Artificial Intelligence in Medicine

Artificial Intelligence in Medicine

22nd International Conference, AIME 2024, Salt Lake City, UT, USA, July 9-12, 2024, Proceedings, Part I

Finkelstein, Joseph; Parimbelli, Enea; Moskovitch, Robert

Springer International Publishing AG

07/2024

418

Mole

9783031665370

15 a 20 dias

Descrição não disponível.
.- Predictive modelling and disease risk prediction.

.- Applying Gaussian Mixture Model for clustering analysis of emergency room patients based on intubation status.

.- Bayesian Neural Network to predict antibiotic resistance.

.- Boosting multitask decomposition: directness, sequentiality, subsampling, cross-gradients.

.- Diagnostic Modeling to Identify Unrecognized Inpatient Hypercapnia Using Health Record Data.

.- Enhancing Hypotension Prediction in Real-time Patient Monitoring Through Deep Learning: A Novel Application of XResNet with Contrastive Learning and Value Attention Mechanisms.

.- Evaluating the TMR model for multimorbidity decision support using a community-of-practice based methodology.

.- Frequent patterns of childhood overweight from longitudinal data on parental and early-life of infants health.

.- Fuzzy neural network model based on uni-nullneuron in extracting knowledge about risk factors of Maternal Health.

.- Identifying Factors Associated with COVID-19 All-Cause 90-Day Readmission: Machine Learning Approaches.

.- Mining Disease Progression Patterns for Advanced Disease Surveillance.

.- Minimizing Survey Questions for PTSD Prediction Following Acute Trauma.

.- Patient-Centric Approach for Utilising Machine Learning to Predict Health-Related Quality of Life Changes during Chemotherapy.

.- Predicting Blood Glucose Levels with LMU Recurrent Neural Networks: A Novel Computational Model.

.- Prediction Modelling and Data Quality Assessment for Nursing Scale in a big hospital: a proposal to save resources and improve data quality.

.- Process Mining for capacity planning and reconfiguration of a logistics system to enhance the intra-hospital patient transport. Case Study..

.- Radiotherapy Dose Optimization via Clinical Knowledge Based Reinforcement Learning.

.- Reinforcement Learning with Balanced Clinical Reward for Sepsis Treatment.

.- Secure and Private Vertical Federated Learning for Predicting Personalized CVA Outcomes.

.- Smoking Status Classification: A Comparative Analysis of Machine Learning Techniques with Clinical Real World Data.

.- The Impact of Data Augmentation on Time Series Classification Models: An In-Depth Study with Biomedical Data.

.- The Impact of Synthetic Data on Fall Detection Application.

.- Natural Language Processing.

.- A Retrieval-Augmented Generation Strategy To Enhance Medical Chatbot Reliability.

.- Beyond Self-Consistency: Ensemble Reasoning Boosts Consistency and Accuracy of LLMs in Cancer Staging.

.- Clinical Reasoning over Tabular Data and Text with Bayesian Networks.

.- Empowering Language Model with Guided Knowledge Fusion for Biomedical Document Re-ranking.

.- Enhancing Abstract Screening Classification in Evidence-Based Medicine: Incorporating domain knowledge into pre-trained models.

.- Exploring Pre-trained Language Models for Vocabulary Alignment in the UMLS.

.- ICU Bloodstream Infection Prediction: A Transformer-Based Approach for EHR Analysis.

.- Modeling multiple adverse pregnancy outcomes: Learning from diverse data sources.

.- OptimalMEE: Optimizing Large Language Models for Medical Event Extraction through Fine-tuning and Post-hoc Verification.

.- Self-Supervised Segment Contrastive Learning for Medical Document Representation 295.

.- Sentence-aligned Simplification of Biomedical Abstracts.

.- Sequence-Model-Based Medication Extraction from Clinical Narratives in German.

.- Social Media as a Sensor: Analyzing Twitter Data for Breast Cancer Medication Effects Using Natural Language Processing.

.- Bioinformatics and omics.

.- Breast cancer subtype prediction model integrating domain adaptation with semi-supervised learning on DNA methylation profiles.

.- CI-VAE for Single-Cell: Leveraging Generative-AI to Enhance Disease Understanding.

.- ProteinEngine: Empower LLM with Domain Knowledge for Protein Engineering.

.- Wearable devices, sensors, and robotics.

.- Advancements in Non-Invasive AI-Powered Glucose Monitoring: Leveraging Multispectral Imaging Across Diverse Wavelengths.

.- Anticipating Stress: Harnessing Biomarker Signals from a Wrist-worn Device for Early Prediction.

.- Improving Reminder Apps for Home Voice Assistants.
Artificial intelligence;Machine learning;Modeling and simulation;Computational biology;Life and medical sciences;Health care information systems;Health informatics;Bioinformatics;Decision support systems;Process control systems;Information retrieval;Semantics and reasoning;Design and analysis of algorithms