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...
| Publicado en: | Journal of Medical Systems Vol. 43; no. 7 |
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| Autores principales: | , , , , |
| Formato: | equations & formulas tables/charts Journal Article |
| Publicado: |
Springer Nature
Jul2019
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=137182951&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 137182951 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01485598 4N0 jtl: Journal of Medical Systems issn: 01485598 maglogo: N pubinfo: dt: Jul2019 vid: 43 iid: 7 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 137182951 137182951 137182951 10.1007/s10916-019-1346-x 137182951 ppct: 1 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Computer-Aided Diagnosis and Clinical Trials of Cardiovascular Diseases Based on Artificial Intelligence Technologies for Risk-Early Warning Model. aug: au: Li, Bin Ding, Shuai Song, Guolei Li, Jiajia Zhang, Qian affil: The First Affiliated Hospital of Bengbu Medical College, 233004, Bengbu, Anhui, China sug: subj: Cardiovascular Diseases Diagnosis Diagnosis, Computer Assisted Artificial Intelligence Risk Assessment Methods Clinical Trials Methods Models, Statistical Data Mining Regression Methods Machine Learning Methods ab: 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. pubtype: Academic Journal doctype: equations & formulas tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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