Artificial intelligence in medicine. Conference on Artificial Intelligence in Medicine (2005- )最新文献

Comparative Evaluation of Clinical Large Language Models and Machine Learning to Predict Antimicrobial Resistance in Hospital-Onset Sepsis. 临床大语言模型和机器学习预测医院源性败血症抗菌素耐药性的比较评价

Artificial intelligence in medicine. Conference on Artificial Intelligence in Medicine (2005- ) Pub Date : 2025-06-01 Epub Date: 2025-06-23 DOI: 10.1007/978-3-031-95838-0_7

Scott A Cohen, Ziyi Chen, Jiang Bian, Christina Boucher, Yonghui Wu, Mattia Prosperi

{"title":"Comparative Evaluation of Clinical Large Language Models and Machine Learning to Predict Antimicrobial Resistance in Hospital-Onset Sepsis.","authors":"Scott A Cohen, Ziyi Chen, Jiang Bian, Christina Boucher, Yonghui Wu, Mattia Prosperi","doi":"10.1007/978-3-031-95838-0_7","DOIUrl":"10.1007/978-3-031-95838-0_7","url":null,"abstract":"Approaches to guide empiric antimicrobial therapy are needed, especially in critically ill populations with prevalent antimicrobial resistance (AMR). While artificial intelligence shows promise in predicting AMR, scalable and generalizable prediction models are essential for broad clinical adoption. We utilized a publicly available clinical large language model (LLM), Gatortron, in comparison to traditional machine learning, to predict AMR and methicillin-resistant Staphylococcus aureus (MRSA)-specific patterns within a hospital-onset sepsis cohort using electronic health record (EHR) data available at time of illness onset. EHR data from approximately 150,000 hospitalizations with a documented bacterial infection at a large tertiary care healthcare system between 2010 and 2023 were examined. Among 2,019 eligible hospital-onset sepsis encounters, an AMR pathogen was identified in 911 (45%) and MRSA was isolated in 234 (26%). LLMs outperformed traditional models in predicting MRSA, achieving an AUC of 0.73 compared to 0.66 for the best traditional ML model, with superior F1 scores (0.43 vs. 0.16 for ML). Negative predictive value for MRSA prediction using LLM was at least 90% across majority of infection presentations. The LLM's superior prediction using a relatively simplified feature set demonstrates the potential of leveraging EHR data for early resistance prediction, though further refinement is needed to enhance sensitivity and clinical applicability.","PeriodicalId":72303,"journal":{"name":"Artificial intelligence in medicine. Conference on Artificial Intelligence in Medicine (2005- )","volume":"15734 ","pages":"65-76"},"PeriodicalIF":0.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12433606/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145071332","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Reinforcement Learning on Dyads to Enhance Medication Adherence. 强化学习对提高药物依从性的影响。

Artificial intelligence in medicine. Conference on Artificial Intelligence in Medicine (2005- ) Pub Date : 2025-06-01 Epub Date: 2025-06-23 DOI: 10.1007/978-3-031-95838-0_48

Ziping Xu, Hinal Jajal, Sung Won Choi, Inbal Nahum-Shani, Guy Shani, Alexandra M Psihogios, Pei-Yao Hung, Susan A Murphy

{"title":"Reinforcement Learning on Dyads to Enhance Medication Adherence.","authors":"Ziping Xu, Hinal Jajal, Sung Won Choi, Inbal Nahum-Shani, Guy Shani, Alexandra M Psihogios, Pei-Yao Hung, Susan A Murphy","doi":"10.1007/978-3-031-95838-0_48","DOIUrl":"10.1007/978-3-031-95838-0_48","url":null,"abstract":"Medication adherence is critical for the recovery of adolescents and young adults (AYAs) who have undergone hematopoietic cell transplantation. However, maintaining adherence is challenging for AYAs after hospital discharge, who experience both individual (e.g. physical and emotional symptoms) and interpersonal barriers (e.g., relational difficulties with their care partner, who is often involved in medication management). To optimize the effectiveness of a three-component digital intervention targeting both members of the dyad as well as their relationship, we propose a novel Multi-Agent Reinforcement Learning (MARL) approach to personalize the delivery of interventions. By incorporating the domain knowledge, the MARL framework, where each agent is responsible for the delivery of one intervention component, allows for faster learning compared with a flattened agent. Evaluation using a dyadic simulator environment, based on real clinical data, shows a significant improvement in medication adherence (approximately 3%) compared to purely random intervention delivery. The effectiveness of this approach will be further evaluated in an upcoming trial.","PeriodicalId":72303,"journal":{"name":"Artificial intelligence in medicine. Conference on Artificial Intelligence in Medicine (2005- )","volume":"15734 ","pages":"490-499"},"PeriodicalIF":0.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12413200/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145016659","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Augmentation-Free Contrastive Learning for EKG Classification. 无增强对比学习在心电图分类中的应用。

Artificial intelligence in medicine. Conference on Artificial Intelligence in Medicine (2005- ) Pub Date : 2025-01-01 Epub Date: 2025-06-23 DOI: 10.1007/978-3-031-95838-0_46

Junheng Wang, Milos Hauskrecht

{"title":"Augmentation-Free Contrastive Learning for EKG Classification.","authors":"Junheng Wang, Milos Hauskrecht","doi":"10.1007/978-3-031-95838-0_46","DOIUrl":"10.1007/978-3-031-95838-0_46","url":null,"abstract":"Electrocardiogram (ECG/EKG) analysis is a vital diagnostic tool for assessing heart conditions, extensively used in clinical applications such as patient monitoring, surgical support, and heart disease research. With the rising demand for automated EKG interpretation, particularly for disease diagnosis and waveform labeling, machine learning models have become essential. However, the scarcity of large, well-labeled EKG datasets poses a significant challenge for training EKG classification models in the supervised form. This has shifted the attention towards unsupervised model pre-training, which often outperforms pure supervised methods when only a limited number of labeled data is available. This study explores the adaptation of the contrastive representation learning framework for EKG classification. Traditional contrastive learning methods rely on data augmentations to create diverse views of the same sample, but these augmentations are domain-specific, difficult to design, and can unpredictably impact model performance across different tasks. In this work, we address these limitations by proposing a novel, augmentation-free approach that integrates seamlessly with existing contrastive frameworks by eliminating their dependence on augmentations and hence their potential drawbacks. We evaluate our approach on the PTB-XL [1] dataset, and demonstrate its benefits in the unsupervised model pre-training step. Our solution offers a promising pathway for enhancing cardiac disease diagnostics in data-constrained environments.","PeriodicalId":72303,"journal":{"name":"Artificial intelligence in medicine. Conference on Artificial Intelligence in Medicine (2005- )","volume":"15734 ","pages":"468-479"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12276877/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144676660","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Minimizing Survey Questions for PTSD Prediction Following Acute Trauma. 尽量减少用于预测急性创伤后创伤后应激障碍的调查问题。

Artificial intelligence in medicine. Conference on Artificial Intelligence in Medicine (2005- ) Pub Date : 2024-07-01 Epub Date: 2024-07-25 DOI: 10.1007/978-3-031-66538-7_11

Ben Kurzion, Chia-Hao Shih, Hong Xie, Xin Wang, Kevin S Xu

{"title":"Minimizing Survey Questions for PTSD Prediction Following Acute Trauma.","authors":"Ben Kurzion, Chia-Hao Shih, Hong Xie, Xin Wang, Kevin S Xu","doi":"10.1007/978-3-031-66538-7_11","DOIUrl":"10.1007/978-3-031-66538-7_11","url":null,"abstract":"Traumatic experiences have the potential to give rise to post-traumatic stress disorder (PTSD), a debilitating psychiatric condition associated with impairments in both social and occupational functioning. There has been great interest in utilizing machine learning approaches to predict the development of PTSD in trauma patients from clinician assessment or survey-based psychological assessments. However, these assessments require a large number of questions, which is time consuming and not easy to administer. In this paper, we aim to predict PTSD development of patients 3 months post-trauma from multiple survey-based assessments taken within 2 weeks post-trauma. Our objective is to minimize the number of survey questions that patients need to answer while maintaining the prediction accuracy from the full surveys. We formulate this as a feature selection problem and consider 4 different feature selection approaches. We demonstrate that it is possible to achieve up to 72% accuracy for predicting the 3-month PTSD diagnosis from 10 survey questions using a mean decrease in impurity-based feature selector followed by a gradient boosting classifier.","PeriodicalId":72303,"journal":{"name":"Artificial intelligence in medicine. Conference on Artificial Intelligence in Medicine (2005- )","volume":"14844 ","pages":"90-100"},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11392023/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142302309","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Enhancing Hypotension Prediction in Real-time Patient Monitoring Through Deep Learning: A Novel Application of XResNet with Contrastive Learning and Value Attention Mechanisms. 通过深度学习加强实时患者监测中的低血压预测：具有对比学习和价值注意机制的 XResNet 的新应用。

Artificial intelligence in medicine. Conference on Artificial Intelligence in Medicine (2005- ) Pub Date : 2024-07-01 Epub Date: 2024-07-25 DOI: 10.1007/978-3-031-66538-7_5

Xiangru Chen, Milos Hauskrecht

{"title":"Enhancing Hypotension Prediction in Real-time Patient Monitoring Through Deep Learning: A Novel Application of XResNet with Contrastive Learning and Value Attention Mechanisms.","authors":"Xiangru Chen, Milos Hauskrecht","doi":"10.1007/978-3-031-66538-7_5","DOIUrl":"10.1007/978-3-031-66538-7_5","url":null,"abstract":"The precise prediction of hypotension is vital for advancing preemptive patient care strategies. Traditional machine learning approaches, while instrumental in this field, are hampered by their dependence on structured historical data and manual feature extraction techniques. These methods often fall short of recognizing the intricate patterns present in physiological signals. Addressing this limitation, our study introduces an innovative application of deep learning technologies, utilizing a sophisticated end-to-end architecture grounded in XResNet. This architecture is further enhanced by the integration of contrastive learning and a value attention mechanism, specifically tailored to analyze arterial blood pressure (ABP) waveform signals. Our approach improves the performance of hypotension prediction over the existing state-of-theart ABP model [7]. This research represents a step towards optimizing patient care, embodying the next generation of AI-driven healthcare solutions. Through our findings, we demonstrate the promise of deep learning in overcoming the limitations of conventional prediction models, thereby offering an avenue for enhancing patient outcomes in clinical settings.","PeriodicalId":72303,"journal":{"name":"Artificial intelligence in medicine. Conference on Artificial Intelligence in Medicine (2005- )","volume":"14844 ","pages":"46-51"},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11326502/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142001467","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Sentence-Aligned Simplification of Biomedical Abstracts. 生物医学摘要的句子对齐简化。

Artificial intelligence in medicine. Conference on Artificial Intelligence in Medicine (2005- ) Pub Date : 2024-01-01 Epub Date: 2024-07-25 DOI: 10.1007/978-3-031-66538-7_32

Brian Ondov, Dina Demner-Fushman

{"title":"Sentence-Aligned Simplification of Biomedical Abstracts.","authors":"Brian Ondov, Dina Demner-Fushman","doi":"10.1007/978-3-031-66538-7_32","DOIUrl":"https://doi.org/10.1007/978-3-031-66538-7_32","url":null,"abstract":"The availability of biomedical abstracts in online databases could improve health literacy and drive more informed choices. However, the technical language of these documents makes them inaccessible to healthcare consumers, causing disengagement, frustration and potential misuse. In this work we explore adapting foundation language models to the Plain Language Adaptation of Biomedical Abstracts benchmark. This task is challenging because it requires sentence-by-sentence simplifications, but entire abstracts must also be simplified cohesively. We present a sentence-wise autoregressive approach and report experiments with this technique in both zero-shot and fine-tuned settings, using both proprietary and open-source models. We also introduce a stochastic regularization technique to encourage recovery from source-copying during autoregressive inference. Our best-performing model achieves a 32 point increase in SARI and 6 point increase in BERTscore over the reported state-of-the-art. This also surpasses performance of recent open-domain and biomedical sentence simplification models on this task. Further, in manual evaluation, models achieve factual accuracy comparable to human-level, with simplicity close to that of humans. Abstracts simplified by these models could unlock a massive source of health information while retaining clear provenance for each statement to enhance trustworthiness.","PeriodicalId":72303,"journal":{"name":"Artificial intelligence in medicine. Conference on Artificial Intelligence in Medicine (2005- )","volume":"14844 ","pages":"322-333"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12377474/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144980546","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Uncovering hidden therapeutic indications through drug repurposing with graph neural networks and heterogeneous data. 利用图神经网络和异构数据，通过药物再利用发现隐藏的治疗适应症。

Artificial intelligence in medicine. Conference on Artificial Intelligence in Medicine (2005- ) Pub Date : 2023-10-01 DOI: 10.2139/ssrn.4385667

Adrián Ayuso-Muñoz, Lucía Prieto-Santamaría, Esther Ugarte-Carro, E. Serrano, A. Rodríguez-González

{"title":"Uncovering hidden therapeutic indications through drug repurposing with graph neural networks and heterogeneous data.","authors":"Adrián Ayuso-Muñoz, Lucía Prieto-Santamaría, Esther Ugarte-Carro, E. Serrano, A. Rodríguez-González","doi":"10.2139/ssrn.4385667","DOIUrl":"https://doi.org/10.2139/ssrn.4385667","url":null,"abstract":"Drug repurposing has gained the attention of many in the recent years. The practice of repurposing existing drugs for new therapeutic uses helps to simplify the drug discovery process, which in turn reduces the costs and risks that are associated with de novo development. Representing biomedical data in the form of a graph is a simple and effective method to depict the underlying structure of the information. Using deep neural networks in combination with this data represents a promising approach to address drug repurposing. This paper presents BEHOR a more comprehensive version of the REDIRECTION model, which was previously presented. Both versions utilize the DISNET biomedical graph as the primary source of information, providing the model with extensive and intricate data to tackle the drug repurposing challenge. This new version's results for the reported metrics in the RepoDB test are 0.9604 for AUROC and 0.9518 for AUPRC. Additionally, a discussion is provided regarding some of the novel predictions to demonstrate the reliability of the model. The authors believe that BEHOR holds promise for generating drug repurposing hypotheses and could greatly benefit the field.","PeriodicalId":72303,"journal":{"name":"Artificial intelligence in medicine. Conference on Artificial Intelligence in Medicine (2005- )","volume":"21 1","pages":"102687"},"PeriodicalIF":0.0,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89077980","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Learning EKG Diagnostic Models with Hierarchical Class Label Dependencies. 学习具有分层类标签依赖关系的心电图诊断模型。

Artificial intelligence in medicine. Conference on Artificial Intelligence in Medicine (2005- ) Pub Date : 2023-06-01 DOI: 10.1007/978-3-031-34344-5_31

Junheng Wang, Milos Hauskrecht

{"title":"Learning EKG Diagnostic Models with Hierarchical Class Label Dependencies.","authors":"Junheng Wang, Milos Hauskrecht","doi":"10.1007/978-3-031-34344-5_31","DOIUrl":"https://doi.org/10.1007/978-3-031-34344-5_31","url":null,"abstract":"Electrocardiogram (EKG/ECG) is a key diagnostic tool to assess patient's cardiac condition and is widely used in clinical applications such as patient monitoring, surgery support, and heart medicine research. With recent advances in machine learning (ML) technology there has been a growing interest in the development of models supporting automatic EKG interpretation and diagnosis based on past EKG data. The problem can be modeled as multi-label classification (MLC), where the objective is to learn a function that maps each EKG reading to a vector of diagnostic class labels reflecting the underlying patient condition at different levels of abstraction. In this paper, we propose and investigate an ML model that considers class-label dependency embedded in the hierarchical organization of EKG diagnoses to improve the EKG classification performance. Our model first transforms the EKG signals into a low-dimensional vector, and after that uses the vector to predict different class labels with the help of the conditional tree structured Bayesian network (CTBN) that is able to capture hierarchical dependencies among class variables. We evaluate our model on the publicly available PTB-XL dataset. Our experiments demonstrate that modeling of hierarchical dependencies among class variables improves the diagnostic model performance under multiple classification performance metrics as compared to classification models that predict each class label independently.","PeriodicalId":72303,"journal":{"name":"Artificial intelligence in medicine. Conference on Artificial Intelligence in Medicine (2005- )","volume":"13897 ","pages":"260-270"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10256236/pdf/nihms-1899160.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10155206","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Predicting Kidney Transplant Survival using Multiple Feature Representations for HLAs. 利用hla的多特征表示预测肾移植生存。

Artificial intelligence in medicine. Conference on Artificial Intelligence in Medicine (2005- ) Pub Date : 2021-06-01 Epub Date: 2021-06-08

Mohammadreza Nemati, Haonan Zhang, Michael Sloma, Dulat Bekbolsynov, Hong Wang, Stanislaw Stepkowski, Kevin S Xu

{"title":"Predicting Kidney Transplant Survival using Multiple Feature Representations for HLAs.","authors":"Mohammadreza Nemati, Haonan Zhang, Michael Sloma, Dulat Bekbolsynov, Hong Wang, Stanislaw Stepkowski, Kevin S Xu","doi":"","DOIUrl":"","url":null,"abstract":"Kidney transplantation can significantly enhance living standards for people suffering from end-stage renal disease. A significant factor that affects graft survival time (the time until the transplant fails and the patient requires another transplant) for kidney transplantation is the compatibility of the Human Leukocyte Antigens (HLAs) between the donor and recipient. In this paper, we propose new biologically-relevant feature representations for incorporating HLA information into machine learning-based survival analysis algorithms. We evaluate our proposed HLA feature representations on a database of over 100,000 transplants and find that they improve prediction accuracy by about 1%, modest at the patient level but potentially significant at a societal level. Accurate prediction of survival times can improve transplant survival outcomes, enabling better allocation of donors to recipients and reducing the number of re-transplants due to graft failure with poorly matched donors.","PeriodicalId":72303,"journal":{"name":"Artificial intelligence in medicine. Conference on Artificial Intelligence in Medicine (2005- )","volume":"12721 ","pages":"51-60"},"PeriodicalIF":0.0,"publicationDate":"2021-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8224462/pdf/nihms-1714702.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39135176","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Identifying Symptom Clusters Through Association Rule Mining. 通过关联规则挖掘识别症状聚类。

Artificial intelligence in medicine. Conference on Artificial Intelligence in Medicine (2005- ) Pub Date : 2021-06-01 Epub Date: 2021-06-08 DOI: 10.1007/978-3-030-77211-6_58

Mikayla Biggs, Carla Floricel, Lisanne Van Dijk, Abdallah S R Mohamed, C David Fuller, G Elisabeta Marai, Xinhua Zhang, Guadalupe Canahuate

引用次数: 1