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

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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
Mohammadreza Nemati, Haonan Zhang, Michael Sloma, D. Bekbolsynov, Hong Wang, S. Stepkowski, Kevin S. Xu
{"title":"Predicting Kidney Transplant Survival using Multiple Feature Representations for HLAs","authors":"Mohammadreza Nemati, Haonan Zhang, Michael Sloma, D. Bekbolsynov, Hong Wang, S. Stepkowski, Kevin S. Xu","doi":"10.1007/978-3-030-77211-6_6","DOIUrl":"https://doi.org/10.1007/978-3-030-77211-6_6","url":null,"abstract":"","PeriodicalId":72303,"journal":{"name":"Artificial intelligence in medicine. Conference on Artificial Intelligence in Medicine (2005- )","volume":"11 1","pages":"51-60"},"PeriodicalIF":0.0,"publicationDate":"2021-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84481214","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: 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,&nbsp;Muhao Chen,&nbsp;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":"<p><p>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.</p>","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
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
Jeong Min Lee, M. Hauskrecht
{"title":"Recent Context-Aware LSTM for Clinical Event Time-Series Prediction","authors":"Jeong Min Lee, M. Hauskrecht","doi":"10.1007/978-3-030-21642-9_3","DOIUrl":"https://doi.org/10.1007/978-3-030-21642-9_3","url":null,"abstract":"","PeriodicalId":72303,"journal":{"name":"Artificial intelligence in medicine. Conference on Artificial Intelligence in Medicine (2005- )","volume":"10 1","pages":"13-23"},"PeriodicalIF":0.0,"publicationDate":"2019-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81994515","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: 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
{"title":"Predicting patient's diagnoses and diagnostic categories from clinical-events in EHR data.","authors":"Seyedsalim Malakouti,&nbsp;Milos Hauskrecht","doi":"10.1007/978-3-030-21642-9_17","DOIUrl":"https://doi.org/10.1007/978-3-030-21642-9_17","url":null,"abstract":"<p><p>In this paper we develop and study machine learning based models based on latent semantic indexing capable of automatically assigning diagnoses and diagnostic categories to patients based on structured clinical data in their Electronic Health record (EHR). These models can be either used for automatic coding of patient's diagnoses from structured EHR data at the time of discharge, or for supporting dynamic diagnosis and summarization of the patient condition. We study the performance of our diagnostic models on MIMIC-III EHR data.</p>","PeriodicalId":72303,"journal":{"name":"Artificial intelligence in medicine. Conference on Artificial Intelligence in Medicine (2005- )","volume":"11526 ","pages":"125-130"},"PeriodicalIF":0.0,"publicationDate":"2019-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1007/978-3-030-21642-9_17","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41221619","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: 16
Mining Compact Predictive Pattern Sets Using Classification Model 利用分类模型挖掘紧凑预测模式集
M. Mantovani, Combi Carlo, M. Hauskrecht
{"title":"Mining Compact Predictive Pattern Sets Using Classification Model","authors":"M. Mantovani, Combi Carlo, M. Hauskrecht","doi":"10.1007/978-3-030-21642-9_49","DOIUrl":"https://doi.org/10.1007/978-3-030-21642-9_49","url":null,"abstract":"","PeriodicalId":72303,"journal":{"name":"Artificial intelligence in medicine. Conference on Artificial Intelligence in Medicine (2005- )","volume":"2 1","pages":"386-396"},"PeriodicalIF":0.0,"publicationDate":"2019-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81919423","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: 4
Change-Point Detection Method for Clinical Decision Support System Rule Monitoring. 临床决策支持系统规则监测的变点检测方法。
Artificial intelligence in medicine. Conference on Artificial Intelligence in Medicine (2005- ) Pub Date : 2017-06-01 Epub Date: 2017-05-30 DOI: 10.1007/978-3-319-59758-4_14
Siqi Liu, Adam Wright, Milos Hauskrecht
{"title":"Change-Point Detection Method for Clinical Decision Support System Rule Monitoring.","authors":"Siqi Liu,&nbsp;Adam Wright,&nbsp;Milos Hauskrecht","doi":"10.1007/978-3-319-59758-4_14","DOIUrl":"https://doi.org/10.1007/978-3-319-59758-4_14","url":null,"abstract":"<p><p>A clinical decision support system (CDSS) and its components can malfunction due to various reasons. Monitoring the system and detecting its malfunctions can help one to avoid any potential mistakes and associated costs. In this paper, we investigate the problem of detecting changes in the CDSS operation, in particular its monitoring and alerting subsystem, by monitoring its rule firing counts. The detection should be performed online, that is whenever a new datum arrives, we want to have a score indicating how likely there is a change in the system. We develop a new method based on Seasonal-Trend decomposition and likelihood ratio statistics to detect the changes. Experiments on real and simulated data show that our method has a lower delay in detection compared with existing change-point detection methods.</p>","PeriodicalId":72303,"journal":{"name":"Artificial intelligence in medicine. Conference on Artificial Intelligence in Medicine (2005- )","volume":"10259 ","pages":"126-135"},"PeriodicalIF":0.0,"publicationDate":"2017-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1007/978-3-319-59758-4_14","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"35255379","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: 20
Identifying Parkinson's Patients: A Functional Gradient Boosting Approach. 识别帕金森患者:一种功能梯度增强方法。
Artificial intelligence in medicine. Conference on Artificial Intelligence in Medicine (2005- ) Pub Date : 2017-06-01 Epub Date: 2017-05-30 DOI: 10.1007/978-3-319-59758-4_39
Devendra Singh Dhami, Ameet Soni, David Page, Sriraam Natarajan
{"title":"Identifying Parkinson's Patients: A Functional Gradient Boosting Approach.","authors":"Devendra Singh Dhami,&nbsp;Ameet Soni,&nbsp;David Page,&nbsp;Sriraam Natarajan","doi":"10.1007/978-3-319-59758-4_39","DOIUrl":"https://doi.org/10.1007/978-3-319-59758-4_39","url":null,"abstract":"<p><p>Parkinson's, a progressive neural disorder, is difficult to identify due to the hidden nature of the symptoms associated. We present a machine learning approach that uses a definite set of features obtained from the Parkinsons Progression Markers Initiative(PPMI) study as input and classifies them into one of two classes: PD(Parkinson's disease) and HC(Healthy Control). As far as we know this is the first work in applying machine learning algorithms for classifying patients with Parkinson's disease with the involvement of domain expert during the feature selection process. We evaluate our approach on 1194 patients acquired from Parkinsons Progression Markers Initiative and show that it achieves a state-of-the-art performance with minimal feature engineering.</p>","PeriodicalId":72303,"journal":{"name":"Artificial intelligence in medicine. Conference on Artificial Intelligence in Medicine (2005- )","volume":"10259 ","pages":"332-337"},"PeriodicalIF":0.0,"publicationDate":"2017-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1007/978-3-319-59758-4_39","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"35551182","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: 11
Extracting Adverse Drug Events from Text using Human Advice. 利用人类建议从文本中提取药物不良事件。
Phillip Odom, Vishal Bangera, Tushar Khot, David Page, Sriraam Natarajan
{"title":"Extracting Adverse Drug Events from Text using Human Advice.","authors":"Phillip Odom,&nbsp;Vishal Bangera,&nbsp;Tushar Khot,&nbsp;David Page,&nbsp;Sriraam Natarajan","doi":"10.1007/978-3-319-19551-3_26","DOIUrl":"https://doi.org/10.1007/978-3-319-19551-3_26","url":null,"abstract":"<p><p>Adverse drug events (ADEs) are a major concern and point of emphasis for the medical profession, government, and society in general. When methods extract ADEs from observational data, there is a necessity to evaluate these methods. More precisely, it is important to know what is already known in the literature. Consequently, we employ a novel relation extraction technique based on a recently developed probabilistic logic learning algorithm that exploits human advice. We demonstrate on a standard adverse drug events data base that the proposed approach can successfully extract existing adverse drug events from limited amount of training data and compares favorably with state-of-the-art probabilistic logic learning methods.</p>","PeriodicalId":72303,"journal":{"name":"Artificial intelligence in medicine. Conference on Artificial Intelligence in Medicine (2005- )","volume":"2015 ","pages":"195-204"},"PeriodicalIF":0.0,"publicationDate":"2015-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1007/978-3-319-19551-3_26","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"35594880","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: 19
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