AIMC Topic: Machine Learning

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What can Neighbourhood Density effects tell us about word learning? Insights from a connectionist model of vocabulary development.

Journal of child language
In this paper, we investigate the effect of neighbourhood density (ND) on vocabulary size in a computational model of vocabulary development. A word has a high ND if there are many words phonologically similar to it. High ND words are more easily lea...

A method for modeling co-occurrence propensity of clinical codes with application to ICD-10-PCS auto-coding.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVE: Natural language processing methods for medical auto-coding, or automatic generation of medical billing codes from electronic health records, generally assign each code independently of the others. They may thus assign codes for closely re...

Evaluation of Underlying Lymphocytic Thyroiditis With Histogram Analysis Using Grayscale Ultrasound Images.

Journal of ultrasound in medicine : official journal of the American Institute of Ultrasound in Medicine
OBJECTIVES: The purpose of this study was to evaluate diagnostic performance of histogram analysis using grayscale ultrasound (US) images in the diagnosis of lymphocytic thyroiditis.

Adaptive Control of Exoskeleton Robots for Periodic Assistive Behaviours Based on EMG Feedback Minimisation.

PloS one
In this paper we propose an exoskeleton control method for adaptive learning of assistive joint torque profiles in periodic tasks. We use human muscle activity as feedback to adapt the assistive joint torque behaviour in a way that the muscle activit...

Computer-Aided Endoscopic Diagnosis Without Human-Specific Labeling.

IEEE transactions on bio-medical engineering
GOAL: Most state-of-the-art computer-aided endoscopic diagnosis methods require pixelwise labeled data to train various supervised machine learning models. However, it is a tedious and time-consuming work to collect sufficient precisely labeled image...

Machine-learning identifies substance-specific behavioral markers for opiate and stimulant dependence.

Drug and alcohol dependence
BACKGROUND: Recent animal and human studies reveal distinct cognitive and neurobiological differences between opiate and stimulant addictions; however, our understanding of the common and specific effects of these two classes of drugs remains limited...

Identification and individualized prediction of clinical phenotypes in bipolar disorders using neurocognitive data, neuroimaging scans and machine learning.

NeuroImage
Diagnosis, clinical management and research of psychiatric disorders remain subjective - largely guided by historically developed categories which may not effectively capture underlying pathophysiological mechanisms of dysfunction. Here, we report a ...

Multi-instance multi-label distance metric learning for genome-wide protein function prediction.

Computational biology and chemistry
Multi-instance multi-label (MIML) learning has been proven to be effective for the genome-wide protein function prediction problems where each training example is associated with not only multiple instances but also multiple class labels. To find an ...

Prediction of In-hospital Mortality in Emergency Department Patients With Sepsis: A Local Big Data-Driven, Machine Learning Approach.

Academic emergency medicine : official journal of the Society for Academic Emergency Medicine
OBJECTIVES: Predictive analytics in emergency care has mostly been limited to the use of clinical decision rules (CDRs) in the form of simple heuristics and scoring systems. In the development of CDRs, limitations in analytic methods and concerns wit...