The directors and members of the Intelligent Clinical Care Center (IC3) have published over 1,500 articles in peer-reviewed, scholarly journals. These include journals such as Nature Reviews Nephrology, Scientific Reports, JAMA Internal Medicine, JAMA Network Open, The Lancet Digital Health, Medical Image Analysis, BMJ Health & Care Informatics, PLOS ONE, Kidney International, and Surgery.
Listed below are recent publications from some of the IC3 member labs. For more information on publications by specific IC3 members or labs, please refer to the IC3 Members page or the IC3 Labs page.
PRISMAP
IC3 co-director Azra Bihorac, associate directors of research Tyler Loftus and Tezcan Ozrazgat Baslanti, and assistant director of clinical AI Benjamin Shickel are all part of the Precision and Intelligent Systems in Medicine Research Partnership (PRISMAP) in the UF College of Medicine.
A case of Crimean-Congo haemorrhagic fever complicated with portal vein thrombosis and hemophagocytosis
CONCLUSION: The clinical presentation of CCHF can range from self-limiting flu-like to severe symptoms possibly fatal. Acute portal vein embolism is a rare complication that has not been reported before…
Identifying acute illness phenotypes via deep temporal interpolation and clustering network on physiologic signatures
Using clustering analysis for early vital signs, unique patient phenotypes with distinct pathophysiological signatures and clinical outcomes may be revealed and support early clinical decision-making. Phenotyping using early vital signs…
Dynamic Delirium Prediction in the Intensive Care Unit using Machine Learning on Electronic Health Records
Delirium is a syndrome of acute brain failure which is prevalent amongst older adults in the Intensive Care Unit (ICU). Incidence of delirium can significantly worsen prognosis and increase mortality,…
Use of artificial intelligence in critical care: opportunities and obstacles
CONCLUSIONS: AI-based CDSS are evolving and are here to stay. It is our obligation to be good shepherds of their use and further development.
i-Heal Lab
IC3 co-director Parisa Rashidi leads the Intelligent Health Systems (i-Heal) Lab in the UF Herbert Wertheim College of Engineering.
Identifying acute illness phenotypes via deep temporal interpolation and clustering network on physiologic signatures
Using clustering analysis for early vital signs, unique patient phenotypes with distinct pathophysiological signatures and clinical outcomes may be revealed and support early clinical decision-making. Phenotyping using early vital signs…
Dynamic Delirium Prediction in the Intensive Care Unit using Machine Learning on Electronic Health Records
Delirium is a syndrome of acute brain failure which is prevalent amongst older adults in the Intensive Care Unit (ICU). Incidence of delirium can significantly worsen prognosis and increase mortality,…
A Network Analysis of Digital Clock Drawing for Command and Copy Conditions
Graphomotor and time-based variables from the digital Clock Drawing Test (dCDT) characterize cognitive functions. However, no prior publications have quantified the strength of the associations between digital clock variables as…
Diurnal Pain Classification in Critically Ill Patients using Machine Learning on Accelerometry and Analgesic Data
Quantifying pain in patients admitted to intensive care units (ICUs) is challenging due to the increased prevalence of communication barriers in this patient population. Previous research has posited a positive…
CMI Lab
IC3 associate director of research Pinaki Sarder leads the Computational Microscopy Imaging (CMI) Lab in the UF College of Medicine.
Ontology-based modeling, integration, and analysis of heterogeneous clinical, pathological, and molecular kidney data for precision medicine
Many data resources generate, process, store, or provide kidney related molecular, pathological, and clinical data. Reference ontologies offer an opportunity to support knowledge and data integration. The Kidney Precision Medicine…
Nascent shifts in renal cellular metabolism, structure, and function due to chronic empagliflozin in prediabetic mice
Sodium-glucose cotransporter, type 2 inhibitors (SGLT2i) are emerging as the gold standard for treatment of type 2 diabetes (T2D) with renal protective benefits independent of glucose lowering. We took a…
ComPRePS: An Automated Cloud-based Image Analysis tool to democratize AI in Digital Pathology
Artificial intelligence (AI) has extensive applications in a wide range of disciplines including healthcare and clinical practice. Advances in high-resolution whole-slide brightfield microscopy allow for the digitization of histologically stained…
Correlating Deep Learning-Based Automated Reference Kidney Histomorphometry with Patient Demographics and Creatinine
CONCLUSIONS: Using DL, we automated precise measurements of kidney histomorphometric features. In the reference kidney tissue, several histomorphometric features demonstrated significant correlation to patient demographics, SCr, and eGFR. DL tools…
SMILE Lab
IC3 associate director of education Ruogu Fang leads the Smart Medical Informatics Learning and Evaluation (SMILE) Lab in the UF Herbert Wertheim College of Engineering.
DeepDynaForecast: Phylogenetic-informed graph deep learning for epidemic transmission dynamic prediction
In the midst of an outbreak or sustained epidemic, reliable prediction of transmission risks and patterns of spread is critical to inform public health programs. Projections of transmission growth or…
Neuron-level explainable AI for Alzheimer's Disease assessment from fundus images
Alzheimer’s Disease (AD) is a progressive neurodegenerative disease and the leading cause of dementia. Early diagnosis is critical for patients to benefit from potential intervention and treatment. The retina has…
Emergence of Emotion Selectivity in Deep Neural Networks Trained to Recognize Visual Objects
Recent neuroimaging studies have shown that the visual cortex plays an important role in representing the affective significance of visual input. The origin of these affect-specific visual representations is debated:…
Precise and Rapid Whole-Head Segmentation from Magnetic Resonance Images of Older Adults using Deep Learning
Whole-head segmentation from Magnetic Resonance Images (MRI) establishes the foundation for individualized computational models using finite element method (FEM). This foundation paves the path for computer-aided solutions in fields, particularly…