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...

Celý popis

Uloženo v:

Podrobná bibliografie
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
On-line přístup:	Získat plný text
Tagy:	Přidat tag Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!

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.
AbstractList	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.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. 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. 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.
ArticleNumber	228
Author	Ding, Shuai Song, Guolei Zhang, Qian Li, Bin Li, Jiajia
Author_xml	– sequence: 1 givenname: Bin surname: Li fullname: Li, Bin email: libin2010000@163.com organization: The First Affiliated Hospital of Bengbu Medical College, School of Management HeFei University of Technology – sequence: 2 givenname: Shuai surname: Ding fullname: Ding, Shuai organization: School of Management HeFei University of Technology – sequence: 3 givenname: Guolei surname: Song fullname: Song, Guolei organization: The First Affiliated Hospital of Bengbu Medical College – sequence: 4 givenname: Jiajia surname: Li fullname: Li, Jiajia organization: The First Affiliated Hospital of Bengbu Medical College – sequence: 5 givenname: Qian surname: Zhang fullname: Zhang, Qian organization: The First Affiliated Hospital of Bengbu Medical College
BackLink	https://www.ncbi.nlm.nih.gov/pubmed/31197490$$D View this record in MEDLINE/PubMed
BookMark	eNp90UFvFCEYBmBiauy2-gO8GBIvXlBgBhiO61i1SY2JWaO3CQPfrFQWtjBj2r_hL5ZmqyZN9AKX5-X7wnuCjmKKgNBTRl8yStWrwqhmklCmCWtaSa4foBUTqiGy01-P0IqytiNC6O4YnZRySSnVUqpH6LhhTKtW0xX62afdfpkhk7V34PAbb7YxFV-wiQ73wUdvTcCb7E0oOE24N9n59MMUuwSTqy9gChT8up4Op4jXefaTt9Xj8zhDCH4L0QLegP0WU0hbX_WUMv7ky3dyZnK4wV9Mjj5u8YfkIDxGD6c6DJ7c3afo89uzTf-eXHx8d96vL4htFJ_JZDrpuJ46y0Fqo8wEWkg-qlFIrZtmbJVQ0GjOxWgEdNopI2g3UtdJMwnZnKIXh3f3OV0tUOZh54utC5sIaSkD563kbdtqUenze_QyLTnW7apqtGBCcFbVszu1jDtwwz77nck3w-_froAdgM2plAzTH8LocNvocGh0qI0Ot40O1zWj7mWsn83sU5yz8eG_SX5IljolbiH_XfrfoV-Gu7ZK
CitedBy_id	crossref_primary_10_1186_s42492_023_00143_6 crossref_primary_10_3390_s22208002 crossref_primary_10_3390_jpm13091421 crossref_primary_10_1016_j_slast_2025_100283 crossref_primary_10_1186_s12889_022_12617_y crossref_primary_10_1007_s10120_019_00992_2 crossref_primary_10_1080_00365521_2022_2113427 crossref_primary_10_1007_s10489_023_04487_7 crossref_primary_10_1016_j_suscom_2023_100868 crossref_primary_10_1016_j_procs_2025_04_083 crossref_primary_10_1007_s13198_024_02307_w crossref_primary_10_1155_2021_2425482 crossref_primary_10_3390_life12111933 crossref_primary_10_1002_ett_3838 crossref_primary_10_1155_2021_2621655 crossref_primary_10_3389_fpubh_2025_1609615 crossref_primary_10_3390_informatics8030055 crossref_primary_10_1371_journal_pdig_0000347
Cites_doi	10.1148/radiol.2017171920 10.15439/2017F219 10.1089/dia.2011.0202 10.1007/s10916-013-9934-7 10.1007/s11655-014-1822-6 10.1109/IEMBS.2009.5333597 10.1007/978-3-319-23024-5_43 10.1109/ACCESS.2018.2825352 10.1109/SACI.2014.6840059 10.1016/j.inpa.2016.12.002 10.1016/j.patcog.2015.08.009 10.1111/j.1747-0803.2007.00163.x 10.1016/j.ins.2017.08.093 10.1145/1835804.1835830 10.1016/j.amjmed.2016.09.031 10.1007/978-3-642-02298-2_19 10.1371/journal.pone.0132466 10.1088/1742-6596/787/1/012008 10.1002/ags3.12173 10.1109/RIOS.2016.7529489 10.1142/S0219878910002087 10.1097/01.CCM.0000109444.02324.AD 10.1016/j.artmed.2016.01.004 10.1227/01.neu.0000484053.82181.f6
ContentType	Journal Article
Copyright	Springer Science+Business Media, LLC, part of Springer Nature 2019 Journal of Medical Systems is a copyright of Springer, (2019). All Rights Reserved.
Copyright_xml	– notice: Springer Science+Business Media, LLC, part of Springer Nature 2019 – notice: Journal of Medical Systems is a copyright of Springer, (2019). All Rights Reserved.
DBID	AAYXX CITATION CGR CUY CVF ECM EIF NPM 3V. 7QF 7QO 7QQ 7RV 7SC 7SE 7SP 7SR 7TA 7TB 7TK 7U5 7X7 7XB 88C 88E 88I 8AL 8AO 8BQ 8FD 8FE 8FG 8FH 8FI 8FJ 8FK ABUWG AFKRA ARAPS AZQEC BBNVY BENPR BGLVJ BHPHI CCPQU DWQXO F28 FR3 FYUFA GHDGH GNUQQ H8D H8G HCIFZ JG9 JQ2 K7- K9. KB0 KR7 L7M LK8 L~C L~D M0N M0S M0T M1P M2P M7P NAPCQ P5Z P62 P64 PHGZM PHGZT PJZUB PKEHL PPXIY PQEST PQGLB PQQKQ PQUKI Q9U 7X8
DOI	10.1007/s10916-019-1346-x
DatabaseName	CrossRef Medline MEDLINE MEDLINE (Ovid) MEDLINE MEDLINE PubMed ProQuest Central (Corporate) Aluminium Industry Abstracts Biotechnology Research Abstracts Ceramic Abstracts Nursing & Allied Health Database Computer and Information Systems Abstracts Corrosion Abstracts Electronics & Communications Abstracts Engineered Materials Abstracts Materials Business File Mechanical & Transportation Engineering Abstracts Neurosciences Abstracts Solid State and Superconductivity Abstracts ProQuest Health & Medical Collection ProQuest Central (purchase pre-March 2016) Healthcare Administration Database (Alumni) Medical Database (Alumni Edition) Science Database (Alumni Edition) Computing Database (Alumni Edition) ProQuest Pharma Collection METADEX Technology Research Database ProQuest SciTech Collection ProQuest Technology Collection ProQuest Natural Science Collection Hospital Premium Collection Hospital Premium Collection (Alumni Edition) ProQuest Central (Alumni) (purchase pre-March 2016) ProQuest Central (Alumni) ProQuest Central UK/Ireland Advanced Technologies & Computer Science Collection ProQuest Central Essentials Biological Science Collection ProQuest Central Technology Collection Natural Science Collection ProQuest One Community College ProQuest Central Korea ANTE: Abstracts in New Technology & Engineering Engineering Research Database Health Research Premium Collection Health Research Premium Collection (Alumni) ProQuest Central Student Aerospace Database Copper Technical Reference Library SciTech Premium Collection Materials Research Database ProQuest Computer Science Collection Computer Science Database ProQuest Health & Medical Complete (Alumni) Nursing & Allied Health Database (Alumni Edition) Civil Engineering Abstracts Advanced Technologies Database with Aerospace ProQuest Biological Science Collection Computer and Information Systems Abstracts Academic Computer and Information Systems Abstracts Professional Computing Database Health & Medical Collection (Alumni Edition) Healthcare Administration Database Medical Database Science Database Biological Science Database Nursing & Allied Health Premium Advanced Technologies & Aerospace Database ProQuest Advanced Technologies & Aerospace Collection Biotechnology and BioEngineering Abstracts ProQuest Central Premium ProQuest One Academic ProQuest Health & Medical Research Collection ProQuest One Academic Middle East (New) ProQuest One Health & Nursing ProQuest One Academic Eastern Edition (DO NOT USE) ProQuest One Applied & Life Sciences ProQuest One Academic (retired) ProQuest One Academic UKI Edition ProQuest Central Basic MEDLINE - Academic
DatabaseTitle	CrossRef MEDLINE Medline Complete MEDLINE with Full Text PubMed MEDLINE (Ovid) Materials Research Database Computer Science Database ProQuest Central Student ProQuest Advanced Technologies & Aerospace Collection ProQuest Central Essentials ProQuest Computer Science Collection Computer and Information Systems Abstracts SciTech Premium Collection Materials Business File ProQuest One Applied & Life Sciences Engineered Materials Abstracts Health Research Premium Collection Natural Science Collection Health & Medical Research Collection Biological Science Collection ProQuest Central (New) ProQuest Medical Library (Alumni) ANTE: Abstracts in New Technology & Engineering Advanced Technologies & Aerospace Collection Aluminium Industry Abstracts ProQuest Science Journals (Alumni Edition) ProQuest Biological Science Collection ProQuest One Academic Eastern Edition Electronics & Communications Abstracts ProQuest Hospital Collection ProQuest Technology Collection Health Research Premium Collection (Alumni) Ceramic Abstracts Biological Science Database Neurosciences Abstracts ProQuest Hospital Collection (Alumni) Biotechnology and BioEngineering Abstracts Nursing & Allied Health Premium ProQuest Health & Medical Complete ProQuest One Academic UKI Edition ProQuest Health Management (Alumni Edition) ProQuest Nursing & Allied Health Source (Alumni) Solid State and Superconductivity Abstracts Engineering Research Database ProQuest One Academic ProQuest One Academic (New) Technology Collection Technology Research Database Computer and Information Systems Abstracts – Academic ProQuest One Academic Middle East (New) Mechanical & Transportation Engineering Abstracts ProQuest Health & Medical Complete (Alumni) ProQuest Central (Alumni Edition) ProQuest One Community College ProQuest One Health & Nursing ProQuest Natural Science Collection ProQuest Pharma Collection ProQuest Central Aerospace Database Copper Technical Reference Library ProQuest Health & Medical Research Collection Biotechnology Research Abstracts Health and Medicine Complete (Alumni Edition) ProQuest Central Korea Advanced Technologies Database with Aerospace Civil Engineering Abstracts ProQuest Computing ProQuest Central Basic ProQuest Science Journals ProQuest Computing (Alumni Edition) ProQuest Health Management ProQuest Nursing & Allied Health Source ProQuest SciTech Collection METADEX Computer and Information Systems Abstracts Professional Advanced Technologies & Aerospace Database ProQuest Medical Library Corrosion Abstracts ProQuest Central (Alumni) MEDLINE - Academic
DatabaseTitleList	MEDLINE - Academic Materials Research Database MEDLINE
Database_xml	– sequence: 1 dbid: NPM name: PubMed url: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed sourceTypes: Index Database – sequence: 2 dbid: BENPR name: ProQuest Central url: https://www.proquest.com/central sourceTypes: Aggregation Database
DeliveryMethod	fulltext_linktorsrc
Discipline	Medicine Public Health
EISSN	1573-689X
EndPage	10
ExternalDocumentID	31197490 10_1007_s10916_019_1346_x
Genre	Journal Article
GroupedDBID	--- -53 -5D -5G -BR -EM -Y2 -~C .86 .GJ .VR 04C 06C 06D 0R~ 0VY 199 1SB 2.D 203 28- 29L 2J2 2JN 2JY 2KG 2KM 2LR 2P1 2VQ 2~H 30V 36B 3SX 3V. 4.4 406 408 409 40E 53G 5GY 5QI 5RE 5VS 67Z 6NX 77K 78A 7RV 7X7 88E 88I 8AO 8FE 8FG 8FH 8FI 8FJ 8TC 8UJ 95- 95. 95~ 96X AAAVM AABHQ AACDK AAHNG AAIAL AAJBT AAJKR AANXM AANZL AARHV AARTL AASML AATNV AATVU AAUYE AAWCG AAWTL AAYIU AAYQN AAYTO AAYZH ABAKF ABBBX ABBXA ABDZT ABECU ABFTV ABHLI ABHQN ABIPD ABJNI ABJOX ABKCH ABKTR ABMNI ABMQK ABNWP ABQBU ABQSL ABSXP ABTEG ABTHY ABTKH ABTMW ABULA ABUWG ABWNU ABXPI ACAOD ACDTI ACGFO ACGFS ACGOD ACHSB ACHXU ACIHN ACIWK ACKNC ACMDZ ACMLO ACOKC ACOMO ACPIV ACPRK ACREN ACUDM ACZOJ ADBBV ADHHG ADHIR ADIMF ADINQ ADJJI ADKNI ADKPE ADRFC ADTPH ADURQ ADYFF ADYOE ADZKW AEAQA AEBTG AEFIE AEFQL AEGAL AEGNC AEJHL AEJRE AEKMD AEMSY AENEX AEOHA AEPYU AESKC AETLH AEVLU AEXYK AFBBN AFEXP AFKRA AFLOW AFQWF AFRAH AFWTZ AFYQB AFZKB AGAYW AGDGC AGGDS AGJBK AGMZJ AGQEE AGQMX AGRTI AGWIL AGWZB AGYKE AHAVH AHBYD AHIZS AHKAY AHMBA AHSBF AHYZX AIAKS AIGIU AIIXL AILAN AITGF AJBLW AJRNO AJZVZ ALIPV ALMA_UNASSIGNED_HOLDINGS ALWAN AMKLP AMTXH AMXSW AMYLF AMYQR AOCGG AQUVI ARAPS ARMRJ ASPBG AVWKF AXYYD AYJHY AZFZN AZQEC B-. BA0 BAPOH BBNVY BBWZM BDATZ BENPR BGLVJ BGNMA BHPHI BKEYQ BMSDO BPHCQ BSONS BVXVI CAG CCPQU COF CS3 CSCUP DDRTE DL5 DNIVK DPUIP DU5 DWQXO EBD EBLON EBS EIHBH EIOEI EJD EMB EMOBN EN4 EPAXT ESBYG EX3 F5P FEDTE FERAY FFXSO FIGPU FINBP FNLPD FRRFC FSGXE FWDCC FYUFA G-Y G-Z GGCAI GGRSB GJIRD GNUQQ GNWQR GQ6 GQ7 GQ8 GRRUI GXS H13 HCIFZ HF~ HG5 HG6 HMCUK HMJXF HQYDN HRMNR HVGLF HZ~ I-F I09 IHE IJ- IKXTQ IMOTQ ITM IWAJR IXC IXE IZIGR IZQ I~X I~Z J-C J0Z JBSCW JCJTX JZLTJ K6V K7- KDC KOV KOW KPH LAK LK8 LLZTM M0N M0T M1P M2P M4Y M7P MA- MK0 N2Q NAPCQ NB0 NDZJH NPVJJ NQJWS NU0 O9- O93 O9G O9I O9J OAM OVD P19 P2P P62 P9S PF0 PQQKQ PROAC PSQYO PT4 PT5 Q2X QOK QOR QOS R4E R89 R9I RHV RNI RNS ROL RPX RSV RZC RZD RZK S16 S1Z S26 S27 S28 S37 S3B SAP SCLPG SDE SDH SDM SHX SISQX SJYHP SMT SNE SNPRN SNX SOHCF SOJ SPISZ SRMVM SSLCW STPWE SV3 SZ9 SZN T13 T16 TEORI TN5 TSG TSK TSV TT1 TUC U2A U9L UG4 UKHRP UOJIU UTJUX UZXMN VC2 VFIZW W23 W48 WH7 WJK WK8 WOW YLTOR Z45 Z7R Z7U Z7X Z7Z Z81 Z82 Z83 Z87 Z88 Z8M Z8R Z8T Z8W Z92 ZMTXR ~A9 ~EX 77I AAPKM AAYXX ABBRH ABDBE ABFSG ABRTQ ACSTC ADHKG AEZWR AFDZB AFFHD AFHIU AFOHR AGQPQ AHPBZ AHWEU AIXLP ATHPR AYFIA CITATION PHGZM PHGZT PJZUB PPXIY PQGLB CGR CUY CVF ECM EIF NPM 7QF 7QO 7QQ 7SC 7SE 7SP 7SR 7TA 7TB 7TK 7U5 7XB 8AL 8BQ 8FD 8FK F28 FR3 H8D H8G JG9 JQ2 K9. KR7 L7M L~C L~D P64 PKEHL PQEST PQUKI Q9U 7X8 PUEGO
ID	FETCH-LOGICAL-c372t-fa86d29f8c2e69a7afe9562b7b569933b4757e39225ba5e89d7a508b0d86af563
IEDL.DBID	M2P
ISICitedReferencesCount	22
ISICitedReferencesURI	http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000471604200001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
ISSN	0148-5598 1573-689X
IngestDate	Thu Oct 02 11:08:25 EDT 2025 Tue Nov 04 22:30:33 EST 2025 Wed Feb 19 02:30:39 EST 2025 Tue Nov 18 21:05:53 EST 2025 Sat Nov 29 05:35:00 EST 2025 Fri Feb 21 02:37:16 EST 2025
IsPeerReviewed	true
IsScholarly	true
Issue	7
Keywords	Naive Bayesian regression Clinical feature Artificial Intelligence Z-score standard Chronic cardiovascular disease Support vector machine Logistic
Language	English
LinkModel	DirectLink
MergedId	FETCHMERGED-LOGICAL-c372t-fa86d29f8c2e69a7afe9562b7b569933b4757e39225ba5e89d7a508b0d86af563
Notes	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23
PMID	31197490
PQID	2239515521
PQPubID	54050
PageCount	10
ParticipantIDs	proquest_miscellaneous_2246244495 proquest_journals_2239515521 pubmed_primary_31197490 crossref_primary_10_1007_s10916_019_1346_x crossref_citationtrail_10_1007_s10916_019_1346_x springer_journals_10_1007_s10916_019_1346_x
PublicationCentury	2000
PublicationDate	2019-07-01
PublicationDateYYYYMMDD	2019-07-01
PublicationDate_xml	– month: 07 year: 2019 text: 2019-07-01 day: 01
PublicationDecade	2010
PublicationPlace	New York
PublicationPlace_xml	– name: New York – name: United States
PublicationTitle	Journal of medical systems
PublicationTitleAbbrev	J Med Syst
PublicationTitleAlternate	J Med Syst
PublicationYear	2019
Publisher	Springer US Springer Nature B.V
Publisher_xml	– name: Springer US – name: Springer Nature B.V
References	AhmadFIsaNAMHussainZIntelligent Medical Disease Diagnosis Using Improved Hybrid Genetic Algorithm - Multilayer Perceptron NetworkJ. Med. Syst.2013372993410.1007/s10916-013-9934-7 QianPZhouJJiangYLiangFZhaoKWangSSuK-HMuzicRFJrMulti-view maximum entropy clustering by jointly leveraging inter-view collaborations and intra-view-weighted attributesIEEE Access20186285942861010.1109/ACCESS.2018.2825352 Peinado, I., Arredondo, M.T., Villalba, E., et al., Patient interaction in homecare systems to treat cardiovascular diseases in the long term. In: International Conference of the IEEE Engineering in Medicine and Biology Society, Minneapolis, pp 308–311, 2009. SunBLiYZhangLThe Intelligent System of Cardiovascular Disease Diagnosis Based on Extension Data Mining. Cutting-Edge Research Topics on Multiple Criteria Decision Making2009Berlin HeidelbergSpringer LeeHGNohKLeeBJCardiovascular Disease Diagnosis Method by Emerging Patterns. International Conference on Advanced Data Mining & Applications2006BerlinSpringer-Verlag CanadasJSánchez-MolinaJARodríguezFImproving automatic climate control with decision support techniques to minimize disease effects in greenhouse tomatoesInformation Processing in Agriculture201741506310.1016/j.inpa.2016.12.002 LeeHGNohKLeeBJCardiovascular Disease Diagnosis Method by Emerging Patterns. Advanced Data Mining and Applications2006Berlin HeidelbergSpringer ParkSeong HoHanKyunghwaMethodologic Guide for Evaluating Clinical Performance and Effect of Artificial Intelligence Technology for Medical Diagnosis and PredictionRadiology2018286380080910.1148/radiol.2017171920 Valavanis I K, Mougiakakou S G, Grimaldi K A, et al. Analysis of Postprandial Lipemia as a Cardiovascular Disease Risk Factor using Genetic and Clinical Information: An Artificial Neural Network Perspective. Conference proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference, 2008:4609-4612, 2008. BondyCACongenital Cardiovascular Disease in Turner SyndromeCongenit. Heart Dis.20083121510.1111/j.1747-0803.2007.00163.x Sun, B., Li, Y., and Zhang, L., The Intelligent System of Cardiovascular Disease Diagnosis Based on Extension Data Mining. Communications in Computer & Information Science 2008:133–140. Ghareh BaghiALindénMAn Internet-Based Tool for Pediatric Cardiac Disease Diagnosis using Intelligent Phonocardiography. International Internet of Things Summit2015New YorkSpringer International Publishing NaganoHBig data, information and communication technology, artificial intelligence, Internet of things: How important are they for gastroenterological surgery?Annals of Gastroenterological Surgery20182316616610.1002/ags3.12173 ShankaracharyaODMallickMJava-based diabetes type 2 prediction tool for better diagnosisDiabetes Technol. Ther.20121432512561:STN:280:DC%2BC383ms1Smtw%3D%3D10.1089/dia.2011.0202 Khosla A, Cao Y, Lin C C Y, et al. An integrated machine learning approach to stroke prediction. Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, 2010: 183–192. Hudson, D. L., and Cohen, M. E., Use of intelligent agents in the diagnosis of cardiac disorders. Comput. Cardiol. IEEE, 2002. NesBMGutvikCRLavieCJPersonalized Activity Intelligence (PAI) for Prevention of Cardiovascular Disease and Promotion of Physical ActivityAm. J. Med.2017130332833610.1016/j.amjmed.2016.09.031 GlassTFKnappJAmburnPUse of artificial intelligence to identify cardiovascular compromise in a model of hemorrhagic shockCrit. Care Med.200432245045610.1097/01.CCM.0000109444.02324.AD Feshki, M. G., and Shijani, O. S., Improving the heart disease diagnosis by evolutionary algorithm of PSO and Feed Forward Neural Network. Artificial Intelligence & Robotics. IEEE, 2016. Babič, F., Olejár, J., Vantová, Z. et al., Predictive and Descriptive Analysis for Heart Disease Diagnosis. Comput. Sci. Inf. Syst.. IEEE, 2017. Alhadidi, T., and Salah, R. B., A new intelligent method for the automatic diagnosis of cardiovascular anomalies. 2015 17th International Conference on E-health Networking, Application & Services (HealthCom). IEEE, 2015. YanJLuYXuYINTELLIGENT DIAGNOSIS OF CARDIOVASCULAR DISEASES UTILIZING ECG SIGNALSInternational Journal of Information Acquisition20100702819710.1142/S0219878910002087 SekarBDDongMFunction Formula Oriented Construction of Bayesian Inference Nets for Diagnosis of Cardiovascular DiseaseBiomed. Res. Int.201420141376378252471744163461 QianPSunSJiangYKuan-HaoSNiTWangSJrRFMCross-domain, soft-partition clustering with diversity measure and knowledge referencePattern Recogn.20165015517710.1016/j.patcog.2015.08.009 DrotárPMekyskaJRektorováIEvaluation of handwriting kinematics and pressure for differential diagnosis of Parkinson’s diseaseArtif. Intell. Med.201667394610.1016/j.artmed.2016.01.004 XuZXXuJYanJJAnalysis of the diagnostic consistency of Chinese medicine specialists in cardiovascular disease cases and syndrome identification based on the relevant feature for each label learning methodChinese Journal of Integrative Medicine201521321722210.1007/s11655-014-1822-6 Ríos, S.A., Tenorio, F.G., and Jimenezmolina, A., A benchmark on artificial intelligence techniques for automatic chronic respiratory diseases risk classification. In: Kes-inmed-15 Third International Conference on Innovation in Medicine & Healthcare. Cham: Springer, 2016. QianPXiCMinXJiangYKuan-HaoSWangSJrRFMSSC-EKE: semi-supervised classification with extensive knowledge exploitationInf. Sci.2018422517610.1016/j.ins.2017.08.093 DysterTimothyShethSameer A.McKhannGuy M.Ready or Not, Here We GoNeurosurgery2016786N11N1210.1227/01.neu.0000484053.82181.f6 Salah, R. B., and Chabchoub, S., Intelligent diagnosis method of cardiovascular anomalies using medical signal processing. World Congress on Information Technology & Computer Applications. IEEE, 2016. DuSSZhaoMMZhangYScreening for differentially expressed proteins relevant to the differential diagnosis of sarcoidosis and tuberculosisP1oS one2015109e013246610.1371/journal.pone.0132466 Miao, F., Cai, Y. P., and Zhang, Y. T., Risk prediction for heart failure incidence within 1-year using clinical and laboratory factors. Engineering in Medicine and Biology Society (EMBC), 2014 36th Annual International Conference of the IEEE. IEEE, 2014: 1790-1793. Yifeng, X. U., Lijun, L., Qingsong, H. et al., Research on TF-IDF weight improvement algorithm in intelligent guidance system. Computer Engineering & Applications, 2017. Filimon, D. M., and Albu, A., Skin diseases diagnosis using artificial neural networks. IEEE International Symposium on Applied Computational Intelligence & Informatics., IEEE, 2014. Tang, Z., Wang, S., Huo, J. et al., Bayesian Framework with Non-local and Low-rank Constraint for Image Reconstruction. J. Phys. Conf. Ser., 2017. Yu-GuangYDe-ChangLIHong-YuGApplication of the Artificial Intelligence Technology in Coronary Heart Disease Diagnosis2008ChangchunJournal of Changchun Normal University H Nagano (1346_CR3) 2018; 2 BD Sekar (1346_CR12) 2014; 2014 F Ahmad (1346_CR10) 2013; 37 1346_CR28 Seong Ho Park (1346_CR2) 2018; 286 HG Lee (1346_CR29) 2006 J Canadas (1346_CR6) 2017; 4 HG Lee (1346_CR17) 2006 1346_CR20 1346_CR4 1346_CR23 1346_CR22 Y Yu-Guang (1346_CR1) 2008 1346_CR25 1346_CR7 1346_CR9 J Yan (1346_CR11) 2010; 07 A Ghareh Baghi (1346_CR15) 2015 B Sun (1346_CR13) 2009 P Drotár (1346_CR5) 2016; 67 CA Bondy (1346_CR24) 2008; 3 OD Shankaracharya (1346_CR27) 2012; 14 P Qian (1346_CR33) 2018; 6 1346_CR18 BM Nes (1346_CR21) 2017; 130 P Qian (1346_CR32) 2016; 50 1346_CR19 TF Glass (1346_CR16) 2004; 32 P Qian (1346_CR34) 2018; 422 SS Du (1346_CR35) 2015; 10 ZX Xu (1346_CR26) 2015; 21 1346_CR31 1346_CR14 1346_CR36 1346_CR30 Timothy Dyster (1346_CR8) 2016; 78
References_xml	– reference: ParkSeong HoHanKyunghwaMethodologic Guide for Evaluating Clinical Performance and Effect of Artificial Intelligence Technology for Medical Diagnosis and PredictionRadiology2018286380080910.1148/radiol.2017171920 – reference: ShankaracharyaODMallickMJava-based diabetes type 2 prediction tool for better diagnosisDiabetes Technol. Ther.20121432512561:STN:280:DC%2BC383ms1Smtw%3D%3D10.1089/dia.2011.0202 – reference: SunBLiYZhangLThe Intelligent System of Cardiovascular Disease Diagnosis Based on Extension Data Mining. Cutting-Edge Research Topics on Multiple Criteria Decision Making2009Berlin HeidelbergSpringer – reference: Sun, B., Li, Y., and Zhang, L., The Intelligent System of Cardiovascular Disease Diagnosis Based on Extension Data Mining. Communications in Computer & Information Science 2008:133–140. – reference: BondyCACongenital Cardiovascular Disease in Turner SyndromeCongenit. Heart Dis.20083121510.1111/j.1747-0803.2007.00163.x – reference: Peinado, I., Arredondo, M.T., Villalba, E., et al., Patient interaction in homecare systems to treat cardiovascular diseases in the long term. In: International Conference of the IEEE Engineering in Medicine and Biology Society, Minneapolis, pp 308–311, 2009. – reference: DrotárPMekyskaJRektorováIEvaluation of handwriting kinematics and pressure for differential diagnosis of Parkinson’s diseaseArtif. Intell. Med.201667394610.1016/j.artmed.2016.01.004 – reference: Tang, Z., Wang, S., Huo, J. et al., Bayesian Framework with Non-local and Low-rank Constraint for Image Reconstruction. J. Phys. Conf. Ser., 2017. – reference: Yu-GuangYDe-ChangLIHong-YuGApplication of the Artificial Intelligence Technology in Coronary Heart Disease Diagnosis2008ChangchunJournal of Changchun Normal University – reference: NesBMGutvikCRLavieCJPersonalized Activity Intelligence (PAI) for Prevention of Cardiovascular Disease and Promotion of Physical ActivityAm. J. Med.2017130332833610.1016/j.amjmed.2016.09.031 – reference: Ghareh BaghiALindénMAn Internet-Based Tool for Pediatric Cardiac Disease Diagnosis using Intelligent Phonocardiography. International Internet of Things Summit2015New YorkSpringer International Publishing – reference: AhmadFIsaNAMHussainZIntelligent Medical Disease Diagnosis Using Improved Hybrid Genetic Algorithm - Multilayer Perceptron NetworkJ. Med. Syst.2013372993410.1007/s10916-013-9934-7 – reference: YanJLuYXuYINTELLIGENT DIAGNOSIS OF CARDIOVASCULAR DISEASES UTILIZING ECG SIGNALSInternational Journal of Information Acquisition20100702819710.1142/S0219878910002087 – reference: NaganoHBig data, information and communication technology, artificial intelligence, Internet of things: How important are they for gastroenterological surgery?Annals of Gastroenterological Surgery20182316616610.1002/ags3.12173 – reference: LeeHGNohKLeeBJCardiovascular Disease Diagnosis Method by Emerging Patterns. International Conference on Advanced Data Mining & Applications2006BerlinSpringer-Verlag – reference: Feshki, M. G., and Shijani, O. S., Improving the heart disease diagnosis by evolutionary algorithm of PSO and Feed Forward Neural Network. Artificial Intelligence & Robotics. IEEE, 2016. – reference: LeeHGNohKLeeBJCardiovascular Disease Diagnosis Method by Emerging Patterns. Advanced Data Mining and Applications2006Berlin HeidelbergSpringer – reference: Khosla A, Cao Y, Lin C C Y, et al. An integrated machine learning approach to stroke prediction. Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, 2010: 183–192. – reference: Filimon, D. M., and Albu, A., Skin diseases diagnosis using artificial neural networks. IEEE International Symposium on Applied Computational Intelligence & Informatics., IEEE, 2014. – reference: Valavanis I K, Mougiakakou S G, Grimaldi K A, et al. Analysis of Postprandial Lipemia as a Cardiovascular Disease Risk Factor using Genetic and Clinical Information: An Artificial Neural Network Perspective. Conference proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference, 2008:4609-4612, 2008. – reference: QianPSunSJiangYKuan-HaoSNiTWangSJrRFMCross-domain, soft-partition clustering with diversity measure and knowledge referencePattern Recogn.20165015517710.1016/j.patcog.2015.08.009 – reference: XuZXXuJYanJJAnalysis of the diagnostic consistency of Chinese medicine specialists in cardiovascular disease cases and syndrome identification based on the relevant feature for each label learning methodChinese Journal of Integrative Medicine201521321722210.1007/s11655-014-1822-6 – reference: Hudson, D. L., and Cohen, M. E., Use of intelligent agents in the diagnosis of cardiac disorders. Comput. Cardiol. IEEE, 2002. – reference: Babič, F., Olejár, J., Vantová, Z. et al., Predictive and Descriptive Analysis for Heart Disease Diagnosis. Comput. Sci. Inf. Syst.. IEEE, 2017. – reference: Ríos, S.A., Tenorio, F.G., and Jimenezmolina, A., A benchmark on artificial intelligence techniques for automatic chronic respiratory diseases risk classification. In: Kes-inmed-15 Third International Conference on Innovation in Medicine & Healthcare. Cham: Springer, 2016. – reference: Salah, R. B., and Chabchoub, S., Intelligent diagnosis method of cardiovascular anomalies using medical signal processing. World Congress on Information Technology & Computer Applications. IEEE, 2016. – reference: QianPXiCMinXJiangYKuan-HaoSWangSJrRFMSSC-EKE: semi-supervised classification with extensive knowledge exploitationInf. Sci.2018422517610.1016/j.ins.2017.08.093 – reference: DysterTimothyShethSameer A.McKhannGuy M.Ready or Not, Here We GoNeurosurgery2016786N11N1210.1227/01.neu.0000484053.82181.f6 – reference: CanadasJSánchez-MolinaJARodríguezFImproving automatic climate control with decision support techniques to minimize disease effects in greenhouse tomatoesInformation Processing in Agriculture201741506310.1016/j.inpa.2016.12.002 – reference: Miao, F., Cai, Y. P., and Zhang, Y. T., Risk prediction for heart failure incidence within 1-year using clinical and laboratory factors. Engineering in Medicine and Biology Society (EMBC), 2014 36th Annual International Conference of the IEEE. IEEE, 2014: 1790-1793. – reference: Alhadidi, T., and Salah, R. B., A new intelligent method for the automatic diagnosis of cardiovascular anomalies. 2015 17th International Conference on E-health Networking, Application & Services (HealthCom). IEEE, 2015. – reference: SekarBDDongMFunction Formula Oriented Construction of Bayesian Inference Nets for Diagnosis of Cardiovascular DiseaseBiomed. Res. Int.201420141376378252471744163461 – reference: GlassTFKnappJAmburnPUse of artificial intelligence to identify cardiovascular compromise in a model of hemorrhagic shockCrit. Care Med.200432245045610.1097/01.CCM.0000109444.02324.AD – reference: DuSSZhaoMMZhangYScreening for differentially expressed proteins relevant to the differential diagnosis of sarcoidosis and tuberculosisP1oS one2015109e013246610.1371/journal.pone.0132466 – reference: Yifeng, X. U., Lijun, L., Qingsong, H. et al., Research on TF-IDF weight improvement algorithm in intelligent guidance system. Computer Engineering & Applications, 2017. – reference: QianPZhouJJiangYLiangFZhaoKWangSSuK-HMuzicRFJrMulti-view maximum entropy clustering by jointly leveraging inter-view collaborations and intra-view-weighted attributesIEEE Access20186285942861010.1109/ACCESS.2018.2825352 – ident: 1346_CR9 – volume: 2014 start-page: 376378 issue: 1 year: 2014 ident: 1346_CR12 publication-title: Biomed. Res. Int. – volume: 286 start-page: 800 issue: 3 year: 2018 ident: 1346_CR2 publication-title: Radiology doi: 10.1148/radiol.2017171920 – ident: 1346_CR25 doi: 10.15439/2017F219 – volume: 14 start-page: 251 issue: 3 year: 2012 ident: 1346_CR27 publication-title: Diabetes Technol. Ther. doi: 10.1089/dia.2011.0202 – volume: 37 start-page: 9934 issue: 2 year: 2013 ident: 1346_CR10 publication-title: J. Med. Syst. doi: 10.1007/s10916-013-9934-7 – volume-title: Cardiovascular Disease Diagnosis Method by Emerging Patterns. Advanced Data Mining and Applications year: 2006 ident: 1346_CR17 – volume: 21 start-page: 217 issue: 3 year: 2015 ident: 1346_CR26 publication-title: Chinese Journal of Integrative Medicine doi: 10.1007/s11655-014-1822-6 – ident: 1346_CR30 doi: 10.1109/IEMBS.2009.5333597 – volume-title: The Intelligent System of Cardiovascular Disease Diagnosis Based on Extension Data Mining. Cutting-Edge Research Topics on Multiple Criteria Decision Making year: 2009 ident: 1346_CR13 – volume-title: An Internet-Based Tool for Pediatric Cardiac Disease Diagnosis using Intelligent Phonocardiography. International Internet of Things Summit year: 2015 ident: 1346_CR15 – ident: 1346_CR4 doi: 10.1007/978-3-319-23024-5_43 – volume: 6 start-page: 28594 year: 2018 ident: 1346_CR33 publication-title: IEEE Access doi: 10.1109/ACCESS.2018.2825352 – volume-title: Application of the Artificial Intelligence Technology in Coronary Heart Disease Diagnosis year: 2008 ident: 1346_CR1 – ident: 1346_CR18 doi: 10.1109/SACI.2014.6840059 – ident: 1346_CR19 – volume: 4 start-page: 50 issue: 1 year: 2017 ident: 1346_CR6 publication-title: Information Processing in Agriculture doi: 10.1016/j.inpa.2016.12.002 – volume: 50 start-page: 155 year: 2016 ident: 1346_CR32 publication-title: Pattern Recogn. doi: 10.1016/j.patcog.2015.08.009 – volume: 3 start-page: 2 issue: 1 year: 2008 ident: 1346_CR24 publication-title: Congenit. Heart Dis. doi: 10.1111/j.1747-0803.2007.00163.x – volume: 422 start-page: 51 year: 2018 ident: 1346_CR34 publication-title: Inf. Sci. doi: 10.1016/j.ins.2017.08.093 – ident: 1346_CR36 – ident: 1346_CR31 doi: 10.1145/1835804.1835830 – volume: 130 start-page: 328 issue: 3 year: 2017 ident: 1346_CR21 publication-title: Am. J. Med. doi: 10.1016/j.amjmed.2016.09.031 – ident: 1346_CR28 doi: 10.1007/978-3-642-02298-2_19 – volume: 10 start-page: e0132466 issue: 9 year: 2015 ident: 1346_CR35 publication-title: P1oS one doi: 10.1371/journal.pone.0132466 – ident: 1346_CR22 doi: 10.1088/1742-6596/787/1/012008 – volume: 2 start-page: 166 issue: 3 year: 2018 ident: 1346_CR3 publication-title: Annals of Gastroenterological Surgery doi: 10.1002/ags3.12173 – ident: 1346_CR20 doi: 10.1109/RIOS.2016.7529489 – volume: 07 start-page: 81 issue: 02 year: 2010 ident: 1346_CR11 publication-title: International Journal of Information Acquisition doi: 10.1142/S0219878910002087 – ident: 1346_CR23 – volume: 32 start-page: 450 issue: 2 year: 2004 ident: 1346_CR16 publication-title: Crit. Care Med. doi: 10.1097/01.CCM.0000109444.02324.AD – ident: 1346_CR14 – ident: 1346_CR7 – volume: 67 start-page: 39 year: 2016 ident: 1346_CR5 publication-title: Artif. Intell. Med. doi: 10.1016/j.artmed.2016.01.004 – volume: 78 start-page: N11 issue: 6 year: 2016 ident: 1346_CR8 publication-title: Neurosurgery doi: 10.1227/01.neu.0000484053.82181.f6 – volume-title: Cardiovascular Disease Diagnosis Method by Emerging Patterns. International Conference on Advanced Data Mining & Applications year: 2006 ident: 1346_CR29
SSID	ssj0009667
Score	2.3189719
Snippet	The use of artificial intelligence in medicine is currently an issue of great interest, especially with regard to the diagnostic or predictive analysis of...
SourceID	proquest pubmed crossref springer
SourceType	Aggregation Database Index Database Enrichment Source Publisher
StartPage	228
SubjectTerms	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
SummonAdditionalLinks	– databaseName: SpringerLink dbid: RSV link: http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnV3fa9UwFD7IJiKMqVNn55Qj-KQE2jRNmsf9ZIIOmVP3VtImgYuXXlnvxL9jf_FOmvbejTlBX_rS06SkJ8138uV8B-CtpRCZVhrLvKLYRAS6tZbes8wXqc91xm0Ucf2ojo_LszP9ecjj7sbT7iMl2f-pryW7EZSh0FezLBeSEXBcpdWuDPUaTr58WyrtShlzpEXJgvr4SGX-qYmbi9EthHmLHe0XncNH__W6j2F9wJi4E53iCdxz7QY8-DSw6BuwFvfqMKYgPYXLsbQD25lYZ3E_Hr-bdGhai4Ny6BRPe1_Fmce9G4dYyb4neTrcpavFWdt3HaUp8MM1zU9cbORTfI4El_Fk0v1gvcgyfo97NBiqs02fwdfDg9O9IzbUamBNrviceVNKy7UvG-6kNsp4R5EXr1VdSIJAeS1UoRyBMV7UpnCltsoQNqxTW0rjC5k_h5V21roXgDlXNtemNlpo4ZQywtSNFDLzPIgX2gTS8aNVzSBkHuppTKulBHMY-4rGvgpjX_1O4N3ikZ9RxeNvxtujJ1TDhO4qQlE6VMPhWQJvFrdpKgZ-xbRudhFshCS0RCFnApvRgxa95YGuFTpN4P3oLsvG73yVrX-yfgkPedrnpJHbbcPK_PzCvYL7za_5pDt_3c-SK3gfDEQ priority: 102 providerName: Springer Nature
Title	Computer-Aided Diagnosis and Clinical Trials of Cardiovascular Diseases Based on Artificial Intelligence Technologies for Risk-Early Warning Model
URI	https://link.springer.com/article/10.1007/s10916-019-1346-x https://www.ncbi.nlm.nih.gov/pubmed/31197490 https://www.proquest.com/docview/2239515521 https://www.proquest.com/docview/2246244495
Volume	43
WOSCitedRecordID	wos000471604200001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
hasFullText	1
inHoldings	1
isFullTextHit
isPrint
journalDatabaseRights	– providerCode: PRVAVX databaseName: SpringerLink customDbUrl: eissn: 1573-689X dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0009667 issn: 0148-5598 databaseCode: RSV dateStart: 19970101 isFulltext: true titleUrlDefault: https://link.springer.com/search?facet-content-type=%22Journal%22 providerName: Springer Nature
link	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV3db9MwED-xDSEkxMf4CozKSDyBLBLHseMntI1NIGhVlTIqXiIntqWKKh1Lh_Z38BdzjpOWaWIvvJwUxYkt3dn-3Z39O4BXBl1k3GkMdRJ9E-7TraVwjiYui12qEmYCietnORrls5kadwG3pjtW2a-J7UJtlpWPkb_FbUz5ciQseXf6k_qqUT672pXQ2IIdRDaJP9I1ZOMN6a4Q4bo0z6knIu-zmuHqHAIjdKQVTVIu6MXlfekK2LySKG33n-N7_zvy-3C3Q55kP5jKA7hh6124Nexy67twJ0TwSLiY9BB-9wUf6P7cWEPeh0N584bo2pCOT3RBpq0Fk6Ujh5eOtmL7NvXTkAOUhizrtutAWEE-_sUEStbhffTaCYJoMpk3P2hLvUy-hcgN8TXbFo_g6_HR9PAD7So40CqVbEWdzoVhyuUVs0JpqZ1Ff4yVsswEAqO05DKTFiEay0qd2VwZqRExlrHJhXaZSB_Ddr2s7VMgKZMmVbrUiitupdRcl5XgInHMUxqaCOJef0XV0Zv7KhuLYkPM7FVeoMoLr_LiIoLX609OA7fHdY33eu0W3TRvio1qI3i5fo0T1GdddG2X574NF4ih0BGN4EkwpnVvqU_ichVH8Ka3rs3P_zmUZ9cP5TncZnF7NQ3New-2V2fn9gXcrH6t5s3ZALbk5MTLmWxlPoCdg6PReIJPnyRFOYyng3YeeSm9HGffUU6-nPwBUA0fLw
linkProvider	ProQuest
linkToHtml	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V3NbtQwEB6VggAJ8VP-AgWMBBeQxa7j2PEBodJSddXtqkKL6C04sS1FrJLSbKG8Bg_CMzKOk12qit564JJLHNtyPntmPDPfALwwaCKjpDHUSbRNuHe35sI5OnTJwMVqyEwgcR3LySQ9OFD7K_C7z4XxYZX9mdge1KYu_B35GxRjypcjYcN3h9-orxrlvat9CY0Ai1378weabM3b0Rb-35eMbX-Ybu7QrqoALWLJ5tTpVBimXFowK5SW2lm0EVgu80SgsI5zLhNpUW1gSa4TmyojNWox-cCkQrtExNjvJbjMPbOYDxVk-0uSXyFCejZPqSc-772oIVUPFTE03BUdxlzQk9Ny8Ixye8Yx28q77Vv_20rdhpudZk02wla4Ayu2WoOre13swBrcCDeUJCRe3YVffUELulEaa8hWCDosG6IrQzq-1BmZtjuU1I5sngrdxfata6sh7_FpSF21QwdCDjL6i-mULNwXJbZGI4F8LJuvtKWWJp_DzRTxNelm9-DThazQfVit6so-BBIzaWKlc6244lZKzXVeCC6GjnnKRhPBoMdLVnT07b6KyCxbEk97iGUIscxDLDuJ4NXik8PAXXJe4_UeTVl3jDXZEkoRPF-8xgPIe5V0Zetj34YL1BHR0I7gQQDvYrTYO6m5GkTwukfzsvN_TuXR-VN5Btd2pnvjbDya7D6G62zQpuHh1lqH1fnRsX0CV4rv87I5etruTgJfLhrkfwDbWHKH
linkToPdf	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V3NbtQwEB6VgiokxE-BslDASHABWU2cxI4PCJUuK1YtqxUqouISnNiWoq6S0myhvAaPw9MxjpNdqoreeuCSSxzbcj57Zjwz3wA812gio6TR1Aq0TWLnbs25tTS0SWAjGTLtSVz3xGSSHhzI6Qr87nNhXFhlfya2B7WuC3dHvoViTLpyJCzcsl1YxHQ4enP0jboKUs7T2pfT8BDZNT9_oPnWvB4P8V-_YGz0bn_nPe0qDNAiEmxOrUq5ZtKmBTNcKqGsQXuB5SJPOAruKI9FIgyqECzJVWJSqYVCjSYPdMqVTXiE_V6BqwJtTBdOOE2-LAl_Ofep2nFKHQl671H1aXuolKERL2kYxZyenpWJ5xTdc07aVvaNbv3Pq3YbbnYaN9n2W-QOrJhqHdY-dDEF63DD31wSn5B1F371hS7odqmNJkMfjFg2RFWadDyqM7Lf7lxSW7JzJqQX27cur4a8xacmddUO7Yk6yPgvBlSycGuU2BqNB_KxbA5pSzlNPvsbK-Jq1c3uwadLWaH7sFrVlXkAJGJCR1LlSsYyNkKoWOUFj3lomaNy1AMIeuxkRUfr7qqLzLIlIbWDW4ZwyxzcstMBvFx8cuQ5TS5qvNkjK-uOtyZbwmoAzxav8WBy3iZVmfrEtYk56o5ogA9gwwN5MVrknNexDAbwqkf2svN_TuXhxVN5CmuI7WxvPNl9BNdZ0Gbn4S7bhNX58Yl5DNeK7_OyOX7SblQCXy8b438A6aB7cw
openUrl	ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Computer-Aided+Diagnosis+and+Clinical+Trials+of+Cardiovascular+Diseases+Based+on+Artificial+Intelligence+Technologies+for+Risk-Early+Warning+Model&rft.jtitle=Journal+of+medical+systems&rft.au=Li%2C+Bin&rft.au=Ding%2C+Shuai&rft.au=Song%2C+Guolei&rft.au=Li%2C+Jiajia&rft.date=2019-07-01&rft.pub=Springer+Nature+B.V&rft.issn=0148-5598&rft.eissn=1573-689X&rft.volume=43&rft.issue=7&rft.spage=1&rft.epage=10&rft_id=info:doi/10.1007%2Fs10916-019-1346-x&rft.externalDBID=HAS_PDF_LINK
thumbnail_l	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=0148-5598&client=summon
thumbnail_m	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=0148-5598&client=summon
thumbnail_s	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=0148-5598&client=summon