Use of Artificial Networks in Clinical Trials: A Pilot Study to Predict Responsiveness to Donepezil in Alzheimer's Disease
OBJECTIVES: To evaluate the accuracy of artificial neural networks compared with discriminant analysis in classifying positive and negative response to the cholinesterase inhibitor donepezil in a group of Alzheimer's disease (AD) patients. DESIGN: Convenience sample. SETTING: Patients with mild...
Gespeichert in:
| Veröffentlicht in: | Journal of the American Geriatrics Society (JAGS) 2002-11, Vol.50 (11), p.1857-1860 |
|---|---|
| Hauptverfasser: | , , , , , , , |
| Format: | Artikel |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | 1860 |
|---|---|
| container_issue | 11 |
| container_start_page | 1857 |
| container_title | Journal of the American Geriatrics Society (JAGS) |
| container_volume | 50 |
| creator | Mecocci, Patrizia Grossi, Enzo Buscema, Massimo Intraligi, Marco Savarè, Rita Rinaldi, Patrizia Cherubini, Antonio Senin, Umberto |
| description | OBJECTIVES: To evaluate the accuracy of artificial neural networks compared with discriminant analysis in classifying positive and negative response to the cholinesterase inhibitor donepezil in a group of Alzheimer's disease (AD) patients.
DESIGN: Convenience sample.
SETTING: Patients with mild to moderate AD consecutively admitted to a geriatric day hospital and treated with donepezil 5 mg/day.
PARTICIPANTS: Sixty‐one older patients of both sexes with AD.
MEASUREMENTS: Accuracy in detecting subjects sensitive (responders) or not (nonresponders) to 3‐month therapy with ANNs. The criterion standard for evaluation of efficacy was the scores of Alzheimer's Disease Assessment Scale—Cognitive portion and Clinician's Interview Based Impression of Change—plus scales.
RESULTS: ANNs were more effective in discriminating between responders and nonresponders than other advanced statistical methods, particularly linear discriminant analysis. The total accuracy in predicting the outcome was 92.59%.
CONCLUSIONS: ANNs appear to be a useful tool in detecting patient responsiveness to pharmacological treatment in AD. |
| doi_str_mv | 10.1046/j.1532-5415.2002.50516.x |
| format | Article |
| fullrecord | <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_miscellaneous_72653184</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>72653184</sourcerecordid><originalsourceid>FETCH-LOGICAL-c5586-f5b700f0df00c78fbe27c4b628a327bf6ca2e81592988b4ca62f259ba55a09063</originalsourceid><addsrcrecordid>eNqNkV1v0zAUhi0EYmXwF5CFBFwl2E78US4mVe3oQNMobNMuLcc9Fu7SpNjp1vbX46zVJnEDV7b8Pn7k4xchTElOSSk-LXLKC5bxkvKcEcJyTjgV-eYZGjwGz9GApChTgpZH6FWMC0IoI0q9REeUlZQMiRyg3XUE3Do8Cp133npT4wvo7ttwG7Fv8Lj2jbfp8CqkKH7GIzzzddvhy2493-KuxbMAc287_BPiqm2iv4MGYuyTSdvACna-7kWjevcL_BLCx4gnPoKJ8Bq9cMkJbw7rMbr-cno1PsvOv0-_jkfnmeVciczxShLiyNwRYqVyFTBpy0owZQomKyesYaAoH7KhUlVpjWCO8WFlODdpRlEcow977yq0v9cQO7300UJdmwbaddSSCV5QVf4T5FJILqVM4Lu_wEW7Dk0aQjNKClWkL0-Q2kM2tDEGcHoV_NKEraZE9y3qhe7L0n1Zum9RP7SoN-nq24N_XS1h_nTxUFsC3h8AE1M7LpjG-vjElYRRxnvuZM_d-xq2__0A_W16-bBNgmwv8LGDzaPAhFstZCG5vrmYaj6bnP24SaKy-AMWGsZY</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>210383614</pqid></control><display><type>article</type><title>Use of Artificial Networks in Clinical Trials: A Pilot Study to Predict Responsiveness to Donepezil in Alzheimer's Disease</title><source>MEDLINE</source><source>Wiley Online Library Journals Frontfile Complete</source><source>Applied Social Sciences Index & Abstracts (ASSIA)</source><creator>Mecocci, Patrizia ; Grossi, Enzo ; Buscema, Massimo ; Intraligi, Marco ; Savarè, Rita ; Rinaldi, Patrizia ; Cherubini, Antonio ; Senin, Umberto</creator><creatorcontrib>Mecocci, Patrizia ; Grossi, Enzo ; Buscema, Massimo ; Intraligi, Marco ; Savarè, Rita ; Rinaldi, Patrizia ; Cherubini, Antonio ; Senin, Umberto</creatorcontrib><description>OBJECTIVES: To evaluate the accuracy of artificial neural networks compared with discriminant analysis in classifying positive and negative response to the cholinesterase inhibitor donepezil in a group of Alzheimer's disease (AD) patients.
DESIGN: Convenience sample.
SETTING: Patients with mild to moderate AD consecutively admitted to a geriatric day hospital and treated with donepezil 5 mg/day.
PARTICIPANTS: Sixty‐one older patients of both sexes with AD.
MEASUREMENTS: Accuracy in detecting subjects sensitive (responders) or not (nonresponders) to 3‐month therapy with ANNs. The criterion standard for evaluation of efficacy was the scores of Alzheimer's Disease Assessment Scale—Cognitive portion and Clinician's Interview Based Impression of Change—plus scales.
RESULTS: ANNs were more effective in discriminating between responders and nonresponders than other advanced statistical methods, particularly linear discriminant analysis. The total accuracy in predicting the outcome was 92.59%.
CONCLUSIONS: ANNs appear to be a useful tool in detecting patient responsiveness to pharmacological treatment in AD.</description><identifier>ISSN: 0002-8614</identifier><identifier>EISSN: 1532-5415</identifier><identifier>DOI: 10.1046/j.1532-5415.2002.50516.x</identifier><identifier>PMID: 12410907</identifier><identifier>CODEN: JAGSAF</identifier><language>eng</language><publisher>Boston, MA, USA: Blackwell Science Inc</publisher><subject>Accuracy ; Aged ; Aged, 80 and over ; Alzheimer Disease - drug therapy ; Alzheimer's disease ; artificial neural network ; Biological and medical sciences ; Cholinergic system ; Cholinesterase Inhibitors - administration & dosage ; Cholinesterase Inhibitors - therapeutic use ; Clinical trials ; Clinical Trials as Topic ; Discriminant Analysis ; Donepezil ; Dose-Response Relationship, Drug ; Drug therapy ; Female ; Humans ; Indans - administration & dosage ; Indans - therapeutic use ; Male ; Medical sciences ; Middle Aged ; Neural network approach ; Neural Networks (Computer) ; Neuropharmacology ; Neurotransmitters. Neurotransmission. Receptors ; Patients ; Pharmacology. Drug treatments ; Pilot Projects ; Pilot studies ; Piperidines - administration & dosage ; Piperidines - therapeutic use ; prediction ; Predictions ; Predictive Value of Tests ; Predictors ; Reproducibility of Results ; responder ; Responses ; Severity of Illness Index ; treatment ; USA</subject><ispartof>Journal of the American Geriatrics Society (JAGS), 2002-11, Vol.50 (11), p.1857-1860</ispartof><rights>2003 INIST-CNRS</rights><rights>Copyright Lippincott Williams & Wilkins Nov 2002</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c5586-f5b700f0df00c78fbe27c4b628a327bf6ca2e81592988b4ca62f259ba55a09063</citedby><cites>FETCH-LOGICAL-c5586-f5b700f0df00c78fbe27c4b628a327bf6ca2e81592988b4ca62f259ba55a09063</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://onlinelibrary.wiley.com/doi/pdf/10.1046%2Fj.1532-5415.2002.50516.x$$EPDF$$P50$$Gwiley$$H</linktopdf><linktohtml>$$Uhttps://onlinelibrary.wiley.com/doi/full/10.1046%2Fj.1532-5415.2002.50516.x$$EHTML$$P50$$Gwiley$$H</linktohtml><link.rule.ids>314,777,781,1412,27905,27906,30981,45555,45556</link.rule.ids><backlink>$$Uhttp://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=14021257$$DView record in Pascal Francis$$Hfree_for_read</backlink><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/12410907$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Mecocci, Patrizia</creatorcontrib><creatorcontrib>Grossi, Enzo</creatorcontrib><creatorcontrib>Buscema, Massimo</creatorcontrib><creatorcontrib>Intraligi, Marco</creatorcontrib><creatorcontrib>Savarè, Rita</creatorcontrib><creatorcontrib>Rinaldi, Patrizia</creatorcontrib><creatorcontrib>Cherubini, Antonio</creatorcontrib><creatorcontrib>Senin, Umberto</creatorcontrib><title>Use of Artificial Networks in Clinical Trials: A Pilot Study to Predict Responsiveness to Donepezil in Alzheimer's Disease</title><title>Journal of the American Geriatrics Society (JAGS)</title><addtitle>Journal of the American Geriatrics Society</addtitle><description>OBJECTIVES: To evaluate the accuracy of artificial neural networks compared with discriminant analysis in classifying positive and negative response to the cholinesterase inhibitor donepezil in a group of Alzheimer's disease (AD) patients.
DESIGN: Convenience sample.
SETTING: Patients with mild to moderate AD consecutively admitted to a geriatric day hospital and treated with donepezil 5 mg/day.
PARTICIPANTS: Sixty‐one older patients of both sexes with AD.
MEASUREMENTS: Accuracy in detecting subjects sensitive (responders) or not (nonresponders) to 3‐month therapy with ANNs. The criterion standard for evaluation of efficacy was the scores of Alzheimer's Disease Assessment Scale—Cognitive portion and Clinician's Interview Based Impression of Change—plus scales.
RESULTS: ANNs were more effective in discriminating between responders and nonresponders than other advanced statistical methods, particularly linear discriminant analysis. The total accuracy in predicting the outcome was 92.59%.
CONCLUSIONS: ANNs appear to be a useful tool in detecting patient responsiveness to pharmacological treatment in AD.</description><subject>Accuracy</subject><subject>Aged</subject><subject>Aged, 80 and over</subject><subject>Alzheimer Disease - drug therapy</subject><subject>Alzheimer's disease</subject><subject>artificial neural network</subject><subject>Biological and medical sciences</subject><subject>Cholinergic system</subject><subject>Cholinesterase Inhibitors - administration & dosage</subject><subject>Cholinesterase Inhibitors - therapeutic use</subject><subject>Clinical trials</subject><subject>Clinical Trials as Topic</subject><subject>Discriminant Analysis</subject><subject>Donepezil</subject><subject>Dose-Response Relationship, Drug</subject><subject>Drug therapy</subject><subject>Female</subject><subject>Humans</subject><subject>Indans - administration & dosage</subject><subject>Indans - therapeutic use</subject><subject>Male</subject><subject>Medical sciences</subject><subject>Middle Aged</subject><subject>Neural network approach</subject><subject>Neural Networks (Computer)</subject><subject>Neuropharmacology</subject><subject>Neurotransmitters. Neurotransmission. Receptors</subject><subject>Patients</subject><subject>Pharmacology. Drug treatments</subject><subject>Pilot Projects</subject><subject>Pilot studies</subject><subject>Piperidines - administration & dosage</subject><subject>Piperidines - therapeutic use</subject><subject>prediction</subject><subject>Predictions</subject><subject>Predictive Value of Tests</subject><subject>Predictors</subject><subject>Reproducibility of Results</subject><subject>responder</subject><subject>Responses</subject><subject>Severity of Illness Index</subject><subject>treatment</subject><subject>USA</subject><issn>0002-8614</issn><issn>1532-5415</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2002</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><sourceid>7QJ</sourceid><recordid>eNqNkV1v0zAUhi0EYmXwF5CFBFwl2E78US4mVe3oQNMobNMuLcc9Fu7SpNjp1vbX46zVJnEDV7b8Pn7k4xchTElOSSk-LXLKC5bxkvKcEcJyTjgV-eYZGjwGz9GApChTgpZH6FWMC0IoI0q9REeUlZQMiRyg3XUE3Do8Cp133npT4wvo7ttwG7Fv8Lj2jbfp8CqkKH7GIzzzddvhy2493-KuxbMAc287_BPiqm2iv4MGYuyTSdvACna-7kWjevcL_BLCx4gnPoKJ8Bq9cMkJbw7rMbr-cno1PsvOv0-_jkfnmeVciczxShLiyNwRYqVyFTBpy0owZQomKyesYaAoH7KhUlVpjWCO8WFlODdpRlEcow977yq0v9cQO7300UJdmwbaddSSCV5QVf4T5FJILqVM4Lu_wEW7Dk0aQjNKClWkL0-Q2kM2tDEGcHoV_NKEraZE9y3qhe7L0n1Zum9RP7SoN-nq24N_XS1h_nTxUFsC3h8AE1M7LpjG-vjElYRRxnvuZM_d-xq2__0A_W16-bBNgmwv8LGDzaPAhFstZCG5vrmYaj6bnP24SaKy-AMWGsZY</recordid><startdate>200211</startdate><enddate>200211</enddate><creator>Mecocci, Patrizia</creator><creator>Grossi, Enzo</creator><creator>Buscema, Massimo</creator><creator>Intraligi, Marco</creator><creator>Savarè, Rita</creator><creator>Rinaldi, Patrizia</creator><creator>Cherubini, Antonio</creator><creator>Senin, Umberto</creator><general>Blackwell Science Inc</general><general>Blackwell</general><general>Wiley Subscription Services, Inc</general><scope>BSCLL</scope><scope>IQODW</scope><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7QP</scope><scope>7TK</scope><scope>K9.</scope><scope>NAPCQ</scope><scope>7QJ</scope><scope>7X8</scope></search><sort><creationdate>200211</creationdate><title>Use of Artificial Networks in Clinical Trials: A Pilot Study to Predict Responsiveness to Donepezil in Alzheimer's Disease</title><author>Mecocci, Patrizia ; Grossi, Enzo ; Buscema, Massimo ; Intraligi, Marco ; Savarè, Rita ; Rinaldi, Patrizia ; Cherubini, Antonio ; Senin, Umberto</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c5586-f5b700f0df00c78fbe27c4b628a327bf6ca2e81592988b4ca62f259ba55a09063</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2002</creationdate><topic>Accuracy</topic><topic>Aged</topic><topic>Aged, 80 and over</topic><topic>Alzheimer Disease - drug therapy</topic><topic>Alzheimer's disease</topic><topic>artificial neural network</topic><topic>Biological and medical sciences</topic><topic>Cholinergic system</topic><topic>Cholinesterase Inhibitors - administration & dosage</topic><topic>Cholinesterase Inhibitors - therapeutic use</topic><topic>Clinical trials</topic><topic>Clinical Trials as Topic</topic><topic>Discriminant Analysis</topic><topic>Donepezil</topic><topic>Dose-Response Relationship, Drug</topic><topic>Drug therapy</topic><topic>Female</topic><topic>Humans</topic><topic>Indans - administration & dosage</topic><topic>Indans - therapeutic use</topic><topic>Male</topic><topic>Medical sciences</topic><topic>Middle Aged</topic><topic>Neural network approach</topic><topic>Neural Networks (Computer)</topic><topic>Neuropharmacology</topic><topic>Neurotransmitters. Neurotransmission. Receptors</topic><topic>Patients</topic><topic>Pharmacology. Drug treatments</topic><topic>Pilot Projects</topic><topic>Pilot studies</topic><topic>Piperidines - administration & dosage</topic><topic>Piperidines - therapeutic use</topic><topic>prediction</topic><topic>Predictions</topic><topic>Predictive Value of Tests</topic><topic>Predictors</topic><topic>Reproducibility of Results</topic><topic>responder</topic><topic>Responses</topic><topic>Severity of Illness Index</topic><topic>treatment</topic><topic>USA</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Mecocci, Patrizia</creatorcontrib><creatorcontrib>Grossi, Enzo</creatorcontrib><creatorcontrib>Buscema, Massimo</creatorcontrib><creatorcontrib>Intraligi, Marco</creatorcontrib><creatorcontrib>Savarè, Rita</creatorcontrib><creatorcontrib>Rinaldi, Patrizia</creatorcontrib><creatorcontrib>Cherubini, Antonio</creatorcontrib><creatorcontrib>Senin, Umberto</creatorcontrib><collection>Istex</collection><collection>Pascal-Francis</collection><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Calcium & Calcified Tissue Abstracts</collection><collection>Neurosciences Abstracts</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Nursing & Allied Health Premium</collection><collection>Applied Social Sciences Index & Abstracts (ASSIA)</collection><collection>MEDLINE - Academic</collection><jtitle>Journal of the American Geriatrics Society (JAGS)</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Mecocci, Patrizia</au><au>Grossi, Enzo</au><au>Buscema, Massimo</au><au>Intraligi, Marco</au><au>Savarè, Rita</au><au>Rinaldi, Patrizia</au><au>Cherubini, Antonio</au><au>Senin, Umberto</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Use of Artificial Networks in Clinical Trials: A Pilot Study to Predict Responsiveness to Donepezil in Alzheimer's Disease</atitle><jtitle>Journal of the American Geriatrics Society (JAGS)</jtitle><addtitle>Journal of the American Geriatrics Society</addtitle><date>2002-11</date><risdate>2002</risdate><volume>50</volume><issue>11</issue><spage>1857</spage><epage>1860</epage><pages>1857-1860</pages><issn>0002-8614</issn><eissn>1532-5415</eissn><coden>JAGSAF</coden><abstract>OBJECTIVES: To evaluate the accuracy of artificial neural networks compared with discriminant analysis in classifying positive and negative response to the cholinesterase inhibitor donepezil in a group of Alzheimer's disease (AD) patients.
DESIGN: Convenience sample.
SETTING: Patients with mild to moderate AD consecutively admitted to a geriatric day hospital and treated with donepezil 5 mg/day.
PARTICIPANTS: Sixty‐one older patients of both sexes with AD.
MEASUREMENTS: Accuracy in detecting subjects sensitive (responders) or not (nonresponders) to 3‐month therapy with ANNs. The criterion standard for evaluation of efficacy was the scores of Alzheimer's Disease Assessment Scale—Cognitive portion and Clinician's Interview Based Impression of Change—plus scales.
RESULTS: ANNs were more effective in discriminating between responders and nonresponders than other advanced statistical methods, particularly linear discriminant analysis. The total accuracy in predicting the outcome was 92.59%.
CONCLUSIONS: ANNs appear to be a useful tool in detecting patient responsiveness to pharmacological treatment in AD.</abstract><cop>Boston, MA, USA</cop><pub>Blackwell Science Inc</pub><pmid>12410907</pmid><doi>10.1046/j.1532-5415.2002.50516.x</doi><tpages>4</tpages></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 0002-8614 |
| ispartof | Journal of the American Geriatrics Society (JAGS), 2002-11, Vol.50 (11), p.1857-1860 |
| issn | 0002-8614 1532-5415 |
| language | eng |
| recordid | cdi_proquest_miscellaneous_72653184 |
| source | MEDLINE; Wiley Online Library Journals Frontfile Complete; Applied Social Sciences Index & Abstracts (ASSIA) |
| subjects | Accuracy Aged Aged, 80 and over Alzheimer Disease - drug therapy Alzheimer's disease artificial neural network Biological and medical sciences Cholinergic system Cholinesterase Inhibitors - administration & dosage Cholinesterase Inhibitors - therapeutic use Clinical trials Clinical Trials as Topic Discriminant Analysis Donepezil Dose-Response Relationship, Drug Drug therapy Female Humans Indans - administration & dosage Indans - therapeutic use Male Medical sciences Middle Aged Neural network approach Neural Networks (Computer) Neuropharmacology Neurotransmitters. Neurotransmission. Receptors Patients Pharmacology. Drug treatments Pilot Projects Pilot studies Piperidines - administration & dosage Piperidines - therapeutic use prediction Predictions Predictive Value of Tests Predictors Reproducibility of Results responder Responses Severity of Illness Index treatment USA |
| title | Use of Artificial Networks in Clinical Trials: A Pilot Study to Predict Responsiveness to Donepezil in Alzheimer's Disease |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-19T10%3A07%3A18IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Use%20of%20Artificial%20Networks%20in%20Clinical%20Trials:%20A%20Pilot%20Study%20to%20Predict%20Responsiveness%20to%20Donepezil%20in%20Alzheimer's%20Disease&rft.jtitle=Journal%20of%20the%20American%20Geriatrics%20Society%20(JAGS)&rft.au=Mecocci,%20Patrizia&rft.date=2002-11&rft.volume=50&rft.issue=11&rft.spage=1857&rft.epage=1860&rft.pages=1857-1860&rft.issn=0002-8614&rft.eissn=1532-5415&rft.coden=JAGSAF&rft_id=info:doi/10.1046/j.1532-5415.2002.50516.x&rft_dat=%3Cproquest_cross%3E72653184%3C/proquest_cross%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=210383614&rft_id=info:pmid/12410907&rfr_iscdi=true |