AIMC Topic: Humans

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Analysis of Winning Experience and Technical Training Effect of Badminton Match Based on BP Neural Network.

Journal of healthcare engineering
The success or failure of badminton competition often depends on the training level of technical, physical, tactical, and psychological quality, as well as the competitive ability to comprehensively use these factors in the competition. Aiming at the...

Significance of Visible Non-Invasive Risk Attributes for the Initial Prediction of Heart Disease Using Different Machine Learning Techniques.

Computational intelligence and neuroscience
INTRODUCTION: Heart disease is emerging as the single most critical cause of death worldwide and is one of the costliest chronic conditions.

An Online Weighted Bayesian Fuzzy Clustering Method for Large Medical Data Sets.

Computational intelligence and neuroscience
With the rapid development of artificial intelligence, various medical devices and wearable devices have emerged, enabling people to collect various health data of themselves in hospitals or other places. This has led to a substantial increase in the...

An Entity Relation Extraction Method for Few-Shot Learning on the Food Health and Safety Domain.

Computational intelligence and neuroscience
In recent years, entity relation extraction has been a critical technique to help people analyze complex structured text data. However, there is no advanced research in food health and safety to help people analyze the complex concepts between food a...

A Study of Social and Behavioral Determinants of Health in Lung Cancer Patients Using Transformers-based Natural Language Processing Models.

AMIA ... Annual Symposium proceedings. AMIA Symposium
Social and behavioral determinants of health (SBDoH) have important roles in shaping people's health. In clinical research studies, especially comparative effectiveness studies, failure to adjust for SBDoH factors will potentially cause confounding i...

Integrating Multimodal Electronic Health Records for Diagnosis Prediction.

AMIA ... Annual Symposium proceedings. AMIA Symposium
Diagnosis prediction aims to predict the patient's future diagnosis based on their Electronic Health Records (EHRs). Most existing works adopt recurrent neural networks (RNNs) to model the sequential EHR data. However, they mainly utilize medical cod...

A Machine Learning Pipeline for Accurate COVID-19 Health Outcome Prediction using Longitudinal Electronic Health Records.

AMIA ... Annual Symposium proceedings. AMIA Symposium
Current COVID-19 predictive models primarily focus on predicting the risk of mortality, and rely on COVID-19 specific medical data such as chest imaging after COVID-19 diagnosis. In this project, we developed an innovative supervised machine learning...

Learning Predictive and Interpretable Timeseries Summaries from ICU Data.

AMIA ... Annual Symposium proceedings. AMIA Symposium
Machine learning models that utilize patient data across time (rather than just the most recent measurements) have increased performance for many risk stratification tasks in the intensive care unit. However, many of these models and their learned re...

First-line drug resistance profiling of : a machine learning approach.

AMIA ... Annual Symposium proceedings. AMIA Symposium
The persistence and emergence of new multi-drug resistant Mycobacterium tuberculosis (M. tb) strains continues to advance the devastating tuberculosis (TB) epidemic. Robust systems are needed to accurately and rapidly perform drug-resistance profilin...

Automated Mapping of Real-world Oncology Laboratory Data to LOINC.

AMIA ... Annual Symposium proceedings. AMIA Symposium
In this study we seek to determine the efficacy of using automated mapping methods to reduce the manual mapping burden of laboratory data to LOINC(r) on a nationwide electronic health record derived oncology specific dataset. We developed novel encod...