Artificial intelligence in medicine. Conference on Artificial Intelligence in Medicine (2005- )最新文献_第2页

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

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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

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 : 2021-03-04 DOI: 10.1007/978-3-030-77211-6_6

Mohammadreza Nemati, Haonan Zhang, Michael Sloma, D. Bekbolsynov, Hong Wang, S. Stepkowski, Kevin S. Xu

引用次数: 6

Diagnostic Prediction with Sequence-of-sets Representation Learning for Clinical Events. 临床事件的集合序列表示学习诊断预测。

Artificial intelligence in medicine. Conference on Artificial Intelligence in Medicine (2005- ) Pub Date : 2020-08-01 Epub Date: 2020-09-26 DOI: 10.1007/978-3-030-59137-3_31

Tianran Zhang, Muhao Chen, Alex A T Bui

{"title":"Diagnostic Prediction with Sequence-of-sets Representation Learning for Clinical Events.","authors":"Tianran Zhang, Muhao Chen, Alex A T Bui","doi":"10.1007/978-3-030-59137-3_31","DOIUrl":"https://doi.org/10.1007/978-3-030-59137-3_31","url":null,"abstract":"Electronic health records (EHRs) contain both ordered and unordered chronologies of clinical events that occur during a patient encounter. However, during data preprocessing steps, many predictive models impose a predefined order on unordered clinical events sets (e.g., alphabetical, natural order from the chart, etc.), which is potentially incompatible with the temporal nature of the sequence and predictive task. To address this issue, we propose DPSS, which seeks to capture each patient's clinical event records as sequences of event sets. For each clinical event set, we assume that the predictive model should be invariant to the order of concurrent events and thus employ a novel permutation sampling mechanism. This paper evaluates the use of this permuted sampling method given different data-driven models for predicting a heart failure (HF) diagnosis in subsequent patient visits. Experimental results using the MIMIC-III dataset show that the permutation sampling mechanism offers improved discriminative power based on the area under the receiver operating curve (AUROC) and precision-recall curve (pr-AUC) metrics as HF diagnosis prediction becomes more robust to different data ordering schemes.","PeriodicalId":72303,"journal":{"name":"Artificial intelligence in medicine. Conference on Artificial Intelligence in Medicine (2005- )","volume":"12299 ","pages":"348-358"},"PeriodicalIF":0.0,"publicationDate":"2020-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8143801/pdf/nihms-1698603.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"38949682","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

Diagnostic Prediction with Sequence-of-setsRepresentation Learning for Clinical Events 用集合序列表示学习进行临床事件诊断预测

Artificial intelligence in medicine. Conference on Artificial Intelligence in Medicine (2005- ) Pub Date : 2020-08-01 DOI: 10.1101/2020.08.03.20167569

Tianran Zhang, Muhao Chen, A. Bui

{"title":"Diagnostic Prediction with Sequence-of-setsRepresentation Learning for Clinical Events","authors":"Tianran Zhang, Muhao Chen, A. Bui","doi":"10.1101/2020.08.03.20167569","DOIUrl":"https://doi.org/10.1101/2020.08.03.20167569","url":null,"abstract":"Electronic health records (EHRs) contain both ordered and unordered chronologies of clinical events that occur during a patient encounter. However, during data preprocessing steps, many predictive models impose a predefined order on unordered clinical events sets (e.g., alphabetical, natural order from the chart, etc.), which is potentially incompatible with the temporal nature of the sequence and predictive task. To address this issue, we proposeDPSS, which seeks to capture each patient's clinical event records as sequences of event sets. Foreach clinical event set, we assume that the predictive model should be invariant to the order of concurrent events and thus employ a novel permutation sampling mechanism. This paper evaluates the use of this permuted sampling method given different data-driven models for predicting a heart failure (HF) diagnosis in sub-sequent patient visits. Experimental results using the MIMIC-III dataset show that the permutation sampling mechanism offers improved discriminative power based on the area under the receiver operating curve (AUROC) and precision-recall curve (pr-AUC) metrics as HF diagnosis prediction becomes more robust to different data ordering schemes.","PeriodicalId":72303,"journal":{"name":"Artificial intelligence in medicine. Conference on Artificial Intelligence in Medicine (2005- )","volume":"8 1","pages":"348-358"},"PeriodicalIF":0.0,"publicationDate":"2020-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76744700","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: 22

Recent Context-Aware LSTM for Clinical Event Time-Series Prediction 近期用于临床事件时间序列预测的上下文感知LSTM

Artificial intelligence in medicine. Conference on Artificial Intelligence in Medicine (2005- ) Pub Date : 2019-06-26 DOI: 10.1007/978-3-030-21642-9_3

Jeong Min Lee, M. Hauskrecht

引用次数: 13

Predicting patient's diagnoses and diagnostic categories from clinical-events in EHR data. 根据EHR数据中的临床事件预测患者的诊断和诊断类别。

Artificial intelligence in medicine. Conference on Artificial Intelligence in Medicine (2005- ) Pub Date : 2019-06-01 Epub Date: 2019-05-30 DOI: 10.1007/978-3-030-21642-9_17

Seyedsalim Malakouti, Milos Hauskrecht

引用次数: 16

Mining Compact Predictive Pattern Sets Using Classification Model 利用分类模型挖掘紧凑预测模式集

Artificial intelligence in medicine. Conference on Artificial Intelligence in Medicine (2005- ) Pub Date : 2019-06-01 DOI: 10.1007/978-3-030-21642-9_49

M. Mantovani, Combi Carlo, M. Hauskrecht

引用次数: 4