Learning Using Partially Available Privileged Information and Label Uncertainty: Application in Detection of Acute Respiratory Distress Syndrome

Sabeti, Elyas; Drews, Joshua; Reamaroon, Narathip; Warner, Elisa; Sjoding, Michael W.; Gryak, Jonathan; Najarian, Kayvan

doi:10.1109/jbhi.2020.3008601

Cited by 15 publications

(14 citation statements)

References 39 publications

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“…During the training process, an SA provides feedback about the prediction accuracy. SA is widely used in data classification process for different applications such as early detection and prediction of diabetes [ 24 , 25 , 26 , 27 ], prediction of Alzheimer’s Disease [ 28 , 29 , 30 , 31 ], detection of Acute Respiratory Distress Syndrome [ 32 , 33 , 34 ] and EEG Signal Processing [ 35 , 36 , 37 , 38 ].…”

Section: Supervised Machine-learning Approachesmentioning

confidence: 99%

EMD-Based Method for Supervised Classification of Parkinson’s Disease Patients Using Balance Control Data

et al. 2022

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There has recently been increasing interest in postural stability aimed at gaining a better understanding of the human postural system. This system controls human balance in quiet standing and during locomotion. Parkinson’s disease (PD) is the most common degenerative movement disorder that affects human stability and causes falls and injuries. This paper proposes a novel methodology to differentiate between healthy individuals and those with PD through the empirical mode decomposition (EMD) method. EMD enables the breaking down of a complex signal into several elementary signals called intrinsic mode functions (IMFs). Three temporal parameters and three spectral parameters are extracted from each stabilometric signal as well as from its IMFs. Next, the best five features are selected using the feature selection method. The classification task is carried out using four known machine-learning methods, KNN, decision tree, Random Forest and SVM classifiers, over 10-fold cross validation. The used dataset consists of 28 healthy subjects (14 young adults and 14 old adults) and 32 PD patients (12 young adults and 20 old adults). The SVM method has a performance of 92% and the Dempster–Sahfer formalism method has an accuracy of 96.51%.

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Section: Supervised Machine-learning Approachesmentioning

confidence: 99%

EMD-Based Method for Supervised Classification of Parkinson’s Disease Patients Using Balance Control Data

et al. 2022

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“…Thus, the electronic searching algorithms can provide useful information in the management of critically ill patients. 20 AI can help to merge different administrative databases through algorithms that promote a more refined record linkage allowing automatic selection of variables of interest.…”

Section: Clinical Applications Of Electronic Medical Records For Patimentioning

confidence: 99%

Analytics with artificial intelligence to advance the treatment of acute respiratory distress syndrome

Zhang

Navarese

Zheng

et al. 2020

J Evidence Based Medicine

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Artificial intelligence (AI) has found its way into clinical studies in the era of big data. Acute respiratory distress syndrome (ARDS) or acute lung injury (ALI) is a clinical syndrome that encompasses a heterogeneous population. Management of such heterogeneous patient population is a big challenge for clinicians. With accumulating ALI datasets being publicly available, more knowledge could be discovered with sophisticated analytics. We reviewed literatures with big data analytics to understand the role of AI for improving the caring of patients with ALI/ARDS. Many studies have utilized the electronic medical records (EMR) data for the identification and prognostication of ARDS patients. As increasing number of ARDS clinical trials data is open to public, secondary analysis on these combined datasets provide a powerful way of finding solution to clinical questions with a new perspective. AI techniques such as Classification and Regression Tree (CART) and artificial neural networks (ANN) have also been successfully used in the investigation of ARDS problems. Individualized treatment of ARDS could be implemented with a support from AI as we are now able to classify ARDS into many subphenotypes by unsupervised machine learning algorithms. Interestingly, these subphenotypes show different responses to a certain intervention. However, current analytics involving ARDS have not fully incorporated information from omics such as transcriptome, proteomics, daily activities and environmental conditions. AI technology is assisting us to interpret complex data of ARDS patients and enable us to

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“…In zahlreichen Arbeiten werden komplexe Machine-Learning-Modelle miteinander verglichen. Fast immer werden retrospektiv Patientendaten aus elektronischen Patientenakten verwendet, klinische und eventuell radiologische Daten extrahiert und KI mit Medizinern verglichen [7]. Somit ist sicher schon häufig genug der Beweis angetreten worden, dass durch KI die diagnostische Performanz für die ARDS-Erkennung erhöht werden kann.…”

Section: Introductionunclassified

Potenzial von KI für die Behandlung des akuten Lungenversagens (ARDS)

Bickenbach

2024

Anästhesiol Intensivmed Notfallmed Schmerzther

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Learning Using Partially Available Privileged Information and Label Uncertainty: Application in Detection of Acute Respiratory Distress Syndrome

Cited by 15 publications

References 39 publications

EMD-Based Method for Supervised Classification of Parkinson’s Disease Patients Using Balance Control Data

EMD-Based Method for Supervised Classification of Parkinson’s Disease Patients Using Balance Control Data

Analytics with artificial intelligence to advance the treatment of acute respiratory distress syndrome

Potenzial von KI für die Behandlung des akuten Lungenversagens (ARDS)

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