Predicting pediatric emergence delirium using data-driven machine learning applied to electronic health record dataset at a quaternary care pediatric hospital.

Objectives: Pediatric emergence delirium is an undesirable outcome that is understudied. Development of a predictive model is an initial step toward reducing its occurrence. This study aimed to apply machine learning (ML) methods to a large clinical dataset to develop a predictive model for pediatric emergence delirium.

Early prediction of clinical deterioration using data-driven machine-learning modeling of electronic health records.

Objectives: To develop and evaluate a high-dimensional, data-driven model to identify patients at high risk of clinical deterioration from routinely collected electronic health record (EHR) data.

Materials And Methods: In this single-center, retrospective cohort study, 488 patients with single-ventricle and shunt-dependent congenital heart disease <6 months old were admitted to the cardiac intensive care unit before stage 2 palliation between 2014 and 2019. Using machine-learning techniques, we developed the Intensive care Warning Index (I-WIN), which systematically assessed 1028 regularly collected EHR variables (vital signs, medications, laboratory tests, and diagnoses) to identify patients in the cardiac intensive care unit at elevated risk of clinical deterioration.

Early prediction of critical events for infants with single-ventricle physiology in critical care using routinely collected data.

Objective: Critical events are common and difficult to predict among infants with congenital heart disease and are associated with mortality and long-term sequelae. We aimed to achieve early prediction of critical events, that is, cardiopulmonary resuscitation, emergency endotracheal intubation, and extracorporeal membrane oxygenation in infants with single-ventricle physiology before second-stage surgery. We hypothesized that naïve Bayesian models learned from expert knowledge and clinical data can predict critical events early and accurately.

An argument for reporting data standardization procedures in multi-site predictive modeling: case study on the impact of LOINC standardization on model performance.

Objectives: We aimed to gain a better understanding of how standardization of laboratory data can impact predictive model performance in multi-site datasets. We hypothesized that standardizing local laboratory codes to logical observation identifiers names and codes (LOINC) would produce predictive models that significantly outperform those learned utilizing local laboratory codes.

Materials And Methods: We predicted 30-day hospital readmission for a set of heart failure-specific visits to 13 hospitals from 2008 to 2012.

A study of the transferability of influenza case detection systems between two large healthcare systems.

Objectives: This study evaluates the accuracy and transferability of Bayesian case detection systems (BCD) that use clinical notes from emergency department (ED) to detect influenza cases.

Methods: A BCD uses natural language processing (NLP) to infer the presence or absence of clinical findings from ED notes, which are fed into a Bayesain network classifier (BN) to infer patients' diagnoses. We developed BCDs at the University of Pittsburgh Medical Center (BCDUPMC) and Intermountain Healthcare in Utah (BCDIH).

A delta-sarcoglycan gene polymorphism as a risk factor for hypertrophic cardiomyopathy.

Background: The C allele of c.-94C>G polymorphism of the delta-sarcoglycan gene was associated as a risk factor for coronary spasm in Japanese patients with hypertrophic cardiomyopathy (HCM).

Aim: We evaluated whether the c.

Personality, cognitive coping strategies, and adaptational outcomes in a sample from the general Spanish population.

The relationship among scores on two personality dimensions, Emotional Stability and Extraversion, and on two cognitive coping strategies, Positive Thinking and Wishful Thinking, and on the Consequences of Coping scale were examined in 169 Spanish persons (78 men and 91 women; Mage = 36.3 yr., SD = 12.

Use of polymerase chain reaction for the identification of Leptospira sp. in urine of carriers.

The objectives of the present work were to detect Leptospira seropositive animals. The ELISA results report only IgG antibodies, which could be attributable to chronic infections or else, that they are healthy carriers. All polymerase chain reaction positive animals should be considered potential sources of infection.

Emotional stability, positive and wishful thinking, and consequences of coping in a Spanish sample.

The relationship between scores on Emotional Stability and on two cognitive coping strategies-Positive Thinking and Wishful Thinking-and the Consequences of Coping scale were examined in a group of 99 Spanish undergraduates. Positive Thinking was associated with high Emotional Stability and positive consequences, whereas Wishful Thinking was associated with low Emotional Stability and negative consequences.

The five-factor model of personality, subjective well-being, and social adaptation: generalizability to the Spanish context.

The relationships between the five-factor model of personality, subjective well-being, and social adaptation were examined in two Spanish groups, one of 112 undergraduate students and one of 177 participants from the general population. Analyses showed a clear pattern of low but positive associations among scores on well-being, social adaptation, and four of the five factors of personality (Extraversion, Agreeableness, Conscientiousness, and Emotional Stability), very similar to those obtained by previous research in the American context.