AIMC Topic: Machine Learning

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Research and Google Trend for Human Neuropsychiatric Disorders and Machine Learning: A Brief Report.

Psychiatria Danubina
OBJECTIVES: The key characteristics of this study are to highlight the research trend pertaining to the use of machine learning in the diagnosis and management of neuropsychiatric conditions.

Prognosticating Outcome in Pancreatic Head Cancer With the use of a Machine Learning Algorithm.

Technology in cancer research & treatment
The purpose of this project is to identify prognostic features in resectable pancreatic head adenocarcinoma and use these features to develop a machine learning algorithm that prognosticates survival for patients pursuing pancreaticoduodenectomy. A...

Characterizing Opioid Overdoses Using Emergency Medical Services Data : A Case Definition Algorithm Enhanced by Machine Learning.

Public health reports (Washington, D.C. : 1974)
OBJECTIVES: Tracking nonfatal overdoses in the escalating opioid overdose epidemic is important but challenging. The objective of this study was to create an innovative case definition of opioid overdose in North Carolina emergency medical services (...

Feature Selection is Critical for 2-Year Prognosis in Advanced Stage High Grade Serous Ovarian Cancer by Using Machine Learning.

Cancer control : journal of the Moffitt Cancer Center
INTRODUCTION: Accurate prediction of patient prognosis can be especially useful for the selection of best treatment protocols. Machine Learning can serve this purpose by making predictions based upon generalizable clinical patterns embedded within le...

Investigating Predictors of Preserved Cognitive Function in Older Women Using Machine Learning: Women's Health Initiative Memory Study.

Journal of Alzheimer's disease : JAD
BACKGROUND: Identification of factors that may help to preserve cognitive function in late life could elucidate mechanisms and facilitate interventions to improve the lives of millions of people. However, the large number of potential factors associa...

Identification of the Neural Circuit Underlying Episodic Memory Deficit in Amnestic Mild Cognitive Impairment via Machine Learning on Gray Matter Volume.

Journal of Alzheimer's disease : JAD
Based on whole-brain gray matter volume (GMV), we used relevance vector regression to predict the Rey's Auditory Verbal Learning Test Delayed Recall (AVLT-DR) scores of individual amnestic mild cognitive impairment (aMCI) patient. The whole-brain GMV...

Machine Learning to Identify Gene Interactions from High-Throughput Mutant Crosses.

Methods in molecular biology (Clifton, N.J.)
Advances in molecular genetics through high-throughput gene mutagenesis and genetic crossing have enabled gene interaction mapping across whole genomes. Detecting gene interactions in even small microbial genomes relies on measuring growth phenotypes...

Construction of a Risk Prediction Model for Hospital-Acquired Pulmonary Embolism in Hospitalized Patients.

Clinical and applied thrombosis/hemostasis : official journal of the International Academy of Clinical and Applied Thrombosis/Hemostasis
The purpose of this study is to establish a novel pulmonary embolism (PE) risk prediction model based on machine learning (ML) methods and to evaluate the predictive performance of the model and the contribution of variables to the predictive perform...

Differentiation of Intrahepatic Cholangiocarcinoma and Hepatic Lymphoma Based on Radiomics and Machine Learning in Contrast-Enhanced Computer Tomography.

Technology in cancer research & treatment
This study aimed to explore the ability of texture parameters combining with machine learning methods in distinguishing intrahepatic cholangiocarcinoma (ICCA) and hepatic lymphoma (HL). A total of 28 patients with HL and 101 patients with ICCA were...

Machine Learning Approaches on High Throughput NGS Data to Unveil Mechanisms of Function in Biology and Disease.

Cancer genomics & proteomics
In this review, the fundamental basis of machine learning (ML) and data mining (DM) are summarized together with the techniques for distilling knowledge from state-of-the-art omics experiments. This includes an introduction to the basic mathematical ...