Identifying Chemotherapy Regimens in Electronic Health Record Data Using Interval-Encoded Sequence Alignment

Syed, Haider; Das, Amar K.

doi:10.1007/978-3-319-19551-3_17

Cited by 8 publications

(5 citation statements)

References 13 publications

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“…The temporal models commonly seen in the literature include models, such as sequence analysis (Wang et al, 2008 ; Batal et al, 2011 ; Tao et al, 2012 ; Casanova et al, 2015 ; Syed and Das, 2015 ), association rule mining (Bellazzi et al, 2011 ; Casanova et al, 2015 ; Yang and Yang, 2015 ), temporal Cox regression (Warner et al, 2013 ; Cai et al, 2015 ; McCoy et al, 2015 ), and clustering (Toddenroth et al, 2014 ; Choi et al, 2017 ). Sequence analysis and association rule mining-based studies require extensive user input for identifying specific features whose patterns of correlation can be studied with respect to the target variable.…”

Section: Introductionmentioning

confidence: 99%

Domain Adaptation Using Convolutional Autoencoder and Gradient Boosting for Adverse Events Prediction in the Intensive Care Unit

Zhu

Venugopalan

Zhang

et al. 2022

Front. Artif. Intell.

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More than 5 million patients have admitted annually to intensive care units (ICUs) in the United States. The leading causes of mortality are cardiovascular failures, multi-organ failures, and sepsis. Data-driven techniques have been used in the analysis of patient data to predict adverse events, such as ICU mortality and ICU readmission. These models often make use of temporal or static features from a single ICU database to make predictions on subsequent adverse events. To explore the potential of domain adaptation, we propose a method of data analysis using gradient boosting and convolutional autoencoder (CAE) to predict significant adverse events in the ICU, such as ICU mortality and ICU readmission. We demonstrate our results from a retrospective data analysis using patient records from a publicly available database called Multi-parameter Intelligent Monitoring in Intensive Care-II (MIMIC-II) and a local database from Children's Healthcare of Atlanta (CHOA). We demonstrate that after adopting novel data imputation on patient ICU data, gradient boosting is effective in both the mortality prediction task and the ICU readmission prediction task. In addition, we use gradient boosting to identify top-ranking temporal and non-temporal features in both prediction tasks. We discuss the relationship between these features and the specific prediction task. Lastly, we indicate that CAE might not be effective in feature extraction on one dataset, but domain adaptation with CAE feature extraction across two datasets shows promising results.

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Section: Introductionmentioning

confidence: 99%

Domain Adaptation Using Convolutional Autoencoder and Gradient Boosting for Adverse Events Prediction in the Intensive Care Unit

Zhu

Venugopalan

Zhang

et al. 2022

Front. Artif. Intell.

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“…Two journals not included in the JCR categories published multiple papers identified in the search, these are Lecture Notes in Computer Science [20,48,85,87,98,112,179] and Studies in Health Technology and Informatics [5,74,76,94,121,136,210].…”

Section: Publication Areamentioning

confidence: 99%

“…Multiple Methods Two [8,11,12,18,54,74,76,99,108,140,151,162,167,179,190,203] Three [7,211] Table A11. Investigating type.…”

Section: Fundingmentioning

confidence: 99%

Clinical pathway modelling: a literature review

2019

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Hospital information systems are increasingly used as part of decision support tools for planning at strategic, tactical and operational decision levels. Clinical pathways are an effective and efficient approach in standardising the progression of treatment, to support patient care and facilitate clinical decision making. This literature review proposes a taxonomy of problems related to clinical pathways and explores the intersection between Information Systems (IS), Operational Research (OR) and industrial engineering. A structured search identified 175 papers included in the taxonomy and analysed in this review. The findings suggest that future work should consider industrial engineering integrated with OR techniques, with an aim to improving the handling of multiple scopes within one model, while encouraging interaction between the disjoint care levels and with a more direct focus on patient outcomes. Achieving this would continue to bridge the gap between OR, IS and industrial engineering, for clinical pathways to aid decision support.

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“…In this analysis, we derive the most frequently used patterns from death certificates using sequence rule mining (SRM). Sequence rule mining, sequence pattern mining [29][30][31][32][33] or association rule mining [34,35] are the most commonly used temporal models in literature for finding temporal relationships among sequences. SRM has diverse pattern mining applications in finance and market analysis [36,37], travel analysis [38], mobile learning [39] and database projections.…”

Section: Deriving Frequent Cod Patterns From Death Certificatesmentioning

confidence: 99%

Improving Validity of Cause of Death on Death Certificates

Hoffman

Venugopalan

et al. 2018

Proceedings of the 2018 ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics

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Accurate reporting of causes of death on death certificates is essential to formulate appropriate disease control, prevention and emergency response by national health-protection institutions such as Center for disease prevention and control (CDC). In this study, we utilize knowledge from publicly available expert-formulated rules for the cause of death to determine the extent of discordance in the death certificates in national mortality data with the expert knowledge base. We also report the most commonly occurring invalid causal pairs which physicians put in the death certificates. We use sequence rule mining to find patterns that are most frequent on death certificates and compare them with the rules from the expert knowledge based. Based on our results, 20.1% of the common patterns derived from entries into death certificates were discordant. The most probable causes of these discordance or invalid rules are missing steps and non-specific ICD-10 codes on the death certificates. Revision (ICD-10) which contains 22 chapters covering 2,046 categories of diseases [3, 4]. Despite the pressing need for high quality cause of death information, challenges such as lack of adequate knowledge and practice still exist for the accurate filling of death certificates. These challenges lead to death certificates of uncertain quality. Studies have

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Identifying Chemotherapy Regimens in Electronic Health Record Data Using Interval-Encoded Sequence Alignment

Cited by 8 publications

References 13 publications

Domain Adaptation Using Convolutional Autoencoder and Gradient Boosting for Adverse Events Prediction in the Intensive Care Unit

Domain Adaptation Using Convolutional Autoencoder and Gradient Boosting for Adverse Events Prediction in the Intensive Care Unit

Clinical pathway modelling: a literature review

Improving Validity of Cause of Death on Death Certificates

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