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.

Prediction of Sepsis and In-Hospital Mortality Using Electronic Health Records.

Objectives: Our goal was to develop predictive models for sepsis and in-hospital mortality using electronic health records (EHRs). We showcased the efficiency of these algorithms in patients diagnosed with pneumonia, a group that is highly susceptible to sepsis.

Methods: We retrospectively analyzed the Health Facts (HF) dataset to develop models to predict mortality and sepsis using the data from the first few hours after admission.

Correction: Using Statistical and Machine Learning Methods to Evaluate the Prognostic Accuracy of SIRS and qSOFA.

[This corrects the article on p. 139 in vol. 24, PMID: 29770247.

Using Statistical and Machine Learning Methods to Evaluate the Prognostic Accuracy of SIRS and qSOFA.

Objectives: The objective of this study was to compare the performance of two popularly used early sepsis diagnostic criteria, systemic inflammatory response syndrome (SIRS) and quick Sepsis-related Organ Failure Assessment (qSOFA), using statistical and machine learning approaches.

Methods: This retrospective study examined patient visits in Emergency Department (ED) with sepsis related diagnosis. The outcome was 28-day in-hospital mortality.

Settlement behavior of municipal solid waste due to internal and external environmental factors in a lysimeter.

Long-term settlement magnitude is influenced by changes in external and internal factors that control the microbiological activity in the landfill waste body. To improve the understanding of settlement phenomena, it is instructive to study lysimeters filled with MSW. This paper aims to understand the settlement behavior of MSW by correlating internal and external factors that influence waste biodegradation in a lysimeter.