AIMC Topic: Machine Learning

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Using machine learning for predicting cervical cancer from Swedish electronic health records by mining hierarchical representations.

PloS one
Electronic health records (EHRs) contain rich documentation regarding disease symptoms and progression, but EHR data is challenging to use for diagnosis prediction due to its high dimensionality, relative scarcity, and substantial level of noise. We ...

Application of Machine Learning Techniques for Enuresis Prediction in Children.

European journal of pediatric surgery : official journal of Austrian Association of Pediatric Surgery ... [et al] = Zeitschrift fur Kinderchirurgie
INTRODUCTION:  As a subset of artificial intelligence, machine learning techniques (MLTs) may evaluate very large and raw datasets. In this study, the aim is to establish a model by MLT for the prediction of enuresis in children.

Challenges and Opportunities in Machine-Augmented Plant Stress Phenotyping.

Trends in plant science
Plant stress phenotyping is essential to select stress-resistant varieties and develop better stress-management strategies. Standardization of visual assessments and deployment of imaging techniques have improved the accuracy and reliability of stres...

Event-driven H control with critic learning for nonlinear systems.

Neural networks : the official journal of the International Neural Network Society
In this paper, we study an event-driven H control problem of continuous-time nonlinear systems. Initially, with the introduction of a discounted cost function, we convert the nonlinear H control problem into an event-driven nonlinear two-player zero-...

GeFeS: A generalized wrapper feature selection approach for optimizing classification performance.

Computers in biology and medicine
In this paper, we propose a generalized wrapper-based feature selection, called GeFeS, which is based on a parallel new intelligent genetic algorithm (GA). The proposed GeFeS works properly under different numerical dataset dimensions and sizes, care...

Predicting Psychological Distress Amid the COVID-19 Pandemic by Machine Learning: Discrimination and Coping Mechanisms of Korean Immigrants in the U.S.

International journal of environmental research and public health
The current study examined the predictive ability of discrimination-related variables, coping mechanisms, and sociodemographic factors on the psychological distress level of Korean immigrants in the U.S. amid the COVID-19 pandemic. Korean immigrants ...

Machine learning-based automated classification of headache disorders using patient-reported questionnaires.

Scientific reports
Classification of headache disorders is dependent on a subjective self-report from patients and its interpretation by physicians. We aimed to apply objective data-driven machine learning approaches to analyze patient-reported symptoms and test the fe...

Using autoencoders as a weight initialization method on deep neural networks for disease detection.

BMC medical informatics and decision making
BACKGROUND: As of today, cancer is still one of the most prevalent and high-mortality diseases, summing more than 9 million deaths in 2018. This has motivated researchers to study the application of machine learning-based solutions for cancer detecti...

Ordinal labels in machine learning: a user-centered approach to improve data validity in medical settings.

BMC medical informatics and decision making
BACKGROUND: Despite the vagueness and uncertainty that is intrinsic in any medical act, interpretation and decision (including acts of data reporting and representation of relevant medical conditions), still little research has focused on how to expl...