Slideshows by User: kasshout http://www.slideshare.net/ http://www.slideshare.net/images/logo.gif Slideshows by User: kasshout http://www.slideshare.net/ Thu, 18 Dec 2008 03:20:31 GMT SlideShare feed for Slideshows by User: kasshout Epi Info™ ─ Mesh4x Synchronization Prototype http://www.slideshare.net/kasshout/epi-info-mesh4x-prototype-presentation

from: kasshout 3 weeks ago

An example scenario where this technology would be useful is in an outbreak investigation. Many epidemiologists are familiar with the food borne outbreak in Oswego, New York, U.S.A. on April 18th, 1940. In this outbreak, 75 of the 80 people known to have been present at the pot-luck church supper were interviewed. A survey was created and interviews were conducted with participants to determine the source of the contamination. While the Oswego study focused on a single region, the significant value of data synchronization can be seen by expanding this scenario to where interviews and data entry are conducted in different localities. Therefore, imagine that the Oswego church supper was attended by residents of the Oswego county and four other neighboring counties: Jefferson, Lewis, Oneida, and Wayne. Imagine two epidemiologists are investigating this outbreak; an Epidemic Intelligence Service (or EIS) officer investigating the outbreak in Oneida county and another officer at the state health department investigating the other counties. After data synchronization, both investigators will have a clear picture of the spread of the illness over space and time and the actual cause of the outbreak.

Tags: technology david nitschke health souce

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from: kasshout 3 weeks ago

An example scenario where this technology would be useful is in an outbreak investigation. Many epidemiologists are familiar with the food borne outbreak in Oswego, New York, U.S.A. on April 18th, 1940. In this outbreak, 75 of the 80 people known to have been present at the pot-luck church supper were interviewed. A survey was created and interviews were conducted with participants to determine the source of the contamination. While the Oswego study focused on a single region, the significant value of data synchronization can be seen by expanding this scenario to where interviews and data entry are conducted in different localities. Therefore, imagine that the Oswego church supper was attended by residents of the Oswego county and four other neighboring counties: Jefferson, Lewis, Oneida, and Wayne. Imagine two epidemiologists are investigating this outbreak; an Epidemic Intelligence Service (or EIS) officer investigating the outbreak in Oneida county and another officer at the state health department investigating the other counties. After data synchronization, both investigators will have a clear picture of the spread of the illness over space and time and the actual cause of the outbreak.

Tags: technology david nitschke health souce

]]> Thu, 18 Dec 2008 03:20:31 GMT http://www.slideshare.net/kasshout/epi-info-mesh4x-prototype-presentation kasshout@slideshare.net(kasshout) Epi Info™ ─ Mesh4x Synchronization Prototype kasshout An example scenario where this technology would be useful is in an outbreak investigation. Many epidemiologists are familiar with the food borne outbreak in Oswego, New York, U.S.A. on April 18th, 1940. In this outbreak, 75 of the 80 people known to have been present at the pot-luck church supper were interviewed. A survey was created and interviews were conducted with participants to determine the source of the contamination. While the Oswego study focused on a single region, the significant value of data synchronization can be seen by expanding this scenario to where interviews and data entry are conducted in different localities. Therefore, imagine that the Oswego church supper was attended by residents of the Oswego county and four other neighboring counties: Jefferson, Lewis, Oneida, and Wayne. Imagine two epidemiologists are investigating this outbreak; an Epidemic Intelligence Service (or EIS) officer investigating the outbreak in Oneida county and another officer at the state health department investigating the other counties. After data synchronization, both investigators will have a clear picture of the spread of the illness over space and time and the actual cause of the outbreak. <div class='snap_preview'><img src="http://cdn.slideshare.net/epiinfomesh4xprototype-1229572607164845-1-thumbnail-2?1229572780" alt ="" style="border:1px solid #C3E6D8;float:right;" /> <p>from: <a href="http://www.slideshare.net/kasshout">kasshout</a> 3 weeks ago</p><p>An example scenario where this technology would be useful is in an outbreak investigation. Many epidemiologists are familiar with the food borne outbreak in Oswego, New York, U.S.A. on April 18th, 1940. In this outbreak, 75 of the 80 people known to have been present at the pot-luck church supper were interviewed. A survey was created and interviews were conducted with participants to determine the source of the contamination. While the Oswego study focused on a single region, the significant value of data synchronization can be seen by expanding this scenario to where interviews and data entry are conducted in different localities. Therefore, imagine that the Oswego church supper was attended by residents of the Oswego county and four other neighboring counties: Jefferson, Lewis, Oneida, and Wayne. Imagine two epidemiologists are investigating this outbreak; an Epidemic Intelligence Service (or EIS) officer investigating the outbreak in Oneida county and another officer at the state health department investigating the other counties. After data synchronization, both investigators will have a clear picture of the spread of the illness over space and time and the actual cause of the outbreak.</p><p>Tags: <a style="text-decoration:underline;" href="http://slideshare.net/tag/technology">technology</a> <a style="text-decoration:underline;" href="http://slideshare.net/tag/david">david</a> <a style="text-decoration:underline;" href="http://slideshare.net/tag/nitschke">nitschke</a> <a style="text-decoration:underline;" href="http://slideshare.net/tag/health">health</a> <a style="text-decoration:underline;" href="http://slideshare.net/tag/souce">souce</a> </p></div> technology,david,nitschke,health,souce, Epi Info™ ─ Mesh4x Synchronization Prototype
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]]> 265 0 http://cdn.slideshare.net/epiinfomesh4xprototype-1229572607164845-1-thumbnail-2?1229572780 Integrated Global Early Warning and Response System (Taha Kass-Hout and Nicolás di Tada) http://www.slideshare.net/kasshout/edd-integrated-global-early-warning-system-taha-kasshout-and-nicols-di-tada-presentation

from: kasshout 1 month ago

InSTEDD at the 7th International Society for Disease Surveillance (ISDS) conference in Raleigh, North Carolina, USA, Dec 2-5, 2008

Tags: health public global international epidemiology

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from: kasshout 1 month ago

InSTEDD at the 7th International Society for Disease Surveillance (ISDS) conference in Raleigh, North Carolina, USA, Dec 2-5, 2008

Tags: health public global international epidemiology

]]> Mon, 08 Dec 2008 03:49:26 GMT http://www.slideshare.net/kasshout/edd-integrated-global-early-warning-system-taha-kasshout-and-nicols-di-tada-presentation kasshout@slideshare.net(kasshout) Integrated Global Early Warning and Response System (Taha Kass-Hout and Nicolás di Tada) kasshout InSTEDD at the 7th International Society for Disease Surveillance (ISDS) conference in Raleigh, North Carolina, USA, Dec 2-5, 2008 <div class='snap_preview'><img src="http://cdn.slideshare.net/insteddisds2008syndromicseventhdraft-1228708186088416-9-thumbnail-2?1230011801" alt ="" style="border:1px solid #C3E6D8;float:right;" /> <p>from: <a href="http://www.slideshare.net/kasshout">kasshout</a> 1 month ago</p><p>InSTEDD at the 7th International Society for Disease Surveillance (ISDS) conference in Raleigh, North Carolina, USA, Dec 2-5, 2008</p><p>Tags: <a style="text-decoration:underline;" href="http://slideshare.net/tag/health">health</a> <a style="text-decoration:underline;" href="http://slideshare.net/tag/public">public</a> <a style="text-decoration:underline;" href="http://slideshare.net/tag/global">global</a> <a style="text-decoration:underline;" href="http://slideshare.net/tag/international">international</a> <a style="text-decoration:underline;" href="http://slideshare.net/tag/epidemiology">epidemiology</a> </p></div> health,public,global,international,epidemiology, Integrated Global Early Warning and Response System (Taha Kass-Hout and Nicolás di Tada)
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]]> 227 0 http://cdn.slideshare.net/insteddisds2008syndromicseventhdraft-1228708186088416-9-thumbnail-2?1230011801 InSTEDD Tools for Outbreak Epidemiology http://www.slideshare.net/kasshout/instedd-tools-for-outbreak-epidemiology-presentation

from: kasshout 1 month ago

Tags: instedd us cdc instedd us

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from: kasshout 1 month ago

Tags: instedd us cdc instedd us

]]> Tue, 25 Nov 2008 00:31:23 GMT http://www.slideshare.net/kasshout/instedd-tools-for-outbreak-epidemiology-presentation kasshout@slideshare.net(kasshout) InSTEDD Tools for Outbreak Epidemiology kasshout <div class='snap_preview'><img src="http://cdn.slideshare.net/instedd-at-cdc-112408-1227573193926140-9-thumbnail-2?1227573246" alt ="" style="border:1px solid #C3E6D8;float:right;" /> <p>from: <a href="http://www.slideshare.net/kasshout">kasshout</a> 1 month ago</p><p></p><p>Tags: <a style="text-decoration:underline;" href="http://slideshare.net/tag/instedd">instedd</a> <a style="text-decoration:underline;" href="http://slideshare.net/tag/us">us</a> <a style="text-decoration:underline;" href="http://slideshare.net/tag/cdc">cdc</a> <a style="text-decoration:underline;" href="http://slideshare.net/tag/instedd">instedd</a> <a style="text-decoration:underline;" href="http://slideshare.net/tag/us">us</a> </p></div> instedd,us,cdc,instedd,us, InSTEDD Tools for Outbreak Epidemiology
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]]> 274 0 http://cdn.slideshare.net/instedd-at-cdc-112408-1227573193926140-9-thumbnail-2?1227573246 Using Open Source Software For Public Health Kass-Hout Di Tada http://www.slideshare.net/kasshout/using-open-source-software-for-public-health-kasshout-di-tada-presentation

from: kasshout 2 months ago

Tags: health biosurveillance public open source

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from: kasshout 2 months ago

Tags: health biosurveillance public open source

]]> Sat, 11 Oct 2008 15:49:35 GMT http://www.slideshare.net/kasshout/using-open-source-software-for-public-health-kasshout-di-tada-presentation kasshout@slideshare.net(kasshout) Using Open Source Software For Public Health Kass-Hout Di Tada kasshout <div class='snap_preview'><img src="http://cdn.slideshare.net/using-opensource-software-for-public-healthkasshoutdi-tada-1223740221911178-9-thumbnail-2?1223740175" alt ="" style="border:1px solid #C3E6D8;float:right;" /> <p>from: <a href="http://www.slideshare.net/kasshout">kasshout</a> 2 months ago</p><p></p><p>Tags: <a style="text-decoration:underline;" href="http://slideshare.net/tag/health">health</a> <a style="text-decoration:underline;" href="http://slideshare.net/tag/biosurveillance">biosurveillance</a> <a style="text-decoration:underline;" href="http://slideshare.net/tag/public">public</a> <a style="text-decoration:underline;" href="http://slideshare.net/tag/open">open</a> <a style="text-decoration:underline;" href="http://slideshare.net/tag/source">source</a> </p></div> health,biosurveillance,public,open,source, Using Open Source Software For Public Health Kass-Hout Di Tada
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]]> 605 0 http://cdn.slideshare.net/using-opensource-software-for-public-healthkasshoutdi-tada-1223740221911178-9-thumbnail-2?1223740175 Biosurveillance: Machine Learning And Disease Surveillance by Kass-Hout Di Tada http://www.slideshare.net/kasshout/machine-learning-and-disease-surveillancebiosurveilance-kass-hout-di-tada-presentation

from: kasshout 2 months ago

The majority of the designs, analyses and evaluations of early detection (or biosurveillance) systems have been geared towards specific data sources and detection algorithms. Much less effort has been focused on how these systems will "interact" with humans. For example, consider multiple domain experts working at different levels across different organizations in an environment where numerous biosurveillance algorithms may provide contradictory interpretations of ongoing events. We present a framework that consists of a collection of autonomous, machine learning-enabled analytic processes, services and tools that; for the first time, will seamlessly integrate surveillance and response systems with human experts.

Tags: biosurveillance machine learning early detection

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from: kasshout 2 months ago

The majority of the designs, analyses and evaluations of early detection (or biosurveillance) systems have been geared towards specific data sources and detection algorithms. Much less effort has been focused on how these systems will "interact" with humans. For example, consider multiple domain experts working at different levels across different organizations in an environment where numerous biosurveillance algorithms may provide contradictory interpretations of ongoing events. We present a framework that consists of a collection of autonomous, machine learning-enabled analytic processes, services and tools that; for the first time, will seamlessly integrate surveillance and response systems with human experts.

Tags: biosurveillance machine learning early detection

]]> Sat, 11 Oct 2008 15:37:06 GMT http://www.slideshare.net/kasshout/machine-learning-and-disease-surveillancebiosurveilance-kass-hout-di-tada-presentation kasshout@slideshare.net(kasshout) Biosurveillance: Machine Learning And Disease Surveillance by Kass-Hout Di Tada kasshout The majority of the designs, analyses and evaluations of early detection (or biosurveillance) systems have been geared towards specific data sources and detection algorithms. Much less effort has been focused on how these systems will "interact" with humans. For example, consider multiple domain experts working at different levels across different organizations in an environment where numerous biosurveillance algorithms may provide contradictory interpretations of ongoing events. We present a framework that consists of a collection of autonomous, machine learning-enabled analytic processes, services and tools that; for the first time, will seamlessly integrate surveillance and response systems with human experts. <div class='snap_preview'><img src="http://cdn.slideshare.net/machine-learning-and-disease-surveillancekasshoutdi-tada-1223739217005826-9-thumbnail-2?1223740305" alt ="" style="border:1px solid #C3E6D8;float:right;" /> <p>from: <a href="http://www.slideshare.net/kasshout">kasshout</a> 2 months ago</p><p>The majority of the designs, analyses and evaluations of early detection (or biosurveillance) systems have been geared towards specific data sources and detection algorithms. Much less effort has been focused on how these systems will "interact" with humans. For example, consider multiple domain experts working at different levels across different organizations in an environment where numerous biosurveillance algorithms may provide contradictory interpretations of ongoing events. We present a framework that consists of a collection of autonomous, machine learning-enabled analytic processes, services and tools that; for the first time, will seamlessly integrate surveillance and response systems with human experts.</p><p>Tags: <a style="text-decoration:underline;" href="http://slideshare.net/tag/biosurveillance">biosurveillance</a> <a style="text-decoration:underline;" href="http://slideshare.net/tag/machine">machine</a> <a style="text-decoration:underline;" href="http://slideshare.net/tag/learning">learning</a> <a style="text-decoration:underline;" href="http://slideshare.net/tag/early">early</a> <a style="text-decoration:underline;" href="http://slideshare.net/tag/detection">detection</a> </p></div> biosurveillance,machine,learning,early,detection, Biosurveillance: Machine Learning And Disease Surveillance by Kass-Hout Di Tada
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]]> http://cdn.slideshare.net/profile-photo-kasshout?1223776793 Taha’s professional approach to his work is based on clear articulation of the value of information technology, systems, cross-disciplinary science and research, and information handling in answering to most challenging and difficult health problems. Ta taha.instedd.org http://cdn.slideshare.net/epiinfomesh4xprototype-1229572607164845-1-thumbnail?1229572780 kasshout/epi-info-mesh4x-prototype-presentation Epi Info™ ─ Mesh4x Syn... http://cdn.slideshare.net/insteddisds2008syndromicseventhdraft-1228708186088416-9-thumbnail?1230011801 kasshout/edd-integrated-global-early-warning-system-taha-kasshout-and-nicols-di-tada-presentation Integrated Global Earl... http://cdn.slideshare.net/instedd-at-cdc-112408-1227573193926140-9-thumbnail?1227573246 kasshout/instedd-tools-for-outbreak-epidemiology-presentation InSTEDD Tools for Outb...