Computer-Aided Diagnosis and Clinical Trials of Cardiovascular Diseases Based on Artificial Intelligence Technologies for Risk-Early Warning Model

The use of artificial intelligence in medicine is currently an issue of great interest, especially with regard to the diagnostic or predictive analysis of medical data. In order to achieve the regional medical and public health data analysis through artificial intelligence technologies, spark data a...

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Vydáno v:Journal of medical systems Ročník 43; číslo 7; s. 228 - 10
Hlavní autoři: Li, Bin, Ding, Shuai, Song, Guolei, Li, Jiajia, Zhang, Qian
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York Springer US 01.07.2019
Springer Nature B.V
Témata:
Acute Disease
Artificial Intelligence
Bayes Theorem
Bayesian analysis
Cardiovascular diseases
Chronic Disease
Clinical trials
Congestive heart failure
Data analysis
Data management
Data mining
Data Mining - methods
Data processing
Diagnosis, Computer-Assisted - methods
Diagnostic systems
Distributed Analytics and Deep Learning in Health Care
Health Informatics
Health Sciences
Heart failure
Heart Failure - diagnosis
Humans
Hypertension
Illnesses
Intelligence (information)
Logistic Models
Medical diagnosis
Medical research
Medicine
Medicine & Public Health
Optimization
Prognosis
Public health
Redundancy
Regional analysis
Regression analysis
Renal failure
Renal Insufficiency - diagnosis
Risk Assessment
Severity of Illness Index
Signs and symptoms
Statistics for Life Sciences
Stroke - diagnosis
Support Vector Machine
Support vector machines
Systems-Level Quality Improvement
Naive Bayesian regression
Clinical feature
Artificial Intelligence
Z-score standard
Chronic cardiovascular disease
Support vector machine
Logistic
ISSN:0148-5598, 1573-689X, 1573-689X
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Shrnutí:The use of artificial intelligence in medicine is currently an issue of great interest, especially with regard to the diagnostic or predictive analysis of medical data. In order to achieve the regional medical and public health data analysis through artificial intelligence technologies, spark data analysis is adopted as the research platform for hypertension patients, and artificial intelligence technologies are used to preprocess the data with inconsistency, redundancy, incompleteness, noise and error; Aiming at the unbalanced data sets, the Z-score standard is adopted to convert data into usable form suitable for data mining. And, the application of Logistic, Naive Bayesian regression, and support vector machine based on three groups of different prognosis in severe cases, including stroke, heart failure and renal failure symptoms, establish the risk early warning model for 3 years time. In addition, to select the optimal feature subset based on medicine big-data features, the model simplification and optimization are done in training process, the experimental results show that the feature subset selection can ensure the classification performance similar to the clinical features of the model. Therefore, according to chronic cardiovascular disease, acute cardiovascular events and cardiovascular events caused by critical illness events, we screen out the relevant prognosis of serious illness (stroke, heart failure, renal failure), which is related to the prognosis of serious illness. Targeted prevention has a guiding role and practical significance according to the results of artificial intelligence analysis.
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ISSN:0148-5598
1573-689X
1573-689X
DOI:10.1007/s10916-019-1346-x