Emergency Medicine

Predicting admission for fall-related injuries in older adults using artificial intelligence: A proof-of-concept study.

AIM: Pre-injury frailty has been investigated as a tool to predict outcomes of older trauma patients. Using artificial intelligence principles of machine learning, we aimed to identify a "signature" (combination of clinical variables) that could predict which older adults are at risk of fall-related hospital admission. We hypothesized that frailty, measured using the 5-item modified Frailty Index,...

Using supervised machine learning and ICD10 to identify non-accidental trauma in pediatric trauma patients in the Maryland Health Services Cost Review Commission dataset.

BACKGROUND: Identifying non-accidental trauma (NAT) in pediatric trauma patients is challenging. We developed a machine learning model that uses demographic characteristics and ICD10 codes to detect the first diagnosis of NAT.

Quantitative prediction of toxicological points of departure using two-stage machine learning models: A new approach methodology (NAM) for chemical risk assessment.

Point of departure (POD) is a concept used in risk assessment to calculate the reference dose of exposure that is likely to have no appreciable risk o...

Patch-based feature mapping with generative adversarial networks for auxiliary hip fracture detection.

BACKGROUND: Hip fractures are a significant public health issue, particularly among the elderly population. Pelvic radiographs (PXRs) play a crucial r...

The advantages of lexicon-based sentiment analysis in an age of machine learning.

Assessing whether texts are positive or negative-sentiment analysis-has wide-ranging applications across many disciplines. Automated approaches make i...

Predicting functional outcomes of patients with spontaneous intracerebral hemorrhage based on explainable machine learning models: a multicenter retrospective study.

BACKGROUND: Spontaneous intracerebral hemorrhage (SICH) is the second most common cause of cerebrovascular disease after ischemic stroke, with high mo...

Developing and validating a machine learning model to predict multidrug-resistant -related septic shock.

BACKGROUND: Multidrug-resistant Klebsiella pneumoniae (MDR-KP) infections pose a significant global healthcare challenge, particularly due to the high...

Can ChatGPT 4.0 reliably answer patient frequently asked questions about boxer's fractures?

BACKGROUND: Patients are increasingly turning to the internet, and recently artificial intelligence engines (e.g., ChatGPT), for answers to common med...

Automated diagnosis and classification of metacarpal and phalangeal fractures using a convolutional neural network: a retrospective data analysis study.

BACKGROUND AND PURPOSE:  Hand fractures are commonly presented in emergency departments, yet diagnostic errors persist, leading to potential complicat...

Evaluating the Quality and Readability of Information Provided by Generative Artificial Intelligence Chatbots on Clavicle Fracture Treatment Options.

Introduction Generative artificial intelligence (AI) chatbots, like ChatGPT, have become more competent and prevalent, making their role in patient ed...

Prognostic value of HSP27 in 28-day mortality in septic ICU patients: a retrospective cohort study.

BACKGROUND: This study aimed to investigate the association between serum heat shock protein 27 (HSP27) levels and 28-day mortality in patients with s...

Machine Learning to Detect Cervical Spine Fractures Missed by Radiologists on CT: Analysis Using Seven Award-Winning Models From the 2022 RSNA Cervical Spine Fracture AI Challenge.

Available data on radiologists' missed cervical spine fractures are based primarily on studies using human reviewers to identify errors on reevaluati...

Changes in hematoma volume following aneurysmal subarachnoid hemorrhage and its impact on patient prognosis.

OBJECTIVE: This study aims to investigate the effects of preoperative intracerebral hematoma volume (HVpre), hematoma volume 6-8 days post-surgery (HV...

Evaluation of Generative Artificial Intelligence Models in Predicting Pediatric Emergency Severity Index Levels.

OBJECTIVE: Evaluate the accuracy and reliability of various generative artificial intelligence (AI) models (ChatGPT-3.5, ChatGPT-4.0, T5, Llama-2, Mis...

Revolutionizing Health Care: The Transformative Impact of Large Language Models in Medicine.

Large language models (LLMs) are rapidly advancing medical artificial intelligence, offering revolutionary changes in health care. These models excel ...

External validation of an artificial intelligence model using clinical variables, including ICD-10 codes, for predicting in-hospital mortality among trauma patients: a multicenter retrospective cohort study.

Artificial intelligence (AI) is being increasingly applied in healthcare to improve patient care and clinical outcomes. We previously developed an AI ...

Leveraging machine learning algorithms to forecast delayed cerebral ischemia following subarachnoid hemorrhage: a systematic review and meta-analysis of 5,115 participants.

It is feasible to predict delayed cerebral ischemia (DCI) after aneurysmal subarachnoid hemorrhage (aSAH) using Artificial intelligence (AI) algorithm...

Seismic anisotropy prediction using ML methods: A case study on an offshore carbonate oilfield.

Estimating seismic anisotropy parameters, such as Thomson's parameters, is crucial for investigating fractured and finely layered geological media. Ho...

Isolation, identification and characteristics of from diseased rainbow trout ().

is an opportunistic pathogen that can infect humans, animals and aquatic species, which is widely distributed in different aquatic environments and p...

Countering AI-powered disinformation through national regulation: learning from the case of Ukraine.

Advances in the use of AI have led to the emergence of a greater variety of forms disinformation can take and channels for its proliferation. In this ...