Accurate prediction of blood culture outcome in the intensive care unit using long short-term memory neural networks

Steenkiste, Tom Van; Ruyssinck, Joeri; Baets, Leen De; Decruyenaere, Johan; Turck, Filip De; Ongenae, Femke; Dhaene, Tom

doi:10.1016/j.artmed.2018.10.008

Cited by 51 publications

(39 citation statements)

References 18 publications

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“…Recent developments in deep learning have led to another temporal sleep apnea detection model. Long Short-Term Memory (LSTM) neural networks, a type of Recurrent Neural Network (RNN), were proposed as a good method capable of detecting long-term as well as shortterm correlations in time-series of human-engineered features for sleep apnea [28], [29], [30] as well as for other medical use-cases [31], [32]. Although such models have incorporated valuable information by integrating the time-based component, other valuable aspects of the data are still lost, due the need for human-engineered features that summarize the data into distinct values.…”

Section: B Sleep Apnea Detectionmentioning

confidence: 99%

Automated Sleep Apnea Detection in Raw Respiratory Signals Using Long Short-Term Memory Neural Networks

Steenkiste

Groenendaal

Deschrijver

et al. 2019

IEEE J. Biomed. Health Inform.

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108

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Sleep apnea is one of the most common sleep disorders and the consequences of undiagnosed sleep apnea can be very severe, ranging from increased blood pressure to heart failure. However, many people are often unaware of their condition. The gold standard for diagnosing sleep apnea is an overnight polysomnography in a dedicated sleep laboratory. Yet, these tests are expensive and beds are limited as trained staff needs to analyze the entire recording. An automated detection method would allow a faster diagnosis and more patients to be analyzed. Most algorithms for automated sleep apnea detection use a set of human-engineered features, potentially missing important sleep apnea markers. In this work, we present an algorithm based on state-of-the-art deep learning models for automatically extracting features and detecting sleep apnea events in respiratory signals. The algorithm is evaluated on the Sleep-Heart-Health-Study-1 dataset and provides per-epoch sensitivity and specificity scores comparable to the state-of-the-art. Furthermore, when these predictions are mapped to the apnea-hypopnea-index, a considerable improvement in per-patient scoring is achieved over conventional methods. This work presents a powerful aid for trained staff to quickly diagnose sleep apnea.

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Section: B Sleep Apnea Detectionmentioning

confidence: 99%

Automated Sleep Apnea Detection in Raw Respiratory Signals Using Long Short-Term Memory Neural Networks

Steenkiste

Groenendaal

Deschrijver

et al. 2019

IEEE J. Biomed. Health Inform.

Self Cite

108

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“…LSTM models might be better suited to identifying/predicting sepsis and other infection management processes with their complexities as described above. Predicting positive blood cultures 12 h in advance in a group of ICU patients receiving blood culture tests resulted in an AUROC of 0.96 using this modelling technique [46]. Early sepsis detection based on defining the time of sepsis onset using ICD codes and timestamps achieved an AUROC of 0.93 [12].…”

Section: Outcome and Data Complexitymentioning

confidence: 99%

“…Thirty studies (57.7%) compared multiple ML techniques. Among these studies, the best performing techniques per research area were long short-term memory networks (LSTM) in the sepsis group [12,46], ANN in the HAI group [28], L1-regularized logistic regression (L1LR) in the SSI and other postoperative infections group [26], SVM in the infections (general) group [37], classification and regression tree (CART) in the microbiological test results group [47], and stochastic gradient boosting (SGB) in the musculoskeletal infections group [35]. However, as outlined below, the definition of the predicted outcome can be very heterogenous.…”

Section: Machine Learning Techniques In Usementioning

confidence: 99%

Machine learning in infection management using routine electronic health records: tools, techniques, and reporting of future technologies

Luz

Vollmer

Decruyenaere

et al. 2020

Clinical Microbiology and Infection

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Background: Machine learning (ML) is increasingly being used in many areas of health care. Its use in infection management is catching up as identified in a recent review in this journal. We present here a complementary review to this work. Objectives: To support clinicians and researchers in navigating through the methodological aspects of ML approaches in the field of infection management. Sources: A Medline search was performed with the keywords artificial intelligence, machine learning, infection*, and infectious disease* for the years 2014e2019. Studies using routinely available electronic hospital record data from an inpatient setting with a focus on bacterial and fungal infections were included. Content: Fifty-two studies were included and divided into six groups based on their focus. These studies covered detection/prediction of sepsis (n ¼ 19), hospital-acquired infections (n ¼ 11), surgical site infections and other postoperative infections (n ¼ 11), microbiological test results (n ¼ 4), infections in general (n ¼ 2), musculoskeletal infections (n ¼ 2), and other topics (urinary tract infections, deep fungal infections, antimicrobial prescriptions; n ¼ 1 each). In total, 35 different ML techniques were used. Logistic regression was applied in 18 studies followed by random forest, support vector machines, and artificial neural networks in 18, 12, and seven studies, respectively. Overall, the studies were very heterogeneous in their approach and their reporting. Detailed information on data handling and software code was often missing. Validation on new datasets and/or in other institutions was rarely done. Clinical studies on the impact of ML in infection management were lacking. Implications: Promising approaches for ML use in infectious diseases were identified. But building trust in these new technologies will require improved reporting. Explainability and interpretability of the models used were rarely addressed and should be further explored. Independent model validation and clinical studies evaluating the added value of ML approaches are needed.

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“…The two-phase deep learning model is based on LSTM neural networks which are a type of recurrent neural network. Such models have been successfully applied across several medical domains, including sleep apnea [45], [46], [47], [48]. A theoretical discussion on LSTM neural networks can be found in [44].…”

Section: B Deep Learning Modelmentioning

confidence: 99%

Portable Detection of Apnea and Hypopnea Events Using Bio-Impedance of the Chest and Deep Learning

Steenkiste

Groenendaal

Dreesen

et al. 2020

IEEE J. Biomed. Health Inform.

Self Cite

View full text Add to dashboard Cite

Accurate prediction of blood culture outcome in the intensive care unit using long short-term memory neural networks

Cited by 51 publications

References 18 publications

Automated Sleep Apnea Detection in Raw Respiratory Signals Using Long Short-Term Memory Neural Networks

Automated Sleep Apnea Detection in Raw Respiratory Signals Using Long Short-Term Memory Neural Networks

Machine learning in infection management using routine electronic health records: tools, techniques, and reporting of future technologies

Portable Detection of Apnea and Hypopnea Events Using Bio-Impedance of the Chest and Deep Learning

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