (AI) framework to identify major chronic risk factors of novel, contagious diseases as early as possible at the time of pandemics.

An analytical approach to evaluate the impact of age demographics in a pandemic.

The time required to identify and confirm risk factors for new diseases and to design an appropriate treatment strategy is one of the most significant obstacles medical professionals face. Traditionally, this approach entails several clinical studies that may last several years, during which time strict preventative measures must be in place to contain the epidemic and limit the number of fatalities. Analytical tools may be used to direct and accelerate this process.

Predicting and Mitigating Freshmen Student Attrition: A Local-Explainable Machine Learning Framework.

With the emergence of novel methods for improving machine learning (ML) transparency, traditional decision-support-focused information systems seem to need an upgrade in their approach toward providing more actionable insights for practitioners. Particularly, given the complex decision-making process of humans, using insights obtained from group-level interpretation of ML models for designing individual interventions may lead to mixed results. The present study proposes a hybrid ML framework by integrating established predictive and explainable ML approaches for decision support systems involving the prediction of human decisions and designing individualized interventions accordingly.

A Bayesian belief network-based analytics methodology for early-stage risk detection of novel diseases.

During a pandemic, medical specialists have substantial challenges in discovering and validating new disease risk factors and designing effective treatment strategies. Traditionally, this approach entails several clinical studies and trials that might last several years, during which strict preventive measures are enforced to manage the outbreak and limit the death toll. Advanced data analytics technologies, on the other hand, could be utilized to monitor and expedite the procedure.

A Machine Learning Framework for Assessing the Risk of Venous Thromboembolism in Patients Undergoing Hip or Knee Replacement.

Venous thromboembolism (VTE) is a well-recognized complication that is prevalent in patients undergoing major orthopedic surgery (e.g., total hip arthroplasty and total knee arthroplasty).

Relationship between electronic health records strategy and user satisfaction: a longitudinal study using clinicians' online reviews.

Objective: We investigated how the electronic health records (EHRs) strategies concerning EHR sourcing and vendor switching impact user satisfaction over time.

Materials And Methods: This study used a novel longitudinal dataset created by scraping clinicians' Glassdoor.com reviews on 109 US health systems from 2012 to 2017 and combining it with the Healthcare Information and Management Systems Society (HIMSS) database.

An explanatory machine learning framework for studying pandemics: The case of COVID-19 emergency department readmissions.

One of the major challenges that confront medical experts during a pandemic is the time required to identify and validate the risk factors of the novel disease and to develop an effective treatment protocol. Traditionally, this process involves numerous clinical trials that may take up to several years, during which strict preventive measures must be in place to control the outbreak and reduce the deaths. Advanced data analytics techniques, however, can be leveraged to guide and speed up this process.

A critical analysis of COVID-19 research literature: Text mining approach.

Objective: Among the stakeholders of COVID-19 research, clinicians particularly experience difficulty keeping up with the deluge of SARS-CoV-2 literature while performing their much needed clinical duties. By revealing major topics, this study proposes a text-mining approach as an alternative to navigating large volumes of COVID-19 literature.

Materials And Methods: We obtained 85,268 references from the NIH COVID-19 Portfolio as of November 21.

Derivation and Validation of Essential Predictors and Risk Index for Early Detection of Diabetic Retinopathy Using Electronic Health Records.

Diabetic retinopathy (DR) is a leading cause for blindness among working-aged adults. The growing prevalence of diabetes urges for cost-effective tools to improve the compliance of eye examinations for early detection of DR. The objective of this research is to identify essential predictors and develop predictive technologies for DR using electronic health records.

A novel diffusion-based model for estimating cases, and fatalities in epidemics: The case of COVID-19.

While the COVID-19 pandemic is still ongoing in a majority of countries, a wealth of literature published in reputable journals attempted to model the spread of the disease. A vast majority of these studies dealt with compartmental models such as susceptible-infected-recovered (SIR) model. Although these models are rather simple, intuitive, and insightful, we argue that they do not necessarily provide a good enough fit to the reported data, which are usually reported in the form of daily fatalities and cases during pandemics.

No Place Like Home: Cross-National Data Analysis of the Efficacy of Social Distancing During the COVID-19 Pandemic.

Background: In the absence of a cure in the time of a pandemic, social distancing measures seem to be the most effective intervention to slow the spread of disease. Various simulation-based studies have been conducted to investigate the effectiveness of these measures. While those studies unanimously confirm the mitigating effect of social distancing on disease spread, the reported effectiveness varies from 10% to more than 90% reduction in the number of infections.

Research Themes and Trends in Ten Top-Ranked Nephrology Journals: A Text Mining Analysis.

Background: Nephrology research is expanding, and harnessing the much-needed information and data for the practice of evidence-based medicine is becoming more challenging. In this study, we used the natural language processing and text mining approach to mitigate some of these challenges.

Methods: We analyzed 17,412 abstracts from the top-10 nephrology journals over 10 years (2007-2017) by using latent semantic analysis and topic analysis.

Post-acute care referral in United States of America: a multiregional study of factors associated with referral destination in a cohort of patients with coronary artery bypass graft or valve replacement.

Background: The use of post-acute care (PAC) for cardiovascular conditions is highly variable across geographical regions. Although PAC benefits include lower readmission rates, better clinical outcomes, and lower mortality, referral patterns vary widely, raising concerns about substandard care and inflated costs. The objective of this study is to identify factors associated with PAC referral decisions at acute care discharge.

Examining the effect of prescription sequence on developing adverse drug reactions: The case of renal failure in diabetic patients.

Objectives: While the effect of medications in development of Adverse Drug Reactions (ADRs) have been widely studied in the past, the literature lacks sufficient coverage in investigating whether the sequence in which [ADR-prone] drugs are prescribed (and administered) can increase the chances of ADR development. The present study investigates this potential effect by applying emergent sequential pattern mining techniques to electronic health records.

Materials And Methods: Using longitudinal medication and diagnosis records from more than 377,000 diabetic patients, in this study, we assessed the possible effect of prescription sequences in developing acute renal failure as a prevalent ADR among this group of patients.

Medical informatics research trend analysis: A text mining approach.

The objective of this research is to identify major subject areas of medical informatics and explore the time-variant changes therein. As such it can inform the field about where medical informatics research has been and where it is heading. Furthermore, by identifying subject areas, this study identifies the development trends and the boundaries of medical informatics as an academic field.

An Automated Grading and Diagnosis System for Evaluation of Dry Eye Syndrome.

This article describes methods used to determine the severity of Dry Eye Syndrome (DES) based on Oxford Grading Schema (OGS) automatically by developing and applying a decider model. The number of dry punctate dots occurred on corneal surface after corneal fluorescein staining can be used as a diagnostic indicator of DES severity according to OGS; however, grading of DES severity exactly by carefully assessing these dots is a rather difficult task for humans. Taking into account that current methods are also subjectively dependent on the perception of the ophtalmologists coupled with the time and resource intensive requirements, enhanced diagnosis techniques would greatly contribute to clinical assessment of DES.

Predicting hospital readmission for lupus patients: An RNN-LSTM-based deep-learning methodology.

Hospital readmission is one of the critical metrics used for measuring the performance of hospitals. The HITECH Act imposes penalties when patients are readmitted to hospitals if they are diagnosed with one of the six conditions mentioned in the Act. However, patients diagnosed with lupus are the sixth highest in terms of rehospitalization.

A chronological pharmacovigilance network analytics approach for predicting adverse drug events.

Objectives: This study extends prior research by combining a chronological pharmacovigilance network approach with machine-learning (ML) techniques to predict adverse drug events (ADEs) based on the drugs' similarities in terms of the proteins they target in the human body. The focus of this research, though, is particularly centered on predicting the drug-ADE associations for a set of 8 common and high-risk ADEs.

Materials And Methods: large collection of annotated MEDLINE biomedical articles was used to construct a drug-ADE network, and the network was further equipped with information about drugs' target proteins.

Text-mining analysis of mHealth research.

In recent years, because of the advancements in communication and networking technologies, mobile technologies have been developing at an unprecedented rate. mHealth, the use of mobile technologies in medicine, and the related research has also surged parallel to these technological advancements. Although there have been several attempts to review mHealth research through manual processes such as systematic reviews, the sheer magnitude of the number of studies published in recent years makes this task very challenging.