Early Prediction of Sepsis in EMR Records Using Traditional ML Techniques and Deep Learning LSTM Networks

Saqib, Mohammed; Sha, Ying; Wang, May D.

doi:10.1109/embc.2018.8513254

Cited by 46 publications

(23 citation statements)

References 14 publications

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“…In our review one study in the sepsis group that used reinforcement learning applied a random forest model to explain the developed model and reported these findings in their supplementary materials [19]. Two studies provided a web interface to study variable importance in the developed ML models [11,35]. However, just recently a debate has unfolded as to whether interpretable predictive models, such as logistic regression, should be preferred over explainable models (demonstrated in Fig.…”

Section: Model Interpretabilitymentioning

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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Section: Model Interpretabilitymentioning

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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“…Duke University Hospital has officially launched Sepsis Watch that identifies incipient sepsis cases and raises an alarm (Strickland, 2018). Several other deep learning (convolutional—long/short‐term memory) (Lin et al., 2018; Saqib, Sha, & Wang, 2018)‐based prediction algorithms are also presented in literature that predict sepsis with high efficiency. Recent temporal patterns (RTPs) used in conjunction with SVM classifier outperforms some other state‐of‐the‐art machine‐learning techniques (Khoshnevisan et al., 2018).…”

Section: Biomarker‐based Label‐free Sepsis Diagnosismentioning

confidence: 99%

A perspective on sepsis pathogenesis, biomarkers and diagnosis: A concise survey

2020

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Sepsis is a potentially fatal physiological state caused by an imbalance in the body's immune response to an infection and is one of the most common causes for deaths in the non‐coronary intensive care unit worldwide. In this article, the state of art on sepsis is presented in a manner that facilitates easy comprehension also for the non‐medical researchers by introducing sepsis, its causes, extent and comparison of diagnostic techniques (conventional labeled as well as label‐free detection). The article also provides a comprehensive discussion on sepsis biomarkers, to help researchers from multi‐disciplinary domain in developing devices and ideas to complement the existing sepsis diagnosis systems for quick and premature detection of the physiological condition and reduce mortality by means of early treatments.

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“…The consensus is to focus on early detection of the microbe, as well as its possible mechanisms of resistance, in order to provide the necessary antimicrobial treatment and avoid a further increase on AMR [12,102,103]. Sepsis is a complex process, and the possibility of applying traditional scores (for example, Sequential Organ Failure Assessment (SOFA) or Acute Physiologic Assessment and Chronic Health Evaluation (APACHE) scores) [93], combined with new scores and early diagnosis, provides future hope for patients [91,[94][95][96]. Recently, Mount Sinai Hospital lunched a precise post-op model to predict prostate cancer disease progression and clinical failure [97].…”

Section: Antimicrobial-pathogen Interactions: Overcoming Antimicrobiamentioning

confidence: 99%

Computational Health Engineering Applied to Model Infectious Diseases and Antimicrobial Resistance Spread

2019

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Infectious diseases are the primary cause of mortality worldwide. The dangers of infectious disease are compounded with antimicrobial resistance, which remains the greatest concern for human health. Although novel approaches are under investigation, the World Health Organization predicts that by 2050, septicaemia caused by antimicrobial resistant bacteria could result in 10 million deaths per year. One of the main challenges in medical microbiology is to develop novel experimental approaches, which enable a better understanding of bacterial infections and antimicrobial resistance. After the introduction of whole genome sequencing, there was a great improvement in bacterial detection and identification, which also enabled the characterization of virulence factors and antimicrobial resistance genes. Today, the use of in silico experiments jointly with computational and machine learning offer an in depth understanding of systems biology, allowing us to use this knowledge for the prevention, prediction, and control of infectious disease. Herein, the aim of this review is to discuss the latest advances in human health engineering and their applicability in the control of infectious diseases. An in-depth knowledge of host–pathogen–protein interactions, combined with a better understanding of a host’s immune response and bacterial fitness, are key determinants for halting infectious diseases and antimicrobial resistance dissemination.

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Early Prediction of Sepsis in EMR Records Using Traditional ML Techniques and Deep Learning LSTM Networks

Cited by 46 publications

References 14 publications

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

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

A perspective on sepsis pathogenesis, biomarkers and diagnosis: A concise survey

Computational Health Engineering Applied to Model Infectious Diseases and Antimicrobial Resistance Spread

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