This section focuses on recent global studies and discoveries in the various fields of healthcare.
Fibromyalgia FM is a multifaceted chronic pain disorder presenting with pain throughout the musculoskeletal system alongside chronic fatiguesleep disturbances and cognitive impairments Current diagnostic criteria primarily rely on symptom questionnaires with no specific laboratory markers or definitive tests making diagnosis subjective and often de...
Large language models LLMs have shown incredible promise in medicine While LLMs may be particularly useful in areas requiring extensive review of clinical records their use remains limited
Outbreaks of healthcareassociated infections HAI result in substantial patient morbidity and mortality mitigation efforts by infection prevention teams have the potential to curb outbreaks and prevent transmission
Accurate classification of pulmonary nodules is essential for early lung cancer diagnosis yet the limited spatial information provided by a single computed tomography slice may hinder the characterization
Contrastive learning is a widely adopted selfsupervised pretraining strategy yet its dependence on cohort composition remains underexplored We present Contrasting by Augmented Patient
We developed and deploy a real time electronic health record integrated machine learning phenotype to identify emergency department patients with opioid use disorder for prospective clinical trial
Outbreaks of healthcareassociated infections HAI result in substantial patient morbidity and mortality mitigation efforts by infection prevention teams have the potential to curb outbreaks and prevent transmission to additional patients The incorporation of whole genome sequencing WGS surveillance of suspected highrisk pathogens often identifies ou...
Accurate classification of pulmonary nodules is essential for early lung cancer diagnosis yet the limited spatial information provided by a single computed tomography slice may hinder the characterization of complex nodule morphology Although multiview strategies can capture complementary anatomical information conventional fusion methods often fai...
Contrastive learning is a widely adopted selfsupervised pretraining strategy yet its dependence on cohort composition remains underexplored We present Contrasting by Augmented Patient Electrocardiograms CAPE foundation model and pretrain on four cohorts n from diverse populations across three continents
We developed and deploy a real time electronic health record integrated machine learning phenotype to identify emergency department patients with opioid use disorder for prospective clinical trial screening and buprenorphine initiation We conducted a multi phase study across three emergency departments in a single United States health
