Machine Learning for Clinical Outcome Prediction

Shamout, Farah E.; Zhu, Tingting; Clifton, David A.

doi:10.1109/rbme.2020.3007816

Cited by 116 publications

(76 citation statements)

References 92 publications

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“…During a time in which a complex pandemic seems still to affect importantly healthcare services, a prognostic prediction tool can support clinical decision in hospitals or sanitary structures by providing data-driven elements for a better time planning and hospital organization [27][28][29].…”

Section: Discussionmentioning

confidence: 99%

Predicting SARS-CoV-2 infection duration at hospital admission:a deep learning solution

Liuzzi

Campagnini

Fanciullacci

et al. 2022

Med Biol Eng Comput

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cases are increasing around the globe with almost 5 million of deaths. We propose here a deep learning model capable of predicting the duration of the infection by means of information available at hospital admission. A total of 222 patients were enrolled in our observational study. Anagraphical and anamnestic data, COVID-19 signs and symptoms, COVID-19 therapy, hematochemical test results, and prior therapies administered to patients are used as predictors. A set of 55 features, all of which can be taken in the first hours of the patient's hospitalization, was considered. Different solutions were compared achieving the best performance with a sequential convolutional neural network-based model merged in an ensemble with two different meta-learners linked in cascade. We obtained a median absolute error of 2.7 days (IQR = 3.0) in predicting the duration of the infection; the error was equally distributed in the infection duration range. This tool could preemptively give an outlook of the COVID-19 patients' expected path and the associated hospitalization effort. The proposed solution could be viable in tackling the huge burden and the logistics complexity of hospitals or rehabilitation centers during the pandemic waves.

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Section: Discussionmentioning

confidence: 99%

Predicting SARS-CoV-2 infection duration at hospital admission:a deep learning solution

Liuzzi

Campagnini

Fanciullacci

et al. 2022

Med Biol Eng Comput

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“…Feature reduction in healthcare data analysis reduces the error rate and increases the performance of the system. Numerous ML applications are applied in the healthcare data analysis [10][11][12][13][14][15] for classifying the medical data.…”

Section: Related Workmentioning

confidence: 99%

A Secure Encrypted Classified Electronic Healthcare Data for Public Cloud Environment

Maathavan¹,

Venkatraman²

2022

Intelligent Automation &Amp; Soft Computing

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“…The caveat with machine learning models, however, is the need for very large data sets to accommodate clinical utility in humans. Training data sets for machine learning techniques sometimes need hundreds of samples to accurately predict the influence a biomarker has on clinical outcomes [63][64][65]. Although this large sample requirement may be daunting, it appears that many diagnostic fields are moving towards incorporating machine learning into their downstream analysis to help provide more information to clinicians.…”

Section: Challenges To Commercializationmentioning

confidence: 99%

The Microfluidic Toolbox for Analyzing Exosome Biomarkers of Aging

et al. 2021

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As the fields of aging and neurological disease expand to liquid biopsies, there is a need to identify informative biomarkers for the diagnosis of neurodegeneration and other age-related disorders such as cancers. A means of high-throughput screening of biomolecules relevant to aging can facilitate this discovery in complex biofluids, such as blood. Exosomes, the smallest of extracellular vesicles, are found in many biofluids and, in recent years, have been found to be excellent candidates as liquid biopsy biomarkers due to their participation in intercellular communication and various pathologies such as cancer metastasis. Recently, exosomes have emerged as novel biomarkers for age-related diseases. Hence, the study of exosomes, their protein and genetic cargo can serve as early biomarkers for age-associated pathologies, especially neurodegenerative diseases. However, a disadvantage of exosome studies includes a lack in standardization of isolating, detecting, and profiling exosomes for downstream analysis. In this review, we will address current techniques for high-throughput isolation and detection of exosomes through various microfluidic and biosensing strategies and how they may be adapted for the detection of biomarkers of age-associated disorders.

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Machine Learning for Clinical Outcome Prediction

Cited by 116 publications

References 92 publications

Predicting SARS-CoV-2 infection duration at hospital admission:a deep learning solution

Predicting SARS-CoV-2 infection duration at hospital admission:a deep learning solution

A Secure Encrypted Classified Electronic Healthcare Data for Public Cloud Environment

The Microfluidic Toolbox for Analyzing Exosome Biomarkers of Aging

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