Supervised classification techniques for prediction of mortality in adult patients with sepsis

Rodríguez, Andrés Herrera; Mendoza, Deibie; Ascuntar, Johana; Jaimes, Fabián

doi:10.1016/j.ajem.2020.09.013

Cited by 22 publications

(17 citation statements)

References 41 publications

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“…The presented results are comparable to the outcomes reported in similar studies ([ 70 ], for example, with the best mortality prediction model showing an AUC-ROC of 0.69, 95% CI: 0.61–0.76, in a sepsis cohort of 2510 patients with 11.5% positive cases). However, as indicated in another example [ 71 ], investigating 90-days mortality prediction models in a cohort of 800 patients (8% positive class), AUC-ROC can portray an overly-optimistic performance of a classifier risk score when applied to imbalanced data and AUC-PR provides better insight about the performance of a classifier by focusing on the minority class.…”

Section: Discussionsupporting

confidence: 88%

Comparison of Machine Learning Techniques for Mortality Prediction in a Prospective Cohort of Older Adults

Tedesco

Andrulli

Larsson

et al. 2021

IJERPH

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As global demographics change, ageing is a global phenomenon which is increasingly of interest in our modern and rapidly changing society. Thus, the application of proper prognostic indices in clinical decisions regarding mortality prediction has assumed a significant importance for personalized risk management (i.e., identifying patients who are at high or low risk of death) and to help ensure effective healthcare services to patients. Consequently, prognostic modelling expressed as all-cause mortality prediction is an important step for effective patient management. Machine learning has the potential to transform prognostic modelling. In this paper, results on the development of machine learning models for all-cause mortality prediction in a cohort of healthy older adults are reported. The models are based on features covering anthropometric variables, physical and lab examinations, questionnaires, and lifestyles, as well as wearable data collected in free-living settings, obtained for the “Healthy Ageing Initiative” study conducted on 2291 recruited participants. Several machine learning techniques including feature engineering, feature selection, data augmentation and resampling were investigated for this purpose. A detailed empirical comparison of the impact of the different techniques is presented and discussed. The achieved performances were also compared with a standard epidemiological model. This investigation showed that, for the dataset under consideration, the best results were achieved with Random UnderSampling in conjunction with Random Forest (either with or without probability calibration). However, while including probability calibration slightly reduced the average performance, it increased the model robustness, as indicated by the lower 95% confidence intervals. The analysis showed that machine learning models could provide comparable results to standard epidemiological models while being completely data-driven and disease-agnostic, thus demonstrating the opportunity for building machine learning models on health records data for research and clinical practice. However, further testing is required to significantly improve the model performance and its robustness.

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Section: Discussionsupporting

confidence: 88%

Comparison of Machine Learning Techniques for Mortality Prediction in a Prospective Cohort of Older Adults

Tedesco

Andrulli

Larsson

et al. 2021

IJERPH

View full text Add to dashboard Cite

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“…In order to predict the mortality of patients with suspected infection or sepsis in ED, the performance of AI was also been evaluated. A study compared the effects of several AIs in the classification and mortality prediction of sepsis patients in ED ( 33 ). A total of four supervised learning models, random forest, C4.5 decision tree, SVM and ANN were compared.…”

Section: Application Of Ai In the Prognosis And Risk Assessment Of Sepsismentioning

confidence: 99%

Artificial Intelligence for Clinical Decision Support in Sepsis

et al. 2021

Front. Med.

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Sepsis is one of the main causes of death in critically ill patients. Despite the continuous development of medical technology in recent years, its morbidity and mortality are still high. This is mainly related to the delay in starting treatment and non-adherence of clinical guidelines. Artificial intelligence (AI) is an evolving field in medicine, which has been used to develop a variety of innovative Clinical Decision Support Systems. It has shown great potential in predicting the clinical condition of patients and assisting in clinical decision-making. AI-derived algorithms can be applied to multiple stages of sepsis, such as early prediction, prognosis assessment, mortality prediction, and optimal management. This review describes the latest literature on AI for clinical decision support in sepsis, and outlines the application of AI in the prediction, diagnosis, subphenotyping, prognosis assessment, and clinical management of sepsis. In addition, we discussed the challenges of implementing and accepting this non-traditional methodology for clinical purposes.

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“…The already identified value creates a vector of input and output called supervised learning. The supervised classification techniques [ 39 ] were performed to predict the clinical decline factor, death rates in ICU, generalized the clinical predictions for complex sepsis prognosis.…”

Section: Background and Motivationmentioning

confidence: 99%

Outbreak COVID-19 in Medical Image Processing Using Deep Learning: A State-of-the-Art Review

Kaur

2021

Arch Computat Methods Eng

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From the month of December-19, the outbreak of Coronavirus (COVID-19) triggered several deaths and overstated every aspect of individual health. COVID-19 has been designated as a pandemic by World Health Organization. The circumstances placed serious trouble on every country worldwide, particularly with health arrangements and time-consuming responses. The increase in the positive cases of COVID-19 globally spread every day. The quantity of accessible diagnosing kits is restricted because of complications in detecting the existence of the illness. Fast and correct diagnosis of COVID-19 is a timely requirement for the prevention and controlling of the pandemic through suitable isolation and medicinal treatment. The significance of the present work is to discuss the outline of the deep learning techniques with medical imaging such as outburst prediction, virus transmitted indications, detection and treatment aspects, vaccine availability with remedy research. Abundant image resources of medical imaging as X-rays, Computed Tomography Scans, Magnetic Resonance imaging, formulate deep learning high-quality methods to fight against the pandemic COVID-19. The review presents a comprehensive idea of deep learning and its related applications in healthcare received over the past decade. At the last, some issues and confrontations to control the health crisis and outbreaks have been introduced. The progress in technology has contributed to developing individual’s lives. The problems faced by the radiologists during medical imaging techniques and deep learning approaches for diagnosing the COVID-19 infections have been also discussed.

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Supervised classification techniques for prediction of mortality in adult patients with sepsis

Cited by 22 publications

References 41 publications

Comparison of Machine Learning Techniques for Mortality Prediction in a Prospective Cohort of Older Adults

Comparison of Machine Learning Techniques for Mortality Prediction in a Prospective Cohort of Older Adults

Artificial Intelligence for Clinical Decision Support in Sepsis

Outbreak COVID-19 in Medical Image Processing Using Deep Learning: A State-of-the-Art Review

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