Predicting ICU readmissions based on bedside medical text notes

Curto, Sérgio; Carvalho, João Paulo; Salgado, Cátia M.; Vieira, Susana M.; Sousa, J.M.

doi:10.1109/fuzz-ieee.2016.7737956

Cited by 16 publications

(14 citation statements)

References 32 publications

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“…First, chart events category, which are extracted from health care provider (e.g., physicians and nurses) notes. Chart events represent the patient's' physiological conditions based on the experts' observation and opinions [19]. Second, patient variables like chronic diseases.…”

Section: Methodsmentioning

confidence: 99%

“…For instance solutions were focused on heart failure [15], HIV [16], diabetes [17], and kidney transplants [18]. Second, no model has been able to predict ICU readmissions to a satisfactory degree yet [19]; most models suffer from a low sensitivity of around 0.6 to 0.65 [5,13,19]. Third, most models do not utilize the sequential data structure and time series feature of many EHR parameters which can lead to information loss [20].…”

Section: Introductionmentioning

confidence: 99%

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Analysis and prediction of unplanned intensive care unit readmission using recurrent neural networks with long short-term memory

et al. 2019

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Background Unplanned readmission of a hospitalized patient is an indicator of patients’ exposure to risk and an avoidable waste of medical resources. In addition to hospital readmission, intensive care unit (ICU) readmission brings further financial risk, along with morbidity and mortality risks. Identification of high-risk patients who are likely to be readmitted can provide significant benefits for both patients and medical providers. The emergence of machine learning solutions to detect hidden patterns in complex, multi-dimensional datasets provides unparalleled opportunities for developing an efficient discharge decision-making support system for physicians and ICU specialists. Methods and findings We used supervised machine learning approaches for ICU readmission prediction. We used machine learning methods on comprehensive, longitudinal clinical data from the MIMIC-III to predict the ICU readmission of patients within 30 days of their discharge. We incorporate multiple types of features including chart events, demographic, and ICD-9 embeddings. We have utilized recent machine learning techniques such as Recurrent Neural Networks (RNN) with Long Short-Term Memory (LSTM), by this we have been able to incorporate the multivariate features of EHRs and capture sudden fluctuations in chart event features (e.g. glucose and heart rate). We show that our LSTM-based solution can better capture high volatility and unstable status in ICU patients, an important factor in ICU readmission. Our machine learning models identify ICU readmissions at a higher sensitivity rate of 0.742 (95% CI, 0.718–0.766) and an improved Area Under the Curve of 0.791 (95% CI, 0.782–0.800) compared with traditional methods. We perform in-depth deep learning performance analysis, as well as the analysis of each feature contribution to the predictive model. Conclusion Our manuscript highlights the ability of machine learning models to improve our ICU decision-making accuracy and is a real-world example of precision medicine in hospitals. These data-driven solutions hold the potential for substantial clinical impact by augmenting clinical decision-making for physicians and ICU specialists. We anticipate that machine learning models will improve patient counseling, hospital administration, allocation of healthcare resources and ultimately individualized clinical care.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Analysis and prediction of unplanned intensive care unit readmission using recurrent neural networks with long short-term memory

et al. 2019

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“…Reducing costs, improving quality of care and effectively managing the resources are nowadays the main concerns of health-care decision makers [1]. Traditionally models which predict in-hospital length of stay, readmission and mortality use the data available within the first 24 hours of admissions.…”

Section: Introductionmentioning

confidence: 99%

Design and implementation of a deep recurrent model for prediction of readmission in urgent care using electronic health records

Zebin

Chaussalet

2019

2019 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB)

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There has been a steady growth in machine learning research in healthcare, however, progress is difficult to measure because of the use of different cohorts, task definitions and input variables. To take the advantage of the availability and value of digital health data, we aim to predict unplanned readmissions to the intensive care unit (ICU) from a publicly available Critical Care dataset called Medical Information Mart for Intensive Care (MIMIC-III). In this research, we formulate a heterogeneous LSTM and CNN architecture specifically to create a model of readmission risk. Our proposed predictive framework outperformed all the benchmark classifiers such as support vector machine, random forest and logistic regression models on all performance measures (AUC, accuracy and precision) except on recall where random forest performed slightly better. Predictions from these models will help in resource planning and decrease mortality or length of stay in clinical care settings.

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“…bed capacity, equipment and staff requirement) [8]. Effective use of resources is of immense importance to health care decision makers nowadays [9]. The input data used to predict LOS usually includes a combination of basic information about the person such as age and gender, medical data, lab results, demographic data and other circumstances regarding the admission.…”

Section: Introductionmentioning

confidence: 99%

A deep learning approach for length of stay prediction in clinical settings from medical records

Zebin

Rezvy

Chaussalet

2019

2019 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB)

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Predicting ICU readmissions based on bedside medical text notes

Cited by 16 publications

References 32 publications

Analysis and prediction of unplanned intensive care unit readmission using recurrent neural networks with long short-term memory

Analysis and prediction of unplanned intensive care unit readmission using recurrent neural networks with long short-term memory

Design and implementation of a deep recurrent model for prediction of readmission in urgent care using electronic health records

A deep learning approach for length of stay prediction in clinical settings from medical records

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