Forecasting school violence risk with incomplete interview data: an automated assessment approach.

Objectives: School violence risk prevention in the United States relies on manual assessments that are time-consuming and subjective. We developed a machine learning algorithm named Automated RIsk Assessment (ARIA), using natural language processing (NLP) to find linguistic patterns in standardized interview questions that can predict risk of aggression. Our goal was to evaluate the incremental change in performance with the addition of each question to simulate situations where interviews cannot be completed.

Implementation of Machine Learning Pipelines for Clinical Practice: Development and Validation Study.

Background: Artificial intelligence (AI) technologies, such as machine learning and natural language processing, have the potential to provide new insights into complex health data. Although powerful, these algorithms rarely move from experimental studies to direct clinical care implementation.

Objective: We aimed to describe the key components for successful development and integration of two AI technology-based research pipelines for clinical practice.

Standardizing electronic health record ventilation data in the pediatric long-term mechanical ventilator-dependent population.

Background: Sharing data across institutions is critical to improving care for children who are using long-term mechanical ventilation (LTMV). Mechanical ventilation data are complex and poorly standardized. This lack of data standardization is a major barrier to data sharing.

Automated prediction of extubation success in extremely preterm infants: the APEX multicenter study.

Background: Extremely preterm infants are frequently subjected to mechanical ventilation. Current prediction tools of extubation success lacks accuracy.

Methods: Multicenter study including infants with birth weight ≤1250 g undergoing their first extubation attempt.

Cardiorespiratory behavior of preterm infants receiving continuous positive airway pressure and high flow nasal cannula post extubation: randomized crossover study.

Background: Nasal continuous positive airway pressure (NCPAP) and high flow nasal cannula (HFNC) are modes of non-invasive respiratory support commonly used after extubation in extremely preterm infants. However, the cardiorespiratory physiology of these infants on each mode is unknown.

Methods: Prospective, randomized crossover study in infants with birth weight ≤1250 g undergoing their first extubation attempt.

Undersampling and Bagging of Decision Trees in the Analysis of Cardiorespiratory Behavior for the Prediction of Extubation Readiness in Extremely Preterm Infants.

Extremely preterm infants often require endotracheal intubation and mechanical ventilation during the first days of life. Due to the detrimental effects of prolonged invasive mechanical ventilation (IMV), clinicians aim to extubate infants as soon as they deem them ready.Unfortunately, existing strategies for prediction of extubation readiness vary across clinicians and institutions, and lead to high reintubation rates.

APEX_SCOPE: A graphical user interface for visualization of multi-modal data in inter-disciplinary studies.

In multi-disciplinary studies, different forms of data are often collected for analysis. For example, APEX, a study on the automated prediction of extubation readiness in extremely preterm infants, collects clinical parameters and cardiorespiratory signals. A variety of cardiorespiratory metrics are computed from these signals and used to assign a cardiorespiratory pattern at each time.

A semi-Markov chain approach to modeling respiratory patterns prior to extubation in preterm infants.

After birth, extremely preterm infants often require specialized respiratory management in the form of invasive mechanical ventilation (IMV). Protracted IMV is associated with detrimental outcomes and morbidities. Premature extubation, on the other hand, would necessitate reintubation which is risky, technically challenging and could further lead to lung injury or disease.

Prediction of Extubation readiness in extremely preterm infants by the automated analysis of cardiorespiratory behavior: study protocol.

Background: Extremely preterm infants (≤ 28 weeks gestation) commonly require endotracheal intubation and mechanical ventilation (MV) to maintain adequate oxygenation and gas exchange. Given that MV is independently associated with important adverse outcomes, efforts should be made to limit its duration. However, current methods for determining extubation readiness are inaccurate and a significant number of infants fail extubation and require reintubation, an intervention that may be associated with increased morbidities.

Automated ongoing data validation and quality control of multi-institutional studies.

This paper addresses the problem of ensuring the validity and quality of data in ongoing multi-disciplinary studies where data acquisition spans several geographical sites. It describes an automated validation and quality control procedure that requires no user supervision and monitors data acquired from different locations before analysis. The procedure is illustrated for the Automated Prediction of Extubation readiness (APEX) project in preterm infants, where acquisition of clinical and cardiorespiratory data occurs at 6 sites using different equipment and personnel.

Correlation of clinical parameters with cardiorespiratory behavior in successfully extubated extremely preterm infants.

Extremely preterm infants (gestational age ≤ 28 weeks) often require EndoTracheal Tube-Invasive Mechanical Ventilation (ETT-IMV) to survive. Clinicians wean infants off ETT-IMV as early as possible using their judgment and clinical information. However, assessment of extubation readiness is not accurate since 20 to 40% of preterm infants fail extubation.

Organizational principles of cloud storage to support collaborative biomedical research.

This paper describes organizational guidelines and an anonymization protocol for the management of sensitive information in interdisciplinary, multi-institutional studies with multiple collaborators. This protocol is flexible, automated, and suitable for use in cloud-based projects as well as for publication of supplementary information in journal papers. A sample implementation of the anonymization protocol is illustrated for an ongoing study dealing with Automated Prediction of EXtubation readiness (APEX).