Real-time prediction of mortality, readmission, and length of stay using electronic health record data

Cai, Xiongcai; Perez‐Concha, Oscar; Coiera, Enrico; Martín-Sánchez, Fernando; Day, Richard O.; Roffe, David; Gallego, Blanca

doi:10.1093/jamia/ocv110

“…The benefits of reusing clinical data have been well recognized for decades [3,[18][19][20][21] and a detailed study by PricewaterhouseCoopers explained how reuse could enable improvements of health outcomes and costs [22]. To improve healthcare management and quality, clinical data has already been reused to measure and improve quality [23,24], predict patients length of stay, discharge, readmission, and death [25][26][27][28], and improve infection control [29][30][31]. Data has also been reused for early detection of diseases, pharmacovigilance, and post-market and public health surveillance [32].…”

Section: B Motivations and Challenges For Clinical Data Reusementioning

confidence: 99%

Clinical Data Reuse or Secondary Use: Current Status and Potential Future Progress

Meystre¹,

Lovis²,

Bürkle³

et al. 2017

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SummaryObjective: To perform a review of recent research in clinical data reuse or secondary use, and envision future advances in this field. Methods: The review is based on a large literature search in MEDLINE (through PubMed), conference proceedings, and the ACM Digital Library, focusing only on research published between 2005 and early 2016. Each selected publication was reviewed by the authors, and a structured analysis and summarization of its content was developed. Results: The initial search produced 359 publications, reduced after a manual examination of abstracts and full publications. The following aspects of clinical data reuse are discussed: motivations and challenges, privacy and ethical concerns, data integration and interoperability, data models and terminologies, unstructured data reuse, structured data mining, clinical practice and research integration, and examples of clinical data reuse (quality measurement and learning healthcare systems). Conclusion: Reuse of clinical data is a fast-growing field recognized as essential to realize the potentials for high quality healthcare, improved healthcare management, reduced healthcare costs, population health management, and effective clinical research.

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“…Research on predicting ICU mortality is of great academic interest in medicine [9] and in clinical machine learning [10], [11]. A number of researchers have investigated how to correlate ICU data with patient outcomes.…”

Section: Background Researchmentioning

confidence: 99%

Dynamic Prediction of ICU Mortality Risk Using Domain Adaptation

Alves

¹

,

Laender

²

,

Veloso

³

et al. 2018

2018 IEEE International Conference on Big Data (Big Data)

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Early recognition of risky trajectories during an Intensive Care Unit (ICU) stay is one of the key steps towards improving patient survival. Learning trajectories from physiological signals continuously measured during an ICU stay requires learning time-series features that are robust and discriminative across diverse patient populations. Patients within different ICU populations (referred here as domains) vary by age, conditions and interventions. Thus, mortality prediction models using patient data from a particular ICU population may perform suboptimally in other populations because the features used to train such models have different distributions across the groups. In this paper, we explore domain adaptation strategies in order to learn mortality prediction models that extract and transfer complex temporal features from multivariate time-series ICU data. Features are extracted in a way that the state of the patient in a certain time depends on the previous state. This enables dynamic predictions and creates a mortality risk space that describes the risk of a patient at a particular time. Experiments based on cross-ICU populations reveals that our model outperforms all considered baselines. Gains in terms of AUC range from 4% to 8% for early predictions when compared with a recent state-of-the-art representative for ICU mortality prediction. In particular, models for the Cardiac ICU population achieve AUC numbers as high as 0.88, showing excellent clinical utility for early mortality prediction. Finally, we present an explanation of factors contributing to the possible ICU outcomes, so that our models can be used to complement clinical reasoning.

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“…To improve healthcare management and quality, clinical data has already been reused to measure and improve quality [23,24], predict patients length of stay, discharge, readmission, and death [25][26][27][28], and improve infection control [29][30][31]. Data has also been reused for early detection of diseases, pharmacovigilance, and post-market and public health surveillance [32].…”

Section: B Motivations and Challenges For Clinical Data Reusementioning

confidence: 99%

Clinical Data Reuse or Secondary Use: Current Status and Potential Future Progress

Meystre¹,

Lovis²,

Bürkle³

et al. 2017

View full text Add to dashboard Cite

SummaryObjective: To perform a review of recent research in clinical data reuse or secondary use, and envision future advances in this field. Methods: The review is based on a large literature search in MEDLINE (through PubMed), conference proceedings, and the ACM Digital Library, focusing only on research published between 2005 and early 2016. Each selected publication was reviewed by the authors, and a structured analysis and summarization of its content was developed. Results: The initial search produced 359 publications, reduced after a manual examination of abstracts and full publications. The following aspects of clinical data reuse are discussed: motivations and challenges, privacy and ethical concerns, data integration and interoperability, data models and terminologies, unstructured data reuse, structured data mining, clinical practice and research integration, and examples of clinical data reuse (quality measurement and learning healthcare systems). Conclusion: Reuse of clinical data is a fast-growing field recognized as essential to realize the potentials for high quality healthcare, improved healthcare management, reduced healthcare costs, population health management, and effective clinical research.

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Real-time prediction of mortality, readmission, and length of stay using electronic health record data

Abstract: Bayesian Networks can model EHRs to provide real-time forecasts for patient outcomes, which provide richer information than traditional independent point predictions of length of stay, death, or readmission, and can thus better support decision making.

Cited by 101 publications

References 17 publications

Clinical Data Reuse or Secondary Use: Current Status and Potential Future Progress

Clinical Data Reuse or Secondary Use: Current Status and Potential Future Progress

Dynamic Prediction of ICU Mortality Risk Using Domain Adaptation

Clinical Data Reuse or Secondary Use: Current Status and Potential Future Progress

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