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Mitigating Bias in Clinical Machine Learning Models
Purpose of reviewIdentifying the risk for and addressing bias in clinical machine learning models is essential to reap its full benefits and ensure...
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Bridging the Worlds of Pharmacometrics and Machine Learning
Precision medicine requires individualized modeling of disease and drug dynamics, with machine learning-based computational techniques gaining...
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Integrated machine learning and deep learning for predicting diabetic nephropathy model construction, validation, and interpretability
ObjectiveTo construct a risk prediction model for assisted diagnosis of Diabetic Nephropathy (DN) using machine learning algorithms, and to validate...
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Machine learning and deep learning for classifying the justification of brain CT referrals
ObjectivesTo train the machine and deep learning models to automate the justification analysis of radiology referrals in accordance with iGuide...
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Machine Learning to Predict Adult Cochlear Implant Candidacy
Purpose of ReviewThe purpose of this review is to summarize candidacy criteria and commonly used referral guidelines for adult cochlear implant (CI)...
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Machine learning algorithm predicts urethral stricture following transurethral prostate resection
PurposeTo predict the post transurethral prostate resection(TURP) urethral stricture probability by applying different machine learning algorithms...
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Exploring the potential of machine learning in gynecological care: a review
Gynecological health remains a critical aspect of women’s overall well-being, with profound implications for maternal and reproductive outcomes. This...
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Machine Learning and Artificial Intelligence to Improve Interpretation of Urodynamics
Purpose of ReviewWe sought to review and discuss the current state and future trajectory of machine learning in interpretation of urodynamics...
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Utilization of machine learning for dengue case screening
Dengue causes approximately 10.000 deaths and 100 million symptomatic infections annually worldwide, making it a significant public health concern....
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Systematic review using a spiral approach with machine learning
With the accelerating growth of the academic corpus, doubling every 9 years, machine learning is a promising avenue to make systematic review...
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Machine learning models to predict systemic inflammatory response syndrome after percutaneous nephrolithotomy
ObjectiveThe objective of this study was to develop and evaluate the performance of machine learning models for predicting the possibility of...
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A retrospective study on machine learning-assisted stroke recognition for medical helpline calls
Advanced stroke treatment is time-dependent and, therefore, relies on recognition by call-takers at prehospital telehealth services to ensure fast...
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Reassessing acquired neonatal intestinal diseases using unsupervised machine learning
BackgroundAcquired neonatal intestinal diseases have an array of overlapping presentations and are often labeled under the dichotomous classification...
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Machine learning, deep learning and hernia surgery. Are we pushing the limits of abdominal core health? A qualitative systematic review
IntroductionThis systematic review aims to evaluate the use of machine learning and artificial intelligence in hernia surgery.
MethodsThe PRISMA...
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A microdiscectomy surgical video annotation framework for supervised machine learning applications
PurposeLumbar discectomy is among the most common spine procedures in the US, with 300,000 procedures performed each year. Like other surgical...
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Application of machine learning in measurement of ageing and geriatric diseases: a systematic review
BackgroundAs the ageing population continues to grow in many countries, the prevalence of geriatric diseases is on the rise. In response, healthcare...
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Improved pediatric ICU mortality prediction for respiratory diseases: machine learning and data subdivision insights
The growing concern of pediatric mortality demands heightened preparedness in clinical settings, especially within intensive care units (ICUs). As...
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Machine learning based on SEER database to predict distant metastasis of thyroid cancer
ObjectiveDistant metastasis of thyroid cancer often indicates poor prognosis, and it is important to identify patients who have developed distant...
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Machine learning methods for adult OSAHS risk prediction
BackgroundObstructive sleep apnea hypopnea syndrome (OSAHS) is a common disease that can cause multiple organ damage in the whole body. Our aim was...
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Machine learning algorithms for predicting COVID-19 mortality in Ethiopia
BackgroundCoronavirus disease 2019 (COVID-19), a global public health crisis, continues to pose challenges despite preventive measures. The daily...
