Predicting pediatric patient rehabilitation outcomes after spinal deformity surgery with artificial intelligence.

Background: Adolescent idiopathic scoliosis (AIS) is the most common type of scoliosis, affecting 1-4% of adolescents. The Scoliosis Research Society-22R (SRS-22R), a health-related quality-of-life instrument for AIS, has allowed orthopedists to measure subjective patient outcomes before and after corrective surgery beyond objective radiographic measurements. However, research has revealed that there is no significant correlation between the correction rate in major radiographic parameters and improvements in patient-reported outcomes (PROs), making it difficult to incorporate PROs into personalized surgical planning.

Graph theoretic visualization of patient and health worker messaging in the EHR.

Introduction: The electronic health record (EHR) has greatly expanded healthcare communication between patients and health workers. However, the volume and complexity of EHR messages have increased health workers' cognitive load, impeding effective care delivery and contributing to burnout.

Methods: To understand these potential detriments resulting from EHR communication, we analyzed EHR messages sent between patients and health workers at Emory Healthcare, a large academic healthcare system in Atlanta, Georgia.

Early and fair COVID-19 outcome risk assessment using robust feature selection.

Personalized medicine plays an important role in treatment optimization for COVID-19 patient management. Early treatment in patients at high risk of severe complications is vital to prevent death and ventilator use. Predicting COVID-19 clinical outcomes using machine learning may provide a fast and data-driven solution for optimizing patient care by estimating the need for early treatment.

Use of Natural Language Processing of Patient-Initiated Electronic Health Record Messages to Identify Patients With COVID-19 Infection.

Importance: Natural language processing (NLP) has the potential to enable faster treatment access by reducing clinician response time and improving electronic health record (EHR) efficiency.

Objective: To develop an NLP model that can accurately classify patient-initiated EHR messages and triage COVID-19 cases to reduce clinician response time and improve access to antiviral treatment.

Design, Setting, And Participants: This retrospective cohort study assessed development of a novel NLP framework to classify patient-initiated EHR messages and subsequently evaluate the model's accuracy.

Explainable synthetic image generation to improve risk assessment of rare pediatric heart transplant rejection.

Expert microscopic analysis of cells obtained from frequent heart biopsies is vital for early detection of pediatric heart transplant rejection to prevent heart failure. Detection of this rare condition is prone to low levels of expert agreement due to the difficulty of identifying subtle rejection signs within biopsy samples. The rarity of pediatric heart transplant rejection also means that very few gold-standard images are available for developing machine learning models.

Explainable Artificial Intelligence Methods in Combating Pandemics: A Systematic Review.

Despite the myriad peer-reviewed papers demonstrating novel Artificial Intelligence (AI)-based solutions to COVID-19 challenges during the pandemic, few have made a significant clinical impact, especially in diagnosis and disease precision staging. One major cause for such low impact is the lack of model transparency, significantly limiting the AI adoption in real clinical practice. To solve this problem, AI models need to be explained to users.

Domain Adaptation Using Convolutional Autoencoder and Gradient Boosting for Adverse Events Prediction in the Intensive Care Unit.

More than 5 million patients have admitted annually to intensive care units (ICUs) in the United States. The leading causes of mortality are cardiovascular failures, multi-organ failures, and sepsis. Data-driven techniques have been used in the analysis of patient data to predict adverse events, such as ICU mortality and ICU readmission.

COVID-19 Automatic Diagnosis With Radiographic Imaging: Explainable Attention Transfer Deep Neural Networks.

Researchers seek help from deep learning methods to alleviate the enormous burden of reading radiological images by clinicians during the COVID-19 pandemic. However, clinicians are often reluctant to trust deep models due to their black-box characteristics. To automatically differentiate COVID-19 and community-acquired pneumonia from healthy lungs in radiographic imaging, we propose an explainable attention-transfer classification model based on the knowledge distillation network structure.

Regularization of Deep Neural Networks for EEG Seizure Detection to Mitigate Overfitting.

Seizure detection is a major goal for simplifying the workflow of clinicians working on EEG records. Current algorithms can only detect seizures effectively for patients already presented to the classifier. These algorithms are hard to generalize outside the initial training set without proper regularization and fail to capture seizures from the larger population.

Advancing Medical Imaging Informatics by Deep Learning-Based Domain Adaptation.

Introduction: There has been a rapid development of deep learning (DL) models for medical imaging. However, DL requires a large labeled dataset for training the models. Getting large-scale labeled data remains a challenge, and multi-center datasets suffer from heterogeneity due to patient diversity and varying imaging protocols.

Feature Exploration and Causal Inference on Mortality of Epilepsy Patients Using Insurance Claims Data.

Approximately 0.5-1% of the global population is afflicted with epilepsy, a neurological disorder characterized by repeated seizures. Sudden Unexpected Death in Epilepsy (SUDEP) is a poorly understood complication that claims the lives of nearly 1-in-1000 epilepsy patients every year.

Improved Prediction on Heart Transplant Rejection Using Convolutional Autoencoder and Multiple Instance Learning on Whole-Slide Imaging.

Heart transplant rejection is one major threat for the survival of patients with a heart transplant. Endomyocardial biopsies are effective in showing signs of heart transplant rejection even before patients have any symptoms. Manually examining the tissue samples is costly, time-consuming and error-prone.

Response and remediation actions following the detection of Naegleria fowleri in two treated drinking water distribution systems, Louisiana, 2013-2014.

Naegleria fowleri causes the usually fatal disease primary amebic meningoencephalitis (PAM), typically in people who have been swimming in warm, untreated freshwater. Recently, some cases in the United States were associated with exposure to treated drinking water. In 2013, a case of PAM was reported for the first time in association with the exposure to water from a US treated drinking water system colonized with culturable N.

Assessing spatial variability of soil petroleum contamination using visible near-infrared diffuse reflectance spectroscopy.

Visible near-infrared (VisNIR) diffuse reflectance spectroscopy (DRS) is a rapid, non-destructive method for sensing the presence and amount of total petroleum hydrocarbon (TPH) contamination in soil. This study demonstrates the feasibility of VisNIR DRS to be used in the field to proximally sense and then map the areal extent of TPH contamination in soil. More specifically, we evaluated whether a combination of two methods, penalized spline regression and geostatistics could provide an efficient approach to assess spatial variability of soil TPH using VisNIR DRS data from soils collected from an 80 ha crude oil spill in central Louisiana, USA.

Use of portable X-ray fluorescence spectrometry for environmental quality assessment of peri-urban agriculture.

Urban expansion into traditional agricultural lands has augmented the potential for heavy metal contamination of soils. This study examined the utility of field portable X-ray fluorescence (PXRF) spectrometry for evaluating the environmental quality of sugarcane fields near two industrial complexes in Louisiana, USA. Results indicated that PXRF provided quality results of heavy metal levels comparable to traditional laboratory analysis.