ArticleJournal of medical systems2019
Computer-Aided Diagnosis and Clinical Trials of Cardiovascular Diseases Based on Artificial Intelligence Technologies for Risk-Early Warning Model.
Article in Journal of medical systems, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
11 citing papers in PubMed, 1 synthesis or guideline pooled it, 29 citations in OpenAlex.
- Artificial intelligence in early warning systems for infectious disease surveillance: a systematic review.Frontiers in public health · 2025Pooled it
- Challenges and best practices for digital unstructured data enrichment in health research: A systematic narrative review.PLOS digital health · 2023Article
- AI-Driven Decision Support for Early Detection of Cardiac Events: Unveiling Patterns and Predicting Myocardial Ischemia.Journal of personalized medicine · 2023Article
- Hyperparameter optimization for cardiovascular disease data-driven prognostic system.Visual computing for industry, biomedicine, and art · 2023Article
- Multivariate time-series sensor vital sign forecasting of cardiovascular and chronic respiratory diseases.Sustainable computing : informatics and systems · 2023Article
- A Machine Learning Model for Detection of Coronary Artery Disease Using Noninvasive Clinical Parameters.Life (Basel, Switzerland) · 2022Article
- Applying Artificial Intelligence to Wearable Sensor Data to Diagnose and Predict Cardiovascular Disease: A Review.Sensors (Basel, Switzerland) · 2022Review
- Construction of Xinjiang metabolic syndrome risk prediction model based on interpretable models.BMC public health · 2022Article
- A Machine-Learning-Based System for Prediction of Cardiovascular and Chronic Respiratory Diseases.Journal of healthcare engineering · 2021Article
- Adoption of Snake Variable Model-Based Method in Segmentation and Quantitative Calculation of Cardiac Ultrasound Medical Images.Journal of healthcare engineering · 2021Article
- Convolutional neural network for the diagnosis of early gastric cancer based on magnifying narrow band imaging.Gastric cancer : official journal of the International Gastric Cancer Association and the Japanese Gastric Cancer Association · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.