ArticleAnnals of operations research2021
Handling of uncertainty in medical data using machine learning and probability theory techniques: a review of 30 years (1991-2020).
Article in Annals of operations research, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers, 1 of them a synthesis that pooled it.
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Who cites it
30 citing papers in PubMed, 1 synthesis or guideline pooled it.
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- Reducing Emergency Diagnostic Uncertainty with TRACE: Triage and Risk Assessment via Cost Estimation.The western journal of emergency medicine · 2026Article
- The constrained disorder principle emphasizes the importance of variability boundaries for systems to function effectively.Journal of medicine and life · 2025Article
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- Using the Constrained Disorder Principle to Navigate Uncertainties in Biology and Medicine: Refining Fuzzy Algorithms.Biology · 2024Review
- Prediction of white blood cell count during exercise: a comparison between standalone and hybrid intelligent algorithms.Scientific reports · 2024Article
- Using ANN for thermal neutron shield designing for BNCT treatment room.Scientific reports · 2024Article
- Bayesian uncertainty estimation for detection of long-tailed and unseen conditions in medical images.Journal of medical imaging (Bellingham, Wash.) · 2023Article
- Using Constrained-Disorder Principle-Based Systems to Improve the Performance of Digital Twins in Biological Systems.Biomimetics (Basel, Switzerland) · 2023Review
- Uncertainty aware training to improve deep learning model calibration for classification of cardiac MR images.Medical image analysis · 2023Article
- Determination of optimum intensity and duration of exercise based on the immune system response using a machine-learning model.Scientific reports · 2023Article
- Artificial Intelligence-Assisted Chest X-ray for the Diagnosis of COVID-19: A Systematic Review and Meta-Analysis.Diagnostics (Basel, Switzerland) · 2023Review
- A large-scale machine learning study of sociodemographic factors contributing to COVID-19 severity.Frontiers in big data · 2023Article
- Breast Cancer Dataset, Classification and Detection Using Deep Learning.Healthcare (Basel, Switzerland) · 2022Review
- Uncertain imputation for time-series forecasting: Application to COVID-19 daily mortality prediction.PLOS digital health · 2022Article
Corrections and comments
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Authors and funding
15 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Understanding the data and reaching accurate conclusions are of paramount importance in the present era of big data. Machine learning and probability theory methods have been widely used for this purpose in various fields. One critically important yet less explored aspect is capturing and analyzing uncertainties in the data and model. Proper quantification of uncertainty helps to provide valuable information to obtain accurate diagnosis. This paper reviewed related studies conducted in the last 30 years (from 1991 to 2020) in handling uncertainties in medical data using probability theory and machine learning techniques. Medical data is more prone to uncertainty due to the presence of noise in the data. So, it is very important to have clean medical data without any noise to get accurate diagnosis. The sources of noise in the medical data need to be known to address this issue. Based on the medical data obtained by the physician, diagnosis of disease, and treatment plan are prescribed. Hence, the uncertainty is growing in healthcare and there is limited knowledge to address these problems. Our findings indicate that there are few challenges to be addressed in handling the uncertainty in medical raw data and new models. In this work, we have summarized various methods employed to overcome this problem. Nowadays, various novel deep learning techniques have been proposed to deal with such uncertainties and improve the performance in decision making.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.