Parent strategies to help emerging adults manage stress are associated with their mental health: A dyadic coping perspective.

Emerging adults (EAs) are at high risk for mental health challenges and frequently reach out to their parents for support. Yet little is known about how parents help emerging adults manage and cope with daily stressors and which strategies help and which hinder EA mental health. In this cross-sectional pilot study of students at a 2- and 4-year college (ages 18-25, N = 680, mean age = 19.

A deep semantic matching approach for identifying relevant messages for social media analysis.

There is a growing interest in using social media content for Natural Language Processing applications. However, it is not easy to computationally identify the most relevant set of tweets related to any specific event. Challenging semantics coupled with different ways for using natural language in social media make it difficult for retrieving the most relevant set of data from any social media outlet.

A Gated Recurrent Unit based architecture for recognizing ontology concepts from biological literature.

Background: Annotating scientific literature with ontology concepts is a critical task in biology and several other domains for knowledge discovery. Ontology based annotations can power large-scale comparative analyses in a wide range of applications ranging from evolutionary phenotypes to rare human diseases to the study of protein functions. Computational methods that can tag scientific text with ontology terms have included lexical/syntactic methods, traditional machine learning, and most recently, deep learning.

Using EHR Data to Identify Patient Frailty and Risk for ICU Transfer.

The predictive properties of four definitions of a frailty risk score (FRS) constructed using combinations of nursing flowsheet data, laboratory tests, and ICD-10 codes were examined for time to first intensive care unit (ICU) transfer in medical-surgical inpatients ≥50 years of age. Cox regression modeled time to first ICU transfer and Schemper-Henderson explained variance summarized predictive accuracy of FRS combinations. Modeling by age group and controlling for sex, all FRS measures significantly predicted time to first ICU transfer.

Machine learning for predicting readmission risk among the frail: Explainable AI for healthcare.

Healthcare costs due to unplanned readmissions are high and negatively affect health and wellness of patients. Hospital readmission is an undesirable outcome for elderly patients. Here, we present readmission risk prediction using five machine learning approaches for predicting 30-day unplanned readmission for elderly patients (age ≥ 50 years).

Frailty and In-Hospital Mortality Risk Using EHR Nursing Data.

PurposeThe purpose of this study was to evaluate four definitions of a Frailty Risk Score (FRS) derived from EHR data that includes combinations of biopsychosocial risk factors using nursing flowsheet data or International Classification of Disease, 10th revision (ICD-10) codes and blood biomarkers and its predictive properties for in-hospital mortality in adults ≥50 years admitted to medical-surgical units. In this retrospective observational study and secondary analysis of an EHR dataset, survival analysis and Cox regression models were performed with sociodemographic and clinical covariates. Integrated area under the ROC curve (iAUC) across follow-up time based on Cox modeling was estimated.

Hospital Readmission Outcomes by Frailty Risk in Adults in Behavioral Health Acute Care.

The purpose of the current retrospective study was to determine whether frailty is predictive of 30-day readmission in adults aged ≥50 years who were admitted with a psychiatric diagnosis to a behavioral health hospital from 2013 to 2017. A total of 1,063 patients were included. A 26-item frailty risk score (FRS-26-ICD) was constructed from electronic health record (EHR) data.

A multi-modal approach towards mining social media data during natural disasters - a case study of Hurricane Irma.

Streaming social media provides a real-time glimpse of extreme weather impacts. However, the volume of streaming data makes mining information a challenge for emergency managers, policy makers, and disciplinary scientists. Here we explore the effectiveness of data learned approaches to mine and filter information from streaming social media data from Hurricane Irma's landfall in Florida, USA.

Comparison of a Frailty Risk Score and Comorbidity Indices for Hospital Readmission Using Electronic Health Record Data.

The purpose of the current study was to investigate the predictive properties of five definitions of a frailty risk score (FRS) and three comorbidity indices using data from electronic health records (EHRs) of hospitalized adults aged ≥50 years for 3-day, 7-day, and 30-day readmission, and to identify an optimal model for a FRS and comorbidity combination. Retrospective analysis of the EHR dataset was performed, and multivariable logistic regression and area under the curve (AUC) were used to examine readmission for frailty and comorbidity. The sample ( = 55,778) was mostly female (53%), non-Hispanic White (73%), married (53%), and on Medicare (55%).

A data-driven approach to predicting diabetes and cardiovascular disease with machine learning.

Background: Diabetes and cardiovascular disease are two of the main causes of death in the United States. Identifying and predicting these diseases in patients is the first step towards stopping their progression. We evaluate the capabilities of machine learning models in detecting at-risk patients using survey data (and laboratory results), and identify key variables within the data contributing to these diseases among the patients.