Detecting irregularities in randomized controlled trials using machine learning.

Background: Over the course of a clinical trial, irregularities may arise in the data. Trialists implement human-intensive, expensive central statistical monitoring procedures to identify and correct these irregularities before the results of the trial are analyzed and disseminated. Machine learning algorithms have shown promise for identifying center-level irregularities in multi-center clinical trials with minimal human intervention.

Breast cancer learning health system: Patient information from a data and analytics platform characterizes care provided.

Purpose: In a learning health system (LHS), data gathered from clinical practice informs care and scientific investigation. To demonstrate how a novel data and analytics platform can enable an LHS at a regional cancer center by characterizing the care provided to breast cancer patients.

Methods: Socioeconomic information, tumor characteristics, treatments and outcomes were extracted from the platform and combined to characterize the patient population and their clinical course.

Developing a Data and Analytics Platform to Enable a Breast Cancer Learning Health System at a Regional Cancer Center.

Purpose: This study documents the creation of automated, longitudinal, and prospective data and analytics platform for breast cancer at a regional cancer center. This platform combines principles of data warehousing with natural language processing (NLP) to provide the integrated, timely, meaningful, high-quality, and actionable data required to establish a learning health system.

Methods: Data from six hospital information systems and one external data source were integrated on a nightly basis by automated extract/transform/load jobs.

Association Between Delayed Discharge From Acute Care and Rehabilitation Outcomes and Length of Stay: A Retrospective Cohort Study.

Objective: To examine the association between discharge delays from acute to rehabilitation care because of capacity strain in the rehabilitation units, patient length of stay (LOS), and functional outcomes in rehabilitation.

Design: Retrospective cohort study using an instrumental variable to remove potential biases because of unobserved patient characteristics.

Setting: Two campuses of a hospital network providing inpatient acute and rehabilitation care.

A contemporary review of machine learning in otolaryngology-head and neck surgery.

One of the key challenges with big data is leveraging the complex network of information to yield useful clinical insights. The confluence of massive amounts of health data and a desire to make inferences and insights on these data has produced a substantial amount of interest in machine-learning analytic methods. There has been a drastic increase in the otolaryngology literature volume describing novel applications of machine learning within the past 5 years.

Liquid-Infused Silicone As a Biofouling-Free Medical Material.

There is a dire need for infection prevention strategies that do not require the use of antibiotics, which exacerbate the rise of multi- and pan-drug resistant infectious organisms. An important target in this area is the bacterial attachment and subsequent biofilm formation on medical devices (e.g.