Overview

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.

Full Product Details

Author:   Joseph Finkelstein ,  Robert Moskovitch ,  Enea Parimbelli
Publisher:   Springer International Publishing AG
Imprint:   Springer International Publishing AG
Edition:   2024 ed.
Volume:   14844
ISBN:  

9783031665370

ISBN 10:   3031665376
Pages:   418
Publication Date:   26 July 2024
Audience:   Professional and scholarly ,  Professional & Vocational
Format:   Paperback
Publisher's Status:   Active
Availability:   Manufactured on demand  
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Table of Contents

.- 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.

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