Domestic Violence

Using Neural Multi-task Learning to Extract Substance Abuse Information from Clinical Notes.

Substance abuse carries many negative health consequences. Detailed information about patients' substance abuse history is usually captured in free-text clinical notes. Automatic extraction of substance abuse information is vital to assess patients' risk for developing certain diseases and adverse outcomes. We introduce a novel neural architecture to automatically extract substance abuse informati...

Defining Massive Transfusion in Civilian Pediatric Trauma With Traumatic Brain Injury.

The purpose of this study was to identify an optimal definition of massive transfusion in civilian pediatric trauma with severe traumatic brain injury (TBI) METHODS: Severely injured children (age ≤18 y) with severe TBI in the Trauma Quality Improvement Program research data sets 2015-2016 that received blood products were identified. Data were analyzed using descriptive statistics, Wilcoxon rank-...

Tox_(R)CNN: Deep learning-based nuclei profiling tool for drug toxicity screening.

Toxicity is an important factor in failed drug development, and its efficient identification and prediction is a major challenge in drug discovery. We...

Application of Bioactivity Profile-Based Fingerprints for Building Machine Learning Models.

The volume of high throughput screening data has considerably increased since the beginning of the automated biochemical and cell-based assays era. Th...

Robot assisted minimally invasive esophagectomy (RAMIE) for esophageal cancer.

Worldwide, the standard treatment for locally advanced esophageal cancer with curative intent is perioperative chemotherapy or preoperative chemoradio...

Prediction of findings at screening colonoscopy using a machine learning algorithm based on complete blood counts (ColonFlag).

Adenomatous polyps are a common precursor lesion for colorectal cancer. ColonFlag is a machine- learning-based algorithm that uses basic patient infor...

The Healthy Children, Strong Families 2 (HCSF2) Randomized Controlled Trial Improved Healthy Behaviors in American Indian Families with Young Children.

BACKGROUND: American Indian (AI) families experience a disproportionate risk of obesity due to a number of complex reasons, including poverty, histori...

A computational strategy for finding novel targets and therapeutic compounds for opioid dependence.

Opioids are widely used for treating different types of pains, but overuse and abuse of prescription opioids have led to opioid epidemic in the United...

Ensemble machine learning prediction of posttraumatic stress disorder screening status after emergency room hospitalization.

Posttraumatic stress disorder (PTSD) develops in a substantial minority of emergency room admits. Inexpensive and accurate person-level assessment of ...

Identifying substance use risk based on deep neural networks and Instagram social media data.

Social media may provide new insight into our understanding of substance use and addiction. In this study, we developed a deep-learning method to auto...

A data-driven artificial intelligence model for remote triage in the prehospital environment.

In a mass casualty incident, the factors that determine the survival rate of injured patients are diverse, but one of the key factors is the time for ...

Novel screening criteria for post-traumatic venous thromboembolism by using D-dimer.

AIM: Because severe trauma patients frequently manifest coagulopathy, it is extremely important to detect venous thromboembolism (VTE) in the acute ph...

Deep learning algorithms for detection of critical findings in head CT scans: a retrospective study.

BACKGROUND: Non-contrast head CT scan is the current standard for initial imaging of patients with head trauma or stroke symptoms. We aimed to develop...

Using a Natural Language Processing and Machine Learning Algorithm Program to Analyze Inter-Radiologist Report Style Variation and Compare Variation Between Radiologists When Using Highly Structured Versus More Free Text Reporting.

PURPOSE: To use a natural language processing and machine learning algorithm to evaluate inter-radiologist report variation and compare variation betw...

A Random Forest-Assisted Evolutionary Algorithm for Data-Driven Constrained Multiobjective Combinatorial Optimization of Trauma Systems.

Many real-world optimization problems can be solved by using the data-driven approach only, simply because no analytic objective functions are availab...

Identify and monitor clinical variation using machine intelligence: a pilot in colorectal surgery.

Standardized clinical pathways are useful tool to reduce variation in clinical management and may improve quality of care. However the evidence suppor...

An accessible and efficient autism screening method for behavioural data and predictive analyses.

Autism spectrum disorder is associated with significant healthcare costs, and early diagnosis can substantially reduce these. Unfortunately, waiting t...

Diagnostic performance of an artificial intelligence-driven cardiac-structured reporting system for myocardial perfusion SPECT imaging.

OBJECTIVES: To describe and validate an artificial intelligence (AI)-driven structured reporting system by direct comparison of automatically generate...

Microanalysis of video from a robotic surgical procedure: implications for observational learning in the robotic environment.

Without haptic feedback, robotic surgeons rely on visual processing to interpret the operative field. To provide guidance for teaching in this environ...

A deep convolutional neural network approach for astrocyte detection.

Astrocytes are involved in various brain pathologies including trauma, stroke, neurodegenerative disorders such as Alzheimer's and Parkinson's disease...