AIMC Topic: Unsupervised Machine Learning
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Unsupervised Machine Learning for the Discovery of Latent Clusters in COVID-19 Patients Using Electronic Health Records.
Studies in health technology and informatics
Jun 26, 2020
The goal of this paper was to apply unsupervised machine learning techniques towards the discovery of latent clusters in COVID-19 patients. Over 6,000 adult patients tested positive for the SARS-CoV-2 infection at the Mount Sinai Health System in New...
Using Unsupervised Learning to Identify Clinical Subtypes of Alzheimer's Disease in Electronic Health Records.
Studies in health technology and informatics
Jun 16, 2020
Identifying subtypes of Alzheimer's Disease (AD) can lead towards the creation of personalized interventions and potentially improve outcomes. In this study, we use UK primary care electronic health records (EHR) from the CALIBER resource to identify...
Novel Imaging Revealing Inner Dynamics for Cardiovascular Waveform Analysis via Unsupervised Manifold Learning.
Anesthesia and analgesia
May 1, 2020
BACKGROUND: Cardiovascular waveforms contain information for clinical diagnosis. By learning and organizing the subtle change of waveform morphology from large amounts of raw waveform data, unsupervised manifold learning helps delineate a high-dimens...
Application of unsupervised learning and process simulation for energy optimization of a WWTP under various weather conditions.
Water science and technology : a journal of the International Association on Water Pollution Research
Apr 1, 2020
This paper outlines a hybrid modeling approach to facilitate weather-based operation and energy optimization for the largest Italian wastewater treatment plant (WWTP). Two clustering methods, K-means algorithm and Gaussian mixture model (GMM) based o...
Artificial intelligence in the diagnosis of cardiovascular disease.
Revista da Associacao Medica Brasileira (1992)
Dec 1, 2019
Artificial intelligence (AI) is a field of computer science that aims to mimic human thought processes. AI techniques have been applied in cardiovascular medicine to explore novel genotypes and phenotypes in existing diseases, improve the quality of ...
Assessing clinical heterogeneity in sepsis through treatment patterns and machine learning.
Journal of the American Medical Informatics Association : JAMIA
Dec 1, 2019
OBJECTIVE: To use unsupervised topic modeling to evaluate heterogeneity in sepsis treatment patterns contained within granular data of electronic health records.
Maxwell®: An Unsupervised Learning Approach for 5P Medicine.
Studies in health technology and informatics
Aug 21, 2019
In the 5P medicine (Personalized, Preventive, Participative, Predictive and Pluri-expert), the general trend is to process data by displacing the barycenter of the information from hospital centered systems to the patient centered ones through his pe...
Deciphering complex metabolite mixtures by unsupervised and supervised substructure discovery and semi-automated annotation from MS/MS spectra.
Faraday discussions
Aug 15, 2019
Complex metabolite mixtures are challenging to unravel. Mass spectrometry (MS) is a widely used and sensitive technique for obtaining structural information of complex mixtures. However, just knowing the molecular masses of the mixture's constituents...
Impact of Artificial Intelligence on Interventional Cardiology: From Decision-Making Aid to Advanced Interventional Procedure Assistance.
JACC. Cardiovascular interventions
Jul 22, 2019
Access to big data analyzed by supercomputers using advanced mathematical algorithms (i.e., deep machine learning) has allowed for enhancement of cognitive output (i.e., visual imaging interpretation) to previously unseen levels and promises to funda...
Cardiac Catheterization
Deep Learning
Patient Selection
Unsupervised Machine Learning
Robotics
Humans
Clinical Decision-Making
Diffusion of Innovation
Predictive Value of Tests
Workflow
Supervised Machine Learning
Cardiovascular Diseases
Diagnosis, Computer-Assisted
Therapy, Computer-Assisted
Machine Learning
Decision Support Techniques
Multi-omics integration-a comparison of unsupervised clustering methodologies.
Briefings in bioinformatics
Jul 19, 2019
With the recent developments in the field of multi-omics integration, the interest in factors such as data preprocessing, choice of the integration method and the number of different omics considered had increased. In this work, the impact of these f...
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