Artificial Intelligence in Medicine 22nd International Conference, AIME 2024, Salt Lake City, UT, USA, July 9–12, 2024, Proceedings, Part I

This two-volume set LNAI 14844-14845 constitutes the refereed proceedings of the 22nd International Conference on Artificial Intelligence in Medicine, AIME 2024, held in Salt Lake City, UT, USA, during July 9-12, 2024.

The 54 full papers and 22 short papers presented in the book were carefully reviewed and selected from 335 submissions.

The papers are grouped in the following topical sections:

Part I: Predictive modelling and disease risk prediction; natural language processing; bioinformatics and omics; and wearable devices, sensors, and robotics.

Part II: Medical imaging analysis; data integration and multimodal analysis; and explainable AI.

.- 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.