An AI model for Rapid and Accurate Identification of Chemical Agents in Mass Casualty Incidents
In this report we examine the effectiveness of WISER in identification of a chemical culprit during a chemical based Mass Casualty Incident (MCI). We also evaluate and compare Binary Decision Tree (BDT) and Artificial Neural Networks (ANN) using the same experimental conditions as WISER. The reverse...
Gespeichert in:
| Hauptverfasser: | , , , , , |
|---|---|
| Format: | Artikel |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext bestellen |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | |
|---|---|
| container_issue | |
| container_start_page | |
| container_title | |
| container_volume | |
| creator | Boltin, Nicholas Vu, Daniel Janos, Bethany Shofner, Alyssa Culley, Joan Valafar, Homayoun |
| description | In this report we examine the effectiveness of WISER in identification of a
chemical culprit during a chemical based Mass Casualty Incident (MCI). We also
evaluate and compare Binary Decision Tree (BDT) and Artificial Neural Networks
(ANN) using the same experimental conditions as WISER. The reverse engineered
set of Signs/Symptoms from the WISER application was used as the training set
and 31,100 simulated patient records were used as the testing set. Three sets
of simulated patient records were generated by 5%, 10% and 15% perturbation of
the Signs/Symptoms of each chemical record. While all three methods achieved a
100% training accuracy, WISER, BDT and ANN produced performances in the range
of: 1.8%-0%, 65%-26%, 67%-21% respectively. A preliminary investigation of
dimensional reduction using ANN illustrated a dimensional collapse from 79
variables to 40 with little loss of classification performance. |
| doi_str_mv | 10.48550/arxiv.2001.09735 |
| format | Article |
| fullrecord | <record><control><sourceid>arxiv_GOX</sourceid><recordid>TN_cdi_arxiv_primary_2001_09735</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2001_09735</sourcerecordid><originalsourceid>FETCH-LOGICAL-a675-2b27cb289441d24884bca3177a479d9b8767205aeb3a205f34ce72ec22f419de3</originalsourceid><addsrcrecordid>eNotj8tqwzAURLXpoqT9gK56f8CuXo7kpTB9GFICJXtzrUcrsOVgOaX5-zppV4dhmIFDyAOjpdRVRZ9w_onfJaeUlbRWorolnUlgWhgn5wcI0wwfeIwOMDkw1p5mXDy0zqclhmhxiVOCKUDz5cc1DmA-1ypDTPCOOUOD-YTDcoY22XhZ5TtyE3DI_v6fG3J4eT40b8Vu_9o2ZlfgVlUF77myPde1lMxxqbXsLQqmFEpVu7rXaqs4rdD3AlcGIa1X3FvOg2S182JDHv9ur4bdcY4jzufuYtpdTcUvRSlNZQ</addsrcrecordid><sourcetype>Open Access Repository</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype></control><display><type>article</type><title>An AI model for Rapid and Accurate Identification of Chemical Agents in Mass Casualty Incidents</title><source>arXiv.org</source><creator>Boltin, Nicholas ; Vu, Daniel ; Janos, Bethany ; Shofner, Alyssa ; Culley, Joan ; Valafar, Homayoun</creator><creatorcontrib>Boltin, Nicholas ; Vu, Daniel ; Janos, Bethany ; Shofner, Alyssa ; Culley, Joan ; Valafar, Homayoun</creatorcontrib><description>In this report we examine the effectiveness of WISER in identification of a
chemical culprit during a chemical based Mass Casualty Incident (MCI). We also
evaluate and compare Binary Decision Tree (BDT) and Artificial Neural Networks
(ANN) using the same experimental conditions as WISER. The reverse engineered
set of Signs/Symptoms from the WISER application was used as the training set
and 31,100 simulated patient records were used as the testing set. Three sets
of simulated patient records were generated by 5%, 10% and 15% perturbation of
the Signs/Symptoms of each chemical record. While all three methods achieved a
100% training accuracy, WISER, BDT and ANN produced performances in the range
of: 1.8%-0%, 65%-26%, 67%-21% respectively. A preliminary investigation of
dimensional reduction using ANN illustrated a dimensional collapse from 79
variables to 40 with little loss of classification performance.</description><identifier>DOI: 10.48550/arxiv.2001.09735</identifier><language>eng</language><subject>Computer Science - Learning ; Statistics - Applications ; Statistics - Machine Learning</subject><creationdate>2019-12</creationdate><rights>http://arxiv.org/licenses/nonexclusive-distrib/1.0</rights><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>228,230,776,881</link.rule.ids><linktorsrc>$$Uhttps://arxiv.org/abs/2001.09735$$EView_record_in_Cornell_University$$FView_record_in_$$GCornell_University$$Hfree_for_read</linktorsrc><backlink>$$Uhttps://doi.org/10.48550/arXiv.2001.09735$$DView paper in arXiv$$Hfree_for_read</backlink></links><search><creatorcontrib>Boltin, Nicholas</creatorcontrib><creatorcontrib>Vu, Daniel</creatorcontrib><creatorcontrib>Janos, Bethany</creatorcontrib><creatorcontrib>Shofner, Alyssa</creatorcontrib><creatorcontrib>Culley, Joan</creatorcontrib><creatorcontrib>Valafar, Homayoun</creatorcontrib><title>An AI model for Rapid and Accurate Identification of Chemical Agents in Mass Casualty Incidents</title><description>In this report we examine the effectiveness of WISER in identification of a
chemical culprit during a chemical based Mass Casualty Incident (MCI). We also
evaluate and compare Binary Decision Tree (BDT) and Artificial Neural Networks
(ANN) using the same experimental conditions as WISER. The reverse engineered
set of Signs/Symptoms from the WISER application was used as the training set
and 31,100 simulated patient records were used as the testing set. Three sets
of simulated patient records were generated by 5%, 10% and 15% perturbation of
the Signs/Symptoms of each chemical record. While all three methods achieved a
100% training accuracy, WISER, BDT and ANN produced performances in the range
of: 1.8%-0%, 65%-26%, 67%-21% respectively. A preliminary investigation of
dimensional reduction using ANN illustrated a dimensional collapse from 79
variables to 40 with little loss of classification performance.</description><subject>Computer Science - Learning</subject><subject>Statistics - Applications</subject><subject>Statistics - Machine Learning</subject><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2019</creationdate><recordtype>article</recordtype><sourceid>GOX</sourceid><recordid>eNotj8tqwzAURLXpoqT9gK56f8CuXo7kpTB9GFICJXtzrUcrsOVgOaX5-zppV4dhmIFDyAOjpdRVRZ9w_onfJaeUlbRWorolnUlgWhgn5wcI0wwfeIwOMDkw1p5mXDy0zqclhmhxiVOCKUDz5cc1DmA-1ypDTPCOOUOD-YTDcoY22XhZ5TtyE3DI_v6fG3J4eT40b8Vu_9o2ZlfgVlUF77myPde1lMxxqbXsLQqmFEpVu7rXaqs4rdD3AlcGIa1X3FvOg2S182JDHv9ur4bdcY4jzufuYtpdTcUvRSlNZQ</recordid><startdate>20191212</startdate><enddate>20191212</enddate><creator>Boltin, Nicholas</creator><creator>Vu, Daniel</creator><creator>Janos, Bethany</creator><creator>Shofner, Alyssa</creator><creator>Culley, Joan</creator><creator>Valafar, Homayoun</creator><scope>AKY</scope><scope>EPD</scope><scope>GOX</scope></search><sort><creationdate>20191212</creationdate><title>An AI model for Rapid and Accurate Identification of Chemical Agents in Mass Casualty Incidents</title><author>Boltin, Nicholas ; Vu, Daniel ; Janos, Bethany ; Shofner, Alyssa ; Culley, Joan ; Valafar, Homayoun</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-a675-2b27cb289441d24884bca3177a479d9b8767205aeb3a205f34ce72ec22f419de3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2019</creationdate><topic>Computer Science - Learning</topic><topic>Statistics - Applications</topic><topic>Statistics - Machine Learning</topic><toplevel>online_resources</toplevel><creatorcontrib>Boltin, Nicholas</creatorcontrib><creatorcontrib>Vu, Daniel</creatorcontrib><creatorcontrib>Janos, Bethany</creatorcontrib><creatorcontrib>Shofner, Alyssa</creatorcontrib><creatorcontrib>Culley, Joan</creatorcontrib><creatorcontrib>Valafar, Homayoun</creatorcontrib><collection>arXiv Computer Science</collection><collection>arXiv Statistics</collection><collection>arXiv.org</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Boltin, Nicholas</au><au>Vu, Daniel</au><au>Janos, Bethany</au><au>Shofner, Alyssa</au><au>Culley, Joan</au><au>Valafar, Homayoun</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>An AI model for Rapid and Accurate Identification of Chemical Agents in Mass Casualty Incidents</atitle><date>2019-12-12</date><risdate>2019</risdate><abstract>In this report we examine the effectiveness of WISER in identification of a
chemical culprit during a chemical based Mass Casualty Incident (MCI). We also
evaluate and compare Binary Decision Tree (BDT) and Artificial Neural Networks
(ANN) using the same experimental conditions as WISER. The reverse engineered
set of Signs/Symptoms from the WISER application was used as the training set
and 31,100 simulated patient records were used as the testing set. Three sets
of simulated patient records were generated by 5%, 10% and 15% perturbation of
the Signs/Symptoms of each chemical record. While all three methods achieved a
100% training accuracy, WISER, BDT and ANN produced performances in the range
of: 1.8%-0%, 65%-26%, 67%-21% respectively. A preliminary investigation of
dimensional reduction using ANN illustrated a dimensional collapse from 79
variables to 40 with little loss of classification performance.</abstract><doi>10.48550/arxiv.2001.09735</doi><oa>free_for_read</oa></addata></record> |
| fulltext | fulltext_linktorsrc |
| identifier | DOI: 10.48550/arxiv.2001.09735 |
| ispartof | |
| issn | |
| language | eng |
| recordid | cdi_arxiv_primary_2001_09735 |
| source | arXiv.org |
| subjects | Computer Science - Learning Statistics - Applications Statistics - Machine Learning |
| title | An AI model for Rapid and Accurate Identification of Chemical Agents in Mass Casualty Incidents |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-02-21T13%3A41%3A25IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-arxiv_GOX&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=An%20AI%20model%20for%20Rapid%20and%20Accurate%20Identification%20of%20Chemical%20Agents%20in%20Mass%20Casualty%20Incidents&rft.au=Boltin,%20Nicholas&rft.date=2019-12-12&rft_id=info:doi/10.48550/arxiv.2001.09735&rft_dat=%3Carxiv_GOX%3E2001_09735%3C/arxiv_GOX%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_id=info:pmid/&rfr_iscdi=true |