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Transparent AI: Developing an Explainable Interface for Predicting Postoperative Complications
Given the sheer volume of surgical procedures and the significant rate of postoperative fatalities, assessing and managing surgical complications has become a critical public health concern.
Global Contrastive Training for Multimodal Electronic Health Records with Language Supervision
To harness the interconnected relationships between medical time series and clinical notes, the framework equips a global contrastive loss, aligning a patient's multimodal feature representations with the corresponding discharge summaries.
Federated learning model for predicting major postoperative complications
Federated learning models achieved comparable AUROC performance to central learning models, except for prolonged ICU stay, where the performance of federated learning models was slightly higher than central learning models at UFH GNV center, but slightly lower at UFH JAX center.
Temporal Cross-Attention for Dynamic Embedding and Tokenization of Multimodal Electronic Health Records
The breadth, scale, and temporal granularity of modern electronic health records (EHR) systems offers great potential for estimating personalized and contextual patient health trajectories using sequential deep learning.
Acute kidney injury prediction for non-critical care patients: a retrospective external and internal validation study
We trained local models for each site (UFH Model trained on UFH, UPMC Model trained on UPMC) and a separate model with a development cohort of patients from both sites (UFH-UPMC Model).
Evaluation of General Large Language Models in Contextually Assessing Semantic Concepts Extracted from Adult Critical Care Electronic Health Record Notes
Methods: We investigated the performance of three general LLMs in understanding and processing real-world clinical notes.
CIS-UNet: Multi-Class Segmentation of the Aorta in Computed Tomography Angiography via Context-Aware Shifted Window Self-Attention
Advancements in medical imaging and endovascular grafting have facilitated minimally invasive treatments for aortic diseases.
APRICOT-Mamba: Acuity Prediction in Intensive Care Unit (ICU): Development and Validation of a Stability, Transitions, and Life-Sustaining Therapies Prediction Model
The acuity state of patients in the intensive care unit (ICU) can quickly change from stable to unstable.
The Potential of Wearable Sensors for Assessing Patient Acuity in Intensive Care Unit (ICU)
In this study, we evaluated the impact of integrating mobility data collected from wrist-worn accelerometers with clinical data obtained from EHR for developing an AI-driven acuity assessment score.
Identifying acute illness phenotypes via deep temporal interpolation and clustering network on physiologic signatures
With clustering analysis for vital signs within six hours of admission, patient phenotypes with distinct pathophysiological signatures and outcomes may support early clinical decisions.
Transformers in Healthcare: A Survey
With Artificial Intelligence (AI) increasingly permeating various aspects of society, including healthcare, the adoption of the Transformers neural network architecture is rapidly changing many applications.
Computable Phenotypes to Characterize Changing Patient Brain Dysfunction in the Intensive Care Unit
We developed algorithms to quantify acute brain dysfunction status including coma, delirium, normal, or death at 12-hour intervals of each ICU admission and to identify acute brain dysfunction phenotypes using continuous acute brain dysfunction status and k-means clustering approach.
Multi-Task Prediction of Clinical Outcomes in the Intensive Care Unit using Flexible Multimodal Transformers
Recent deep learning research based on Transformer model architectures has demonstrated state-of-the-art performance across a variety of domains and tasks, mostly within the computer vision and natural language processing domains.
Sequential Interpretability: Methods, Applications, and Future Direction for Understanding Deep Learning Models in the Context of Sequential Data
Given recent deep learning advancements in highly sequential domains such as natural language processing and physiological signal processing, the need for deep sequential explanations is at an all-time high.
Dynamic Predictions of Postoperative Complications from Explainable, Uncertainty-Aware, and Multi-Task Deep Neural Networks
In a longitudinal cohort study of 56, 242 patients undergoing 67, 481 inpatient surgical procedures at a university medical center, we compared deep learning models with random forests for predicting nine common postoperative complications using preoperative, intraoperative, and perioperative patient data.
Computable Phenotypes of Patient Acuity in the Intensive Care Unit
We gathered two single-center, longitudinal electronic health record datasets for 51, 372 adult ICU patients admitted to the University of Florida Health (UFH) Gainesville (GNV) and Jacksonville (JAX).
Automatic Detection and Classification of Cognitive Distortions in Mental Health Text
In this paper, we present a machine learning framework for the automatic detection and classification of 15 common cognitive distortions in two novel mental health free text datasets collected from both crowdsourcing and a real-world online therapy program.
The Intelligent ICU Pilot Study: Using Artificial Intelligence Technology for Autonomous Patient Monitoring
In this pilot study, we examined the feasibility of using pervasive sensing technology and artificial intelligence for autonomous and granular monitoring of critically ill patients and their environment in the Intensive Care Unit (ICU).
DeepSOFA: A Continuous Acuity Score for Critically Ill Patients using Clinically Interpretable Deep Learning
Traditional methods for assessing illness severity and predicting in-hospital mortality among critically ill patients require time-consuming, error-prone calculations using static variable thresholds.
Hashtag Healthcare: From Tweets to Mental Health Journals Using Deep Transfer Learning
As the popularity of social media platforms continues to rise, an ever-increasing amount of human communication and self- expression takes place online.
Deep EHR: A Survey of Recent Advances in Deep Learning Techniques for Electronic Health Record (EHR) Analysis
The past decade has seen an explosion in the amount of digital information stored in electronic health records (EHR).
