Benchmarking Predictive Models in Electronic Health Records: Sepsis Length of Stay Prediction

Alsinglawi, Belal; Alnajjar, Fady; Mubin, Omar; Novoa, Mauricio; Karajeh, Ola; Darwish, Omar

doi:10.1007/978-3-030-44041-1_24

Cited by 7 publications

(2 citation statements)

References 18 publications

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“… They have a wide range of data types that they can accommodate, for example, 2D imagery data and complex 3D data such as medical imagery and remote sensing. In addition, they can use HSI data's spectral and spatial domains in both standalone and linked ways [ 106 – 108 ]. They provide architects a lot of versatility in terms of layer types, blocks, units, and depth.…”

Section: Machine Learning-based Techniques For Hsi Classificationmentioning

confidence: 99%

Hyperspectral Image Classification: Potentials, Challenges, and Future Directions

Datta

Mallick

Bhoi

et al. 2022

Computational Intelligence and Neuroscience

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Recent imaging science and technology discoveries have considered hyperspectral imagery and remote sensing. The current intelligent technologies, such as support vector machines, sparse representations, active learning, extreme learning machines, transfer learning, and deep learning, are typically based on the learning of the machines. These techniques enrich the processing of such three-dimensional, multiple bands, and high-resolution images with their precision and fidelity. This article presents an extensive survey depicting machine-dependent technologies’ contributions and deep learning on landcover classification based on hyperspectral images. The objective of this study is three-fold. First, after reading a large pool of Web of Science (WoS), Scopus, SCI, and SCIE-indexed and SCIE-related articles, we provide a novel approach for review work that is entirely systematic and aids in the inspiration of finding research gaps and developing embedded questions. Second, we emphasize contemporary advances in machine learning (ML) methods for identifying hyperspectral images, with a brief, organized overview and a thorough assessment of the literature involved. Finally, we draw the conclusions to assist researchers in expanding their understanding of the relationship between machine learning and hyperspectral images for future research.

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Section: Machine Learning-based Techniques For Hsi Classificationmentioning

confidence: 99%

Hyperspectral Image Classification: Potentials, Challenges, and Future Directions

Datta

Mallick

Bhoi

et al. 2022

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

show abstract

“…Most importantly, these prognostic models can make reliable predictions based on the data at admission. Electronic health records have been utilized in developing machine learning models for predicting the length of stay (LOS) in patients suffering from sepsis and COVID-19 in ICU [ 29 , 30 , 31 , 32 ]. Alabbad et al [ 33 ] used a random forest classifier to predict the ICU requirement of COVID-19 patients and estimated their LOS in ICU with an accuracy of 94.16%, using data from King Fahad University Hospital, Saudi Arabia.…”

Section: Related Workmentioning

confidence: 99%

Prognostic Model of ICU Admission Risk in Patients with COVID-19 Infection Using Machine Learning

et al. 2022

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With the onset of the COVID-19 pandemic, the number of critically sick patients in intensive care units (ICUs) has increased worldwide, putting a burden on ICUs. Early prediction of ICU requirement is crucial for efficient resource management and distribution. Early-prediction scoring systems for critically ill patients using mathematical models are available, but are not generalized for COVID-19 and Non-COVID patients. This study aims to develop a generalized and reliable prognostic model for ICU admission for both COVID-19 and non-COVID-19 patients using best feature combination from the patient data at admission. A retrospective cohort study was conducted on a dataset collected from the pulmonology department of Moscow City State Hospital between 20 April 2020 and 5 June 2020. The dataset contains ten clinical features for 231 patients, of whom 100 patients were transferred to ICU and 131 were stable (non-ICU) patients. There were 156 COVID positive patients and 75 non-COVID patients. Different feature selection techniques were investigated, and a stacking machine learning model was proposed and compared with eight different classification algorithms to detect risk of need for ICU admission for both COVID-19 and non-COVID patients combined and COVID patients alone. C-reactive protein (CRP), chest computed tomography (CT), lung tissue affected (%), age, admission to hospital, and fibrinogen parameters at hospital admission were found to be important features for ICU-requirement risk prediction. The best performance was produced by the stacking approach, with weighted precision, sensitivity, F1-score, specificity, and overall accuracy of 84.45%, 84.48%, 83.64%, 84.47%, and 84.48%, respectively, for both types of patients, and 85.34%, 85.35%, 85.11%, 85.34%, and 85.35%, respectively, for COVID-19 patients only. The proposed work can help doctors to improve management through early prediction of the risk of need for ICU admission of patients during the COVID-19 pandemic, as the model can be used for both types of patients.

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Impact of IoT Technology and Social Networking Sites on Mothers, Teachers, and Children with Autism Spectrum Disorder in Jordan

Al-Sarayrah,

Abo-Zaitoon,

Aldosary

et al. 2024

Lecture Notes on Data Engineering and Communications Technologies

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Benchmarking Predictive Models in Electronic Health Records: Sepsis Length of Stay Prediction

Cited by 7 publications

References 18 publications

Hyperspectral Image Classification: Potentials, Challenges, and Future Directions

Hyperspectral Image Classification: Potentials, Challenges, and Future Directions

Prognostic Model of ICU Admission Risk in Patients with COVID-19 Infection Using Machine Learning

Impact of IoT Technology and Social Networking Sites on Mothers, Teachers, and Children with Autism Spectrum Disorder in Jordan

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