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

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":"https://doi.org/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

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

Neural Clinical Event Sequence Prediction through Personalized Online Adaptive Learning. 基于个性化在线自适应学习的神经临床事件序列预测。

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_20

Jeong Min Lee, Milos Hauskrecht

引用次数: 3

Detection of Junctional Ectopic Tachycardia by Central Venous Pressure. 中心静脉压检测交界性异位心动过速。

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_29

Xin Tan, Yanwan Dai, Ahmed Imtiaz Humayun, Haoze Chen, Genevera I Allen, Parag N Jain

引用次数: 0

Improving Prediction of Low-Prior Clinical Events with Simultaneous General Patient-State Representation Learning. 同时一般患者状态表征学习改善低先验临床事件预测。

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_57

Matthew Barren, Milos Hauskrecht

{"title":"Improving Prediction of Low-Prior Clinical Events with Simultaneous General Patient-State Representation Learning.","authors":"Matthew Barren, Milos Hauskrecht","doi":"10.1007/978-3-030-77211-6_57","DOIUrl":"10.1007/978-3-030-77211-6_57","url":null,"abstract":"Low-prior targets are common among many important clinical events, which introduces the challenge of having enough data to support learning of their predictive models. Many prior works have addressed this problem by first building a general patient-state representation model, and then adapting it to a new low-prior prediction target. In this schema, there is potential for the predictive performance to be hindered by the misalignment between the general patient-state model and the target task. To overcome this challenge, we propose a new method that simultaneously optimizes a shared model through multi-task learning of both the low-prior supervised target and general purpose patient-state representation (GPSR). More specifically, our method improves prediction performance of a low-prior task by jointly optimizing a shared model that combines the loss of the target event and a broad range of generic clinical events. We study the approach in the context of Recurrent Neural Networks (RNNs). Through extensive experiments on multiple clinical event targets using MIMIC-III [8] data, we show that the inclusion of general patient-state representation tasks during model training improves the prediction of individual low-prior targets.","PeriodicalId":72303,"journal":{"name":"Artificial intelligence in medicine. Conference on Artificial Intelligence in Medicine (2005- )","volume":"12721 ","pages":"479-490"},"PeriodicalIF":0.0,"publicationDate":"2021-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8301230/pdf/nihms-1713021.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39221679","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

A Probabilistic Approach to Extract Qualitative Knowledge for Early Prediction of Gestational Diabetes. 一种概率方法提取妊娠期糖尿病早期预测的定性知识。

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_59

Athresh Karanam, Alexander L Hayes, Harsha Kokel, David M Haas, Predrag Radivojac, Sriraam Natarajan

引用次数: 2