AIMC Topic: Machine Learning

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Predictive analysis on the factors associated with birth Outcomes: A machine learning perspective.

International journal of medical informatics
BACKGROUND: Recent studies reveal that around 1.9 million stillbirths occur annually worldwide, with Sub-Saharan Africa having among the highest cases. Some Sub-Saharan African countries, including Ghana, failed to meet Millennium Development Goal 5 ...

Machine Learning to Predict Prostate Artery Embolization Outcomes.

Cardiovascular and interventional radiology
PURPOSE: This study leverages pre-procedural data and machine learning (ML) techniques to predict outcomes at one year following prostate artery embolization (PAE).

Machine learning predicts the serum PFOA and PFOS levels in pregnant women: Enhancement of fatty acid status on model performance.

Environment international
Human exposure to per- and polyfluoroalkyl substances (PFASs) has received considerable attention, particularly in pregnant women because of their dramatic changes in physiological status and dietary patterns. Predicting internal PFAS exposure in pre...

Building a challenging medical dataset for comparative evaluation of classifier capabilities.

Computers in biology and medicine
Since the 2000s, digitalization has been a crucial transformation in our lives. Nevertheless, digitalization brings a bulk of unstructured textual data to be processed, including articles, clinical records, web pages, and shared social media posts. A...

It takes one to know one-Machine learning for identifying OBGYN abstracts written by ChatGPT.

International journal of gynaecology and obstetrics: the official organ of the International Federation of Gynaecology and Obstetrics

Validation of a Machine Learning Algorithm, EVendo, for Predicting Esophageal Varices in Hepatocellular Carcinoma.

Digestive diseases and sciences
BACKGROUND: Treatment with atezolizumab and bevacizumab has become standard of care for advanced unresectable hepatocellular carcinoma (HCC) but carries an increased gastrointestinal bleeding risk. Therefore, patients are often required to undergo es...

Iterative Regression of Corrective Baselines (IRCB): A New Model for Quantitative Spectroscopy.

Journal of chemical information and modeling
In this work, a new model with broad utility for quantitative spectroscopy development is reported. A primary objective of this work is to create a novel modeling procedure that may allow for higher automation of the model development process. The fu...

Machine learning predicts cerebral vasospasm in patients with subarachnoid haemorrhage.

EBioMedicine
BACKGROUND: Cerebral vasospasm (CV) is a feared complication which occurs after 20-40% of subarachnoid haemorrhage (SAH). It is standard practice to admit patients with SAH to intensive care for an extended period of resource-intensive monitoring. We...