AIMC Topic: Machine Learning

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Fast and Accurate Bacterial Species Identification in Urine Specimens Using LC-MS/MS Mass Spectrometry and Machine Learning.

Molecular & cellular proteomics : MCP
Fast identification of microbial species in clinical samples is essential to provide an appropriate antibiotherapy to the patient and reduce the prescription of broad-spectrum antimicrobials leading to antibioresistances. MALDI-TOF-MS technology has ...

Multivariate classification of schizophrenia and its familial risk based on load-dependent attentional control brain functional connectivity.

Neuropsychopharmacology : official publication of the American College of Neuropsychopharmacology
Patients with schizophrenia (SCZ), as well as their unaffected siblings (SIB), show functional connectivity (FC) alterations during performance of tasks involving attention. As compared with SCZ, these alterations are present in SIB to a lesser exten...

Adaptive latent similarity learning for multi-view clustering.

Neural networks : the official journal of the International Neural Network Society
Most existing clustering methods employ the original multi-view data as input to learn the similarity matrix which characterizes the underlying cluster structure shared by multiple views. This reduces the flexibility of multi-view clustering methods ...

Applying density-based outlier identifications using multiple datasets for validation of stroke clinical outcomes.

International journal of medical informatics
INTRODUCTION: Clinicians commonly use the modified Rankin Scale (mRS) and the Barthel Index (BI) to measure clinical outcome after stroke. These are potential targets in machine learning models for stroke outcome prediction. Therefore, the quality of...

Machine Learning Methods for Automated Quantification of Ventricular Dimensions.

Zebrafish
Medaka () and zebrafish () contribute substantially to our understanding of the genetic and molecular etiology of human cardiovascular diseases. In this context, the quantification of important cardiac functional parameters is fundamental. We have de...

A machine learning-based service for estimating quality of genomes using PATRIC.

BMC bioinformatics
BACKGROUND: Recent advances in high-volume sequencing technology and mining of genomes from metagenomic samples call for rapid and reliable genome quality evaluation. The current release of the PATRIC database contains over 220,000 genomes, and curre...

KETOS: Clinical decision support and machine learning as a service - A training and deployment platform based on Docker, OMOP-CDM, and FHIR Web Services.

PloS one
BACKGROUND AND OBJECTIVE: To take full advantage of decision support, machine learning, and patient-level prediction models, it is important that models are not only created, but also deployed in a clinical setting. The KETOS platform demonstrated in...

Can machine learning improve patient selection for cardiac resynchronization therapy?

PloS one
RATIONALE: Multiple clinical trials support the effectiveness of cardiac resynchronization therapy (CRT); however, optimal patient selection remains challenging due to substantial treatment heterogeneity among patients who meet the clinical practice ...

Machine Learning in Epidemiology and Health Outcomes Research.

Annual review of public health
Machine learning approaches to modeling of epidemiologic data are becoming increasingly more prevalent in the literature. These methods have the potential to improve our understanding of health and opportunities for intervention, far beyond our past ...