AIMC Topic: Machine Learning

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Discriminating malignant and benign clinical T1 renal masses on computed tomography: A pragmatic radiomics and machine learning approach.

Medicine
The aim of this study was to discriminate malignant and benign clinical T1 renal masses on routinely acquired computed tomography (CT) images using radiomics and machine learning techniques.Adult patients undergoing surgical resection and histopathol...

ShinyLearner: A containerized benchmarking tool for machine-learning classification of tabular data.

GigaScience
BACKGROUND: Classification algorithms assign observations to groups based on patterns in data. The machine-learning community have developed myriad classification algorithms, which are used in diverse life science research domains. Algorithm choice c...

Photographic and Video Deepfakes Have Arrived: How Machine Learning May Influence Plastic Surgery.

Plastic and reconstructive surgery
Advances in computer science and photography not only are pervasive but are also quantifiably influencing the practice of medicine. Recent progress in both software and hardware technology has translated into the design of advanced artificial neural ...

Forecasting beef production and quality using large-scale integrated data from Brazil.

Journal of animal science
With agriculture rapidly becoming a data-driven field, it is imperative to extract useful information from large data collections to optimize the production systems. We compared the efficacy of regression (linear regression or generalized linear regr...

Chaotic emperor penguin optimised extreme learning machine for microarray cancer classification.

IET systems biology
Microarray technology plays a significant role in cancer classification, where a large number of genes and samples are simultaneously analysed. For the efficient analysis of the microarray data, there is a great demand for the development of intellig...

External Validation of PATHFx Version 3.0 in Patients Treated Surgically and Nonsurgically for Symptomatic Skeletal Metastases.

Clinical orthopaedics and related research
BACKGROUND: PATHFx is a clinical decision-support tool based on machine learning capable of estimating the likelihood of survival after surgery for patients with skeletal metastases. The applicability of any machine-learning tool depends not only on ...

Physician understanding, explainability, and trust in a hypothetical machine learning risk calculator.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVE: Implementation of machine learning (ML) may be limited by patients' right to "meaningful information about the logic involved" when ML influences healthcare decisions. Given the complexity of healthcare decisions, it is likely that ML outp...

The rise and fall of the model for end-stage liver disease score and the need for an optimized machine learning approach for liver allocation.

Current opinion in organ transplantation
PURPOSE OF REVIEW: The Model for End-Stage Liver Disease (MELD) has been used to rank liver transplant candidates since 2002, and at the time bringing much needed objectivity to the liver allocation process. However, and despite numerous revisions to...