Artificial intelligence: practical primer for clinical research in cardiovascular disease
Artificial intelligence (AI) has begun to permeate and reform the field of medicine and
cardiovascular medicine. Impacting about 100 million patients in the United States, the …
cardiovascular medicine. Impacting about 100 million patients in the United States, the …
Improving methods of identifying anaphylaxis for medical product safety surveillance using natural language processing and machine learning
We sought to determine whether machine learning and natural language processing (NLP)
applied to electronic medical records could improve performance of automated health-care …
applied to electronic medical records could improve performance of automated health-care …
[HTML][HTML] Using deep learning to identify high-risk patients with heart failure with reduced ejection fraction
Background: Deep Learning (DL) has not been well-established as a method to identify high-
risk patients among patients with heart failure (HF). Objectives: This study aimed to use DL …
risk patients among patients with heart failure (HF). Objectives: This study aimed to use DL …
HSGA: A Hybrid LSTM-CNN Self-Guided Attention to predict the future diagnosis from discharge narratives
The prognosis of a patient's re-admission and the forecast of future diagnoses is a critical
task in the process of inferring clinical outcomes. The discharge summaries recorded in the …
task in the process of inferring clinical outcomes. The discharge summaries recorded in the …
Use of classification algorithms in health care
H Khan, A Srivastav, AK Mishra - Big data analytics and intelligence: A …, 2020 - emerald.com
A detailed description will be provided of all the classification algorithms that have been
widely used in the domain of medical science. The foundation will be laid by giving a …
widely used in the domain of medical science. The foundation will be laid by giving a …
Comparison of temporal and non-temporal features effect on machine learning models quality and interpretability for chronic heart failure patients
K Balabaeva, S Kovalchuk - Procedia Computer Science, 2019 - Elsevier
Chronic diseases are complex systems that can be described by various heteroscedastic
data that varies in time. The goal of this work is to determine whether historical data helps to …
data that varies in time. The goal of this work is to determine whether historical data helps to …
[HTML][HTML] Совершенствование возможностей оценки сердечно-сосудистого риска при помощи методов машинного обучения
АВ Гусев, ДВ Гаврилов, РЭ Новицкий… - Российский …, 2021 - cyberleninka.ru
Рост распространенности сердечно-сосудистых заболеваний (ССЗ) определяет
важность их прогноза, необходимость точной стратификации рисков …
важность их прогноза, необходимость точной стратификации рисков …
Churn prediction using customers' implicit behavioral patterns and deep learning
A Tanveer - 2019 - research.sabanciuniv.edu
The processes of market globalization are rapidly changing the competitive conditions of the
business and financial sectors. With the emergence of new competitors and increasing …
business and financial sectors. With the emergence of new competitors and increasing …
Using Deep Learning with Canadian Primary Care Data for Disease Diagnosis
H Zafari, L Kosowan, JT Lam, W Peeler… - Deep Learning for …, 2021 - Springer
The majority of Canadian primary care systems record patient data in the form of Electronic
Medical Records (EMR). EMRs hold structured, semi-structured and unstructured …
Medical Records (EMR). EMRs hold structured, semi-structured and unstructured …
[图书][B] On Prediction of Early Signs of Alzheimer's—A Machine Learning Framework
A Alsaedi - 2021 - search.proquest.com
Dementia is a collective term used to indicate a loss of memory functions with the presence
of at least one additional loss of a major cognitive ability that hinders a person's previous …
of at least one additional loss of a major cognitive ability that hinders a person's previous …