Prediction of sepsis patients using machine learning approach: A meta-analysis

Islam, Mohaimenul; Nasrin, Tahmina; Walther, Bruno; Wu, Chieh-Chen; Yang, Hsuan-Chia; Li, Yu-Chuan

doi:10.1016/j.cmpb.2018.12.027

Cited by 165 publications

(104 citation statements)

References 33 publications

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“…Systematic reviews with meta-analysis of diagnostic studies might have heterogeneous findings due to differences in their study design [20]. Therefore, MMI and TNP independently utilized the Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) tool for assessing the quality of the included diagnostic studies.…”

Section: Assessment Of Bias Riskmentioning

confidence: 99%

Artificial Intelligence in Ophthalmology: A Meta-Analysis of Deep Learning Models for Retinal Vessels Segmentation

Islam

Poly

Walther

et al. 2020

JCM

Self Cite

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Background and Objective: Accurate retinal vessel segmentation is often considered to be a reliable biomarker of diagnosis and screening of various diseases, including cardiovascular diseases, diabetic, and ophthalmologic diseases. Recently, deep learning (DL) algorithms have demonstrated high performance in segmenting retinal images that may enable fast and lifesaving diagnoses. To our knowledge, there is no systematic review of the current work in this research area. Therefore, we performed a systematic review with a meta-analysis of relevant studies to quantify the performance of the DL algorithms in retinal vessel segmentation. Methods: A systematic search on EMBASE, PubMed, Google Scholar, Scopus, and Web of Science was conducted for studies that were published between 1 January 2000 and 15 January 2020. We followed the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) procedure. The DL-based study design was mandatory for a study’s inclusion. Two authors independently screened all titles and abstracts against predefined inclusion and exclusion criteria. We used the Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) tool for assessing the risk of bias and applicability. Results: Thirty-one studies were included in the systematic review; however, only 23 studies met the inclusion criteria for the meta-analysis. DL showed high performance for four publicly available databases, achieving an average area under the ROC of 0.96, 0.97, 0.96, and 0.94 on the DRIVE, STARE, CHASE_DB1, and HRF databases, respectively. The pooled sensitivity for the DRIVE, STARE, CHASE_DB1, and HRF databases was 0.77, 0.79, 0.78, and 0.81, respectively. Moreover, the pooled specificity of the DRIVE, STARE, CHASE_DB1, and HRF databases was 0.97, 0.97, 0.97, and 0.92, respectively. Conclusion: The findings of our study showed the DL algorithms had high sensitivity and specificity for segmenting the retinal vessels from digital fundus images. The future role of DL algorithms in retinal vessel segmentation is promising, especially for those countries with limited access to healthcare. More compressive studies and global efforts are mandatory for evaluating the cost-effectiveness of DL-based tools for retinal disease screening worldwide.

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Section: Assessment Of Bias Riskmentioning

confidence: 99%

Artificial Intelligence in Ophthalmology: A Meta-Analysis of Deep Learning Models for Retinal Vessels Segmentation

Islam

Poly

Walther

et al. 2020

JCM

Self Cite

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“…First of all, it appears possible for automated algorithms to identify patients at risk of having sepsis, either in real time ("sepsis detection") or in advance ("sepsis prediction") [2]. This can be achieved with a range of supervised learning algorithms trained on a dataset containing negative and positive instances of sepsis.…”

Section: Notable Applications Of Artificial Intelligence In Sepsismentioning

confidence: 99%

Clinical management of sepsis can be improved by artificial intelligence: yes

Komorowski

2019

Intensive Care Med

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“…Sepsis consists of a heterogeneous mix of phenotypes (4,5) , various degrees of disease complexities, and trajectories leading to recovery or death (6,7) . Different strategies have been pursued predicting deterioration (8)(9)(10) and managing patients with sepsis in critical care units (11) using physiological, clinical, and biomarker parameters. However, due to the heterogeneous nature of patients presenting to the ICU, and the diverse disease course that follows, it has been difficult to identify generalized models of disease (12) .…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Identifies Complicated Sepsis Course and Subsequent Mortality Based on 20 Genes in Peripheral Blood Immune Cells at 24 Hours post ICU admission

Banerjee

Mohammed

Wong

et al. 2020

Preprint

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A complicated clinical course for critically ill patients admitted to the ICU usually includes multiorgan dysfunction and subsequent death. Owning to the heterogeneity, complexity, and unpredictability of the disease progression, patient care is challenging. Identifying the predictors of complicated courses and subsequent mortality at the early stages of the disease and recognizing the trajectory of the disease from the vast array of longitudinal quantitative clinical data is difficult. Therefore, we attempted to identify novel early biomarkers and train the artificial intelligence systems to recognize the disease trajectories and subsequent clinical outcomes. Using the gene expression profile of peripheral blood cells obtained within 24 hours of PICU admission and numerous clinical data from 228 septic patients from pediatric ICU, we identified 20 differentially expressed genes that were predictive of complicated course outcomes and developed a new machine learning model. After 5-fold cross-validation with ten iterations, the overall mean area under the curve reached 0.82. This model was highly effective in identifying the clinical trajectories of the patients and mortality. Artificial intelligence systems identified eight out of twenty novel genetic markers SDC4 , CLEC5A , TCN1 , MS4A3 , HCAR3 , OLAH , PLCB1 and NLRP1 that help to predict sepsis severity or mortality. The discovery of eight novel genetic biomarkers related to the overactive innate immune system and neutrophils functions, and a new predictive machine learning method provides options to effectively recognize sepsis trajectories, modify real-time treatment options, improve prognosis, and patient survival.

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Prediction of sepsis patients using machine learning approach: A meta-analysis

Cited by 165 publications

References 33 publications

Artificial Intelligence in Ophthalmology: A Meta-Analysis of Deep Learning Models for Retinal Vessels Segmentation

Artificial Intelligence in Ophthalmology: A Meta-Analysis of Deep Learning Models for Retinal Vessels Segmentation

Clinical management of sepsis can be improved by artificial intelligence: yes

Machine Learning Identifies Complicated Sepsis Course and Subsequent Mortality Based on 20 Genes in Peripheral Blood Immune Cells at 24 Hours post ICU admission

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