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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 revie...

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Bibliografiske detaljer
Institution som forfatter: SpringerLink (Online service)
Andre forfattere: Finkelstein, Joseph (Editor), Moskovitch, Robert (Editor), Parimbelli, Enea (Editor)
Format: Electronisk eBog
Sprog:engelsk
Udgivet: Cham : Springer Nature Switzerland : Imprint: Springer, 2024.
Udgivelse:1st ed. 2024.
Serier:Lecture Notes in Artificial Intelligence, 14844
Fag:
Artificial intelligence.
Education > Data processing.
Computer networks .
Database management.
Data mining.
Application software.
Artificial Intelligence.
Computers and Education.
Computer Communication Networks.
Database Management.
Data Mining and Knowledge Discovery.
Computer and Information Systems Applications.
Online adgang:https://doi.org/10.1007/978-3-031-66538-7
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Indholdsfortegnelse:
  • -- 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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