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ORCID22nd AIME 2024: Salt Lake City, UT, USA - Part I
- Joseph Finkelstein
, Robert Moskovitch, Enea Parimbelli:
Artificial Intelligence in Medicine - 22nd International Conference, AIME 2024, Salt Lake City, UT, USA, July 9-12, 2024, Proceedings, Part I. Lecture Notes in Computer Science 14844, Springer 2024, ISBN 978-3-031-66537-0
Predictive Modelling and Disease Risk Prediction
- Po-Kuang Chen, Shih-Hsien Sung, Ling Chen:
Applying Gaussian Mixture Model for Clustering Analysis of Emergency Room Patients Based on Intubation Status. 3-10 - Laurent Vouriot, Stanislas Rebaudet, Jean Gaudart, Raquel Ureña:
Bayesian Neural Network to Predict Antibiotic Resistance. 11-16 - András Millinghoffer, Mátyás Antal, Márk Marosi, András Formanek, András Antos, Péter Antal:
Boosting Multitask Decomposition: Directness, Sequentiality, Subsampling, Cross-Gradients. 17-35 - Brian W. Locke, W. Wayne Richards, Jeanette P. Brown, Wanting Cui, Joseph Finkelstein, Krishna M. Sundar, Ramkiran Gouripeddi:
Diagnostic Modeling to Identify Unrecognized Inpatient Hypercapnia Using Health Record Data. 36-45 - Xiangru Chen, Milos Hauskrecht:
Enhancing Hypotension Prediction in Real-Time Patient Monitoring Through Deep Learning: A Novel Application of XResNet with Contrastive Learning and Value Attention Mechanisms. 46-51 - Josip Grguric, Annette ten Teije, Frank van Harmelen:
Evaluating the TMR Model for Multimorbidity Decision Support Using a Community-of-Practice Based Methodology. 52-63 - Beatriz López, David Galera, Abel López-Bermejo, Judit Bassols:
Frequent Patterns of Childhood Overweight from Longitudinal Data on Parental and Early-Life of Infants Health. 64-69 - Paulo Vitor de Campos Souza, Mauro Dragoni:
Fuzzy Neural Network Model Based on Uni-Nullneuron in Extracting Knowledge About Risk Factors of Maternal Health. 70-75 - Shiwei Lin, Shiqiang Tao, Yan Huang, Xiaojin Li, Guo-Qiang Zhang:
Identifying Factors Associated with COVID-19 All-Cause 90-Day Readmission: Machine Learning Approaches. 76-80 - Syed Hamail Hussain Zaidi, Amna Basharat, Muddassar Farooq:
Mining Disease Progression Patterns for Advanced Disease Surveillance. 81-89 - Ben Kurzion, Chia-Hao Shih, Hong Xie, Xin Wang, Kevin S. Xu:
Minimizing Survey Questions for PTSD Prediction Following Acute Trauma. 90-100 - Zuzanna Wójcik, Vania Dimitrova, Lorraine Warrington, Galina Velikova, Kate Absolom:
Patient-Centric Approach for Utilising Machine Learning to Predict Health-Related Quality of Life Changes During Chemotherapy. 101-116 - Ladislav Floris, Daniel Vasata:
Predicting Blood Glucose Levels with LMU Recurrent Neural Networks: A Novel Computational Model. 117-127 - Chiara Dachena, Roberto Gatta, Mariachiara Savino, Stefania Orini, Nicola Acampora, M. Letizia Serra, Stefano Patarnello, Christian Barillaro, Carlotta Masciocchi:
Prediction Modelling and Data Quality Assessment for Nursing Scale in a Big Hospital: A Proposal to Save Resources and Improve Data Quality. 128-137 - Tobias Kropp, Shiva Faeghi, Kunibert Lennerts:
Process Mining for Capacity Planning and Reconfiguration of a Logistics System to Enhance the Intra-Hospital Patient Transport. Case Study. 138-150 - Paul Dubois, Paul-Henry Cournède, Nikos Paragios, Pascal Fenoglietto:
Radiotherapy Dose Optimization via Clinical Knowledge Based Reinforcement Learning. 151-160 - Zhilin Lu, Jingming Liu, Ruihong Luo, Chunping Li:
Reinforcement Learning with Balanced Clinical Reward for Sepsis Treatment. 161-171 - Corinne G. Allaart, Marc X. Makkes, Lea Dijksman, Paul van der Nat, Douwe Biesma, Henri E. Bal, Aart van Halteren:
Secure and Private Vertical Federated Learning for Predicting Personalized CVA Outcomes. 172-181 - Amila Kugic, Akhila Abdulnazar, Anto Knezovic, Stefan Schulz, Markus Kreuzthaler:
Smoking Status Classification: A Comparative Analysis of Machine Learning Techniques with Clinical Real World Data. 182-191 - Bikram De, Mykhailo Sakevych, Vangelis Metsis:
The Impact of Data Augmentation on Time Series Classification Models: An In-Depth Study with Biomedical Data. 192-203 - Minakshi Debnath, Md Shahriar Kabir, Jianyuan Ni, Anne Hee Hiong Ngu:
The Impact of Synthetic Data on Fall Detection Application. 204-209
Natural Language Processing
- Saba Ghanbari Haez, Marina Segala, Patrizio Bellan, Simone Magnolini, Leonardo Sanna, Monica Consolandi, Mauro Dragoni:
A Retrieval-Augmented Generation Strategy to Enhance Medical Chatbot Reliability. 213-223 - Chia-Hsuan Chang, Mary M. Lucas, Yeawon Lee, Christopher C. Yang, Grace Lu-Yao:
Beyond Self-consistency: Ensemble Reasoning Boosts Consistency and Accuracy of LLMs in Cancer Staging. 224-228 - Paloma Rabaey, Johannes Deleu, Stefan Heytens, Thomas Demeester:
Clinical Reasoning over Tabular Data and Text with Bayesian Networks. 229-250 - Deepak Gupta, Dina Demner-Fushman:
Empowering Language Model with Guided Knowledge Fusion for Biomedical Document Re-ranking. 251-260 - Regina Ofori-Boateng, Magaly Aceves-Martins, Nirmalie Wiratunga, Carlos Francisco Moreno-García:
Enhancing Abstract Screening Classification in Evidence-Based Medicine: Incorporating Domain Knowledge into Pre-trained Models. 261-272 - Xubing Hao, Rashmie Abeysinghe, Jay Shi, Licong Cui:
Exploring Pre-trained Language Models for Vocabulary Alignment in the UMLS. 273-278 - Ortal Hirszowicz, Dvir Aran:
ICU Bloodstream Infection Prediction: A Transformer-Based Approach for EHR Analysis. 279-292 - Saurabh Mathur, Veerendra P. Gadekar, Rashika Ramola, Peixin Wang, Ramachandran Thiruvengadam, David M. Haas, Shinjini Bhatnagar, Nitya Wadhwa, Garbhini Study Group, Predrag Radivojac, Himanshu Sinha, Kristian Kersting, Sriraam Natarajan:
Modeling Multiple Adverse Pregnancy Outcomes: Learning from Diverse Data Sources. 293-302 - Yaoqian Sun, Dan Wu, Zikang Chen, Hailing Cai, Jiye An:
OptimalMEE: Optimizing Large Language Models for Medical Event Extraction Through Fine-Tuning and Post-hoc Verification. 303-311 - Waheed Ahmed Abro, Hanane Kteich, Zied Bouraoui:
Self-supervised Segment Contrastive Learning for Medical Document Representation. 312-321 - Brian D. Ondov, Dina Demner-Fushman:
Sentence-Aligned Simplification of Biomedical Abstracts. 322-333 - Vishakha Sharma, Andreas Thalhammer, Amila Kugic, Stefan Schulz, Markus Kreuzthaler:
Sequence-Model-Based Medication Extraction from Clinical Narratives in German. 334-344 - Seibi Kobara, Alireza Rafiei, Masoud Nateghi, Selen Bozkurt, Rishikesan Kamaleswaran, Abeed Sarker:
Social Media as a Sensor: Analyzing Twitter Data for Breast Cancer Medication Effects Using Natural Language Processing. 345-354
Bioinformatics and Omics
- Joung Min Choi, Liqing Zhang:
Breast Cancer Subtype Prediction Model Integrating Domain Adaptation with Semi-supervised Learning on DNA Methylation Profiles. 357-366 - Mohsen Nabian, Zahra Eftekhari, Chi Wah Wong:
CI-VAE for Single-Cell: Leveraging Generative-AI to Enhance Disease Understanding. 367-372 - Yiqing Shen, Outongyi Lv, Houying Zhu, Yu Guang Wang:
ProteinEngine: Empower LLM with Domain Knowledge for Protein Engineering. 373-383
Wearable Devices, Sensors, and Robotics
- Tahsin Kazi, John Oakley, Anh Duong, El Arbi Belfasi, Katherine H. Ingram, Maria Valero:
Advancements in Non-invasive AI-Powered Glucose Monitoring: Leveraging Multispectral Imaging Across Diverse Wavelengths. 387-396 - Marina Andric, Mauro Dragoni, Francesco Ricci:
Anticipating Stress: Harnessing Biomarker Signals from a Wrist-Worn Device for Early Prediction. 397-408 - Abrar S. Alrumayh, Chiu C. Tan:
Improving Reminder Apps for Home Voice Assistants. 409-413
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