Artificial Intelligence in Medicine: 22nd International Conference, AIME 2024, S

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The Nile on eBay FREE SHIPPING UK WIDE Artificial Intelligence in Medicine by Joseph Finkelstein, Robert Moskovitch, Enea Parimbelli 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. FORMAT Paperback CONDITION Brand New 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. Details ISBN 3031665376 Author Enea Parimbelli Publisher Springer International Publishing AG Year 2024 ISBN-10 3031665376 ISBN-13 9783031665370 Format Paperback Imprint Springer International Publishing AG Subtitle 22nd International Conference, AIME 2024, Salt Lake City, UT, USA, July 9–12, 2024, Proceedings, Part I Place of Publication Cham Country of Publication Switzerland Edited by Enea Parimbelli Series Number 14844 Audience Professional & Vocational Edition Description 2024 ed. Edition 2024th Pages 418 Publication Date 2024-07-26 DEWEY 610.28563 UK Release Date 2024-07-26 Country of Origin NL Product Class Description Computing: Professional & Programming Series Lecture Notes in Computer Science Alternative 9783031665387 Illustrations 106 Illustrations, color; 19 Illustrations, black and white We've got this At The Nile, if you're looking for it, we've got it. 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