AIMC Topic: Decision Support Systems, Clinical
Clear Filters Showing 201 to 210 of 741 articles
A clinical deep learning framework for continually learning from cardiac signals across diseases, time, modalities, and institutions.
Nature communications
Jul 9, 2021
Deep learning algorithms trained on instances that violate the assumption of being independent and identically distributed (i.i.d.) are known to experience destructive interference, a phenomenon characterized by a degradation in performance. Such a v...
A transfer learning approach to facilitate ComBat-based harmonization of multicentre radiomic features in new datasets.
PloS one
Jul 1, 2021
PURPOSE: To facilitate the demonstration of the prognostic value of radiomics, multicenter radiomics studies are needed. Pooling radiomic features of such data in a statistical analysis is however challenging, as they are sensitive to the variability...
Young Adult
Adult
Uterine Cervical Neoplasms
Middle Aged
Tomography, X-Ray Computed
Cervix Uteri
Image Interpretation, Computer-Assisted
Humans
Female
Positron-Emission Tomography
Decision Support Systems, Clinical
Datasets as Topic
Machine Learning
Treatment Outcome
Aged
Follow-Up Studies
Chemoradiotherapy
Retrospective Studies
Aged, 80 and over
Magnetic Resonance Imaging
Augmented intelligence to predict 30-day mortality in patients with cancer.
Future oncology (London, England)
Jun 30, 2021
An augmented intelligence tool to predict short-term mortality risk among patients with cancer could help identify those in need of actionable interventions or palliative care services. An algorithm to predict 30-day mortality risk was developed us...
A Real-Time Clinical Decision Support System, for Mild Cognitive Impairment Detection, Based on a Hybrid Neural Architecture.
Computational and mathematical methods in medicine
Jun 21, 2021
Clinical procedure for mild cognitive impairment (MCI) is mainly based on clinical records and short cognitive tests. However, low suspicion and difficulties in understanding test cut-offs make diagnostic accuracy being low, particularly in primary c...
Prediction of caregiver quality of life in amyotrophic lateral sclerosis using explainable machine learning.
Scientific reports
Jun 10, 2021
Amyotrophic Lateral Sclerosis (ALS) is a rare neurodegenerative, fatal and currently incurable disease. People with ALS need support from informal caregivers due to the motor and cognitive decline caused by the disease. This study aims to identify ca...
Evaluating the Machine Learning Literature: A Primer and User's Guide for Psychiatrists.
The American journal of psychiatry
Jun 3, 2021
Robust diagnostic classification via Q-learning.
Scientific reports
Jun 3, 2021
Machine learning (ML) models have demonstrated the power of utilizing clinical instruments to provide tools for domain experts in gaining additional insights toward complex clinical diagnoses. In this context these tools desire two additional propert...
Artificial intelligence in oncology: Path to implementation.
Cancer medicine
May 7, 2021
In recent years, the field of artificial intelligence (AI) in oncology has grown exponentially. AI solutions have been developed to tackle a variety of cancer-related challenges. Medical institutions, hospital systems, and technology companies are de...
Artificial intelligence-enabled electrocardiograms for identification of patients with low ejection fraction: a pragmatic, randomized clinical trial.
Nature medicine
May 6, 2021
We have conducted a pragmatic clinical trial aimed to assess whether an electrocardiogram (ECG)-based, artificial intelligence (AI)-powered clinical decision support tool enables early diagnosis of low ejection fraction (EF), a condition that is unde...
Projecting COVID-19 disease severity in cancer patients using purposefully-designed machine learning.
BMC infectious diseases
May 4, 2021
BACKGROUND: Accurately predicting outcomes for cancer patients with COVID-19 has been clinically challenging. Numerous clinical variables have been retrospectively associated with disease severity, but the predictive value of these variables, and how...
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