Artificial Intelligence in Medicine [electronic resource] : 22nd International Conference, AIME 2024, Salt Lake City, UT, USA, July 9-12, 2024, Proceedings, Part I / edited by Joseph Finkelstein, Robert Moskovitch, Enea Parimbelli.

-- 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.

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.

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