際際滷shows by User: drmingle / http://www.slideshare.net/images/logo.gif 際際滷shows by User: drmingle / Mon, 26 Apr 2021 17:20:20 GMT 際際滷Share feed for 際際滷shows by User: drmingle Classify Rice Disease Using Self-Optimizing Models and Edge Computing with Agricultural Implications /slideshow/classify-rice-disease-using-selfoptimizing-models-and-edge-computing-with-agricultural-implications/247049368 artoaj-210426172020
Rice continues to be a primary food for the worlds population. Over its complex history, dating as far back as 8,000 B.C., there have been agricultural challenges, such as a variety of diseases. A consequence of disease in rice plants may lead to no harvest of grain; therefore, detecting disease early and providing expert remedies in a low-cost solution is highly desirable. In this article, we study a pragmatic approach for rice growers to leverage artificial intelligence solutions that reduce cost, increase speed, improve ease of use, and increase model performance over other solutions, thereby directly impacting field operations. Our method significantly improves upon prior methods by combining automated feature extraction for image data, exploring thousands of traditional machine learning configurations, defining a search space for hyperparameters, deploying a model using edge computing field usability, and suggesting remedies for rice growers. These results prove the validity of the proposed approach for rice disease detection and treatments.]]>
Rice continues to be a primary food for the worlds population. Over its complex history, dating as far back as 8,000 B.C., there have been agricultural challenges, such as a variety of diseases. A consequence of disease in rice plants may lead to no harvest of grain; therefore, detecting disease early and providing expert remedies in a low-cost solution is highly desirable. In this article, we study a pragmatic approach for rice growers to leverage artificial intelligence solutions that reduce cost, increase speed, improve ease of use, and increase model performance over other solutions, thereby directly impacting field operations. Our method significantly improves upon prior methods by combining automated feature extraction for image data, exploring thousands of traditional machine learning configurations, defining a search space for hyperparameters, deploying a model using edge computing field usability, and suggesting remedies for rice growers. These results prove the validity of the proposed approach for rice disease detection and treatments.]]> Mon, 26 Apr 2021 17:20:20 GMT /slideshow/classify-rice-disease-using-selfoptimizing-models-and-edge-computing-with-agricultural-implications/247049368 drmingle@slideshare.net(drmingle) Classify Rice Disease Using Self-Optimizing Models and Edge Computing with Agricultural Implications drmingle Rice continues to be a primary food for the worlds population. Over its complex history, dating as far back as 8,000 B.C., there have been agricultural challenges, such as a variety of diseases. A consequence of disease in rice plants may lead to no harvest of grain; therefore, detecting disease early and providing expert remedies in a low-cost solution is highly desirable. In this article, we study a pragmatic approach for rice growers to leverage artificial intelligence solutions that reduce cost, increase speed, improve ease of use, and increase model performance over other solutions, thereby directly impacting field operations. Our method significantly improves upon prior methods by combining automated feature extraction for image data, exploring thousands of traditional machine learning configurations, defining a search space for hyperparameters, deploying a model using edge computing field usability, and suggesting remedies for rice growers. These results prove the validity of the proposed approach for rice disease detection and treatments. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/artoaj-210426172020-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Rice continues to be a primary food for the worlds population. Over its complex history, dating as far back as 8,000 B.C., there have been agricultural challenges, such as a variety of diseases. A consequence of disease in rice plants may lead to no harvest of grain; therefore, detecting disease early and providing expert remedies in a low-cost solution is highly desirable. In this article, we study a pragmatic approach for rice growers to leverage artificial intelligence solutions that reduce cost, increase speed, improve ease of use, and increase model performance over other solutions, thereby directly impacting field operations. Our method significantly improves upon prior methods by combining automated feature extraction for image data, exploring thousands of traditional machine learning configurations, defining a search space for hyperparameters, deploying a model using edge computing field usability, and suggesting remedies for rice growers. These results prove the validity of the proposed approach for rice disease detection and treatments.
Classify Rice Disease Using Self-Optimizing Models and Edge Computing with Agricultural Implications from Damian R. Mingle, MBA
]]> 85 0 https://cdn.slidesharecdn.com/ss_thumbnails/artoaj-210426172020-thumbnail.jpg?width=120&height=120&fit=bounds document Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Predicting Diabetic Readmission Rates: Moving Beyond HbA1c /slideshow/predicting-diabetic-readmission-rates-moving-beyond-hba1c/73970688 mingledpredictingdiabeticreadmissionrates-movingbeyondhba1c-170330140220
Are you interested in learning how to prevent hospital readmissions for your diabetic population? It is a popular belief that measuring blood glucose for your diabetic population is the most predictive variable in determining a hospital readmission for a diabetic. However, many providers of care simply do not perform the test on known diabetic patients. This study takes a look at an advanced analytic method that works within the current healthcare providers workflow to looks to identify the likelihood of a future 30-day unplanned readmission before hospital discharge. ]]>
Are you interested in learning how to prevent hospital readmissions for your diabetic population? It is a popular belief that measuring blood glucose for your diabetic population is the most predictive variable in determining a hospital readmission for a diabetic. However, many providers of care simply do not perform the test on known diabetic patients. This study takes a look at an advanced analytic method that works within the current healthcare providers workflow to looks to identify the likelihood of a future 30-day unplanned readmission before hospital discharge. ]]> Thu, 30 Mar 2017 14:02:20 GMT /slideshow/predicting-diabetic-readmission-rates-moving-beyond-hba1c/73970688 drmingle@slideshare.net(drmingle) Predicting Diabetic Readmission Rates: Moving Beyond HbA1c drmingle Are you interested in learning how to prevent hospital readmissions for your diabetic population? It is a popular belief that measuring blood glucose for your diabetic population is the most predictive variable in determining a hospital readmission for a diabetic. However, many providers of care simply do not perform the test on known diabetic patients. This study takes a look at an advanced analytic method that works within the current healthcare providers workflow to looks to identify the likelihood of a future 30-day unplanned readmission before hospital discharge. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/mingledpredictingdiabeticreadmissionrates-movingbeyondhba1c-170330140220-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Are you interested in learning how to prevent hospital readmissions for your diabetic population? It is a popular belief that measuring blood glucose for your diabetic population is the most predictive variable in determining a hospital readmission for a diabetic. However, many providers of care simply do not perform the test on known diabetic patients. This study takes a look at an advanced analytic method that works within the current healthcare providers workflow to looks to identify the likelihood of a future 30-day unplanned readmission before hospital discharge.
Predicting Diabetic Readmission Rates: Moving Beyond HbA1c from Damian R. Mingle, MBA
]]> 677 11 https://cdn.slidesharecdn.com/ss_thumbnails/mingledpredictingdiabeticreadmissionrates-movingbeyondhba1c-170330140220-thumbnail.jpg?width=120&height=120&fit=bounds document Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Greek Letters with LaTeX Cheat Sheet /slideshow/greek-letters-with-latex-cheat-sheet/71499492 greekletterswithlatex-damianmingle-170128221818
Looking for a Greek Letters with LaTeX cheat sheet? We provide a gentle introduction to using LaTex and all the corresponding Greek symbols. ]]>
Looking for a Greek Letters with LaTeX cheat sheet? We provide a gentle introduction to using LaTex and all the corresponding Greek symbols. ]]> Sat, 28 Jan 2017 22:18:18 GMT /slideshow/greek-letters-with-latex-cheat-sheet/71499492 drmingle@slideshare.net(drmingle) Greek Letters with LaTeX Cheat Sheet drmingle Looking for a Greek Letters with LaTeX cheat sheet? We provide a gentle introduction to using LaTex and all the corresponding Greek symbols. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/greekletterswithlatex-damianmingle-170128221818-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Looking for a Greek Letters with LaTeX cheat sheet? We provide a gentle introduction to using LaTex and all the corresponding Greek symbols.
Greek Letters with LaTeX Cheat Sheet from Damian R. Mingle, MBA
]]> 760 10 https://cdn.slidesharecdn.com/ss_thumbnails/greekletterswithlatex-damianmingle-170128221818-thumbnail.jpg?width=120&height=120&fit=bounds document Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Clustering: A Scikit Learn Tutorial /slideshow/clustering-a-scikit-learn-tutorial/71436857 scikitlearn-kmeansclustering-damianmingle-170126213445
A brief introduction to clustering with Scikit learn. In this presentation, we provide an overview with real examples of how to make use and optimize within k-means clustering.]]>
A brief introduction to clustering with Scikit learn. In this presentation, we provide an overview with real examples of how to make use and optimize within k-means clustering.]]> Thu, 26 Jan 2017 21:34:45 GMT /slideshow/clustering-a-scikit-learn-tutorial/71436857 drmingle@slideshare.net(drmingle) Clustering: A Scikit Learn Tutorial drmingle A brief introduction to clustering with Scikit learn. In this presentation, we provide an overview with real examples of how to make use and optimize within k-means clustering. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/scikitlearn-kmeansclustering-damianmingle-170126213445-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> A brief introduction to clustering with Scikit learn. In this presentation, we provide an overview with real examples of how to make use and optimize within k-means clustering.
Clustering: A Scikit Learn Tutorial from Damian R. Mingle, MBA
]]> 711 7 https://cdn.slidesharecdn.com/ss_thumbnails/scikitlearn-kmeansclustering-damianmingle-170126213445-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Scikit Learn: How to Deal with Missing Values /slideshow/scikit-learn-how-to-deal-with-missing-values/71306728 scikitlearn-imputationformissingvalues-machinelearning-170123215407
This presentation provides a code walk through of an alternative to simply deleting rows or columns of data with missing values. Machine learning is used to supply the mean, median, or the most frequent value to create a more robust model which in many cases leads to higher prediction quality.]]>
This presentation provides a code walk through of an alternative to simply deleting rows or columns of data with missing values. Machine learning is used to supply the mean, median, or the most frequent value to create a more robust model which in many cases leads to higher prediction quality.]]> Mon, 23 Jan 2017 21:54:06 GMT /slideshow/scikit-learn-how-to-deal-with-missing-values/71306728 drmingle@slideshare.net(drmingle) Scikit Learn: How to Deal with Missing Values drmingle This presentation provides a code walk through of an alternative to simply deleting rows or columns of data with missing values. Machine learning is used to supply the mean, median, or the most frequent value to create a more robust model which in many cases leads to higher prediction quality. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/scikitlearn-imputationformissingvalues-machinelearning-170123215407-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> This presentation provides a code walk through of an alternative to simply deleting rows or columns of data with missing values. Machine learning is used to supply the mean, median, or the most frequent value to create a more robust model which in many cases leads to higher prediction quality.
Scikit Learn: How to Deal with Missing Values from Damian R. Mingle, MBA
]]> 388 2 https://cdn.slidesharecdn.com/ss_thumbnails/scikitlearn-imputationformissingvalues-machinelearning-170123215407-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 SciKit Learn: How to Standardize Your Data /slideshow/scikit-learn-how-to-standardize-your-data/71302808 scikitlearn-howtostandardizeyourdata-machinelearning-170123193640
Everybody starts in a different place with their Data Science/Machine Learning understanding. This presentation demonstrates the simplicity of SciKit learn preprocessing functions and a very important concept - standardization.]]>
Everybody starts in a different place with their Data Science/Machine Learning understanding. This presentation demonstrates the simplicity of SciKit learn preprocessing functions and a very important concept - standardization.]]> Mon, 23 Jan 2017 19:36:40 GMT /slideshow/scikit-learn-how-to-standardize-your-data/71302808 drmingle@slideshare.net(drmingle) SciKit Learn: How to Standardize Your Data drmingle Everybody starts in a different place with their Data Science/Machine Learning understanding. This presentation demonstrates the simplicity of SciKit learn preprocessing functions and a very important concept - standardization. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/scikitlearn-howtostandardizeyourdata-machinelearning-170123193640-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Everybody starts in a different place with their Data Science/Machine Learning understanding. This presentation demonstrates the simplicity of SciKit learn preprocessing functions and a very important concept - standardization.
SciKit Learn: How to Standardize Your Data from Damian R. Mingle, MBA
]]> 398 3 https://cdn.slidesharecdn.com/ss_thumbnails/scikitlearn-howtostandardizeyourdata-machinelearning-170123193640-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Scikit Learn: Data Normalization Techniques That Work /slideshow/scikit-learn-data-normalization-techniques-that-work/71298960 scikitlearn-helpyourdatabenormal-machinelearning-170123174116
Data scientists come in all shapes and sizes when it comes to understanding and experience of machine learning. We take a look at what's possible sklearns capabilities using Python. Concerning data normalization, we make clear what others make difficult to understand. In the case of data normalization, this presentation is an easy to use introduction to machine learning in Python.]]>
Data scientists come in all shapes and sizes when it comes to understanding and experience of machine learning. We take a look at what's possible sklearns capabilities using Python. Concerning data normalization, we make clear what others make difficult to understand. In the case of data normalization, this presentation is an easy to use introduction to machine learning in Python.]]> Mon, 23 Jan 2017 17:41:16 GMT /slideshow/scikit-learn-data-normalization-techniques-that-work/71298960 drmingle@slideshare.net(drmingle) Scikit Learn: Data Normalization Techniques That Work drmingle Data scientists come in all shapes and sizes when it comes to understanding and experience of machine learning. We take a look at what's possible sklearns capabilities using Python. Concerning data normalization, we make clear what others make difficult to understand. In the case of data normalization, this presentation is an easy to use introduction to machine learning in Python. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/scikitlearn-helpyourdatabenormal-machinelearning-170123174116-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Data scientists come in all shapes and sizes when it comes to understanding and experience of machine learning. We take a look at what&#39;s possible sklearns capabilities using Python. Concerning data normalization, we make clear what others make difficult to understand. In the case of data normalization, this presentation is an easy to use introduction to machine learning in Python.
Scikit Learn: Data Normalization Techniques That Work from Damian R. Mingle, MBA
]]> 381 5 https://cdn.slidesharecdn.com/ss_thumbnails/scikitlearn-helpyourdatabenormal-machinelearning-170123174116-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 What is sepsis? /slideshow/what-is-sepsis-70915417/70915417 whatissepsis-170111172041
The definition of sepsis continues to change for both patients and healthcare providers. Medicine does not currently share a consensus understanding of sepsis which may place patients at greater risk. This presentation goes beyond the controversy of Sepsis-3 and provides a Data Science solution to the 3,000-year-old problem known as sepsis.]]>
The definition of sepsis continues to change for both patients and healthcare providers. Medicine does not currently share a consensus understanding of sepsis which may place patients at greater risk. This presentation goes beyond the controversy of Sepsis-3 and provides a Data Science solution to the 3,000-year-old problem known as sepsis.]]> Wed, 11 Jan 2017 17:20:41 GMT /slideshow/what-is-sepsis-70915417/70915417 drmingle@slideshare.net(drmingle) What is sepsis? drmingle The definition of sepsis continues to change for both patients and healthcare providers. Medicine does not currently share a consensus understanding of sepsis which may place patients at greater risk. This presentation goes beyond the controversy of Sepsis-3 and provides a Data Science solution to the 3,000-year-old problem known as sepsis. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/whatissepsis-170111172041-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> The definition of sepsis continues to change for both patients and healthcare providers. Medicine does not currently share a consensus understanding of sepsis which may place patients at greater risk. This presentation goes beyond the controversy of Sepsis-3 and provides a Data Science solution to the 3,000-year-old problem known as sepsis.
What is sepsis? from Damian R. Mingle, MBA
]]> 904 4 https://cdn.slidesharecdn.com/ss_thumbnails/whatissepsis-170111172041-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Controlling informative features for improved accuracy and faster predictions in omentum cancer models /drmingle/controlling-informative-features-for-improved-accuracy-and-faster-predictions-in-omentum-cancer-models mingled-controllinginformativefeaturesforimprovedaccuracyandfasterpredictionsinomentumcancermodels-170107144021
Identification of suitable biomarkers for accurate prediction of phenotypic outcomes is a goal for personalized medicine. However, current machine learning approaches are either too complex or perform poorly. For more information: http://societyofdatascientists.com/controlling-informative-features-for-improved-accuracy-and-faster-predictions-in-omentum-cancer-models/?src=slideshare ]]>
Identification of suitable biomarkers for accurate prediction of phenotypic outcomes is a goal for personalized medicine. However, current machine learning approaches are either too complex or perform poorly. For more information: http://societyofdatascientists.com/controlling-informative-features-for-improved-accuracy-and-faster-predictions-in-omentum-cancer-models/?src=slideshare ]]> Sat, 07 Jan 2017 14:40:21 GMT /drmingle/controlling-informative-features-for-improved-accuracy-and-faster-predictions-in-omentum-cancer-models drmingle@slideshare.net(drmingle) Controlling informative features for improved accuracy and faster predictions in omentum cancer models drmingle Identification of suitable biomarkers for accurate prediction of phenotypic outcomes is a goal for personalized medicine. However, current machine learning approaches are either too complex or perform poorly. For more information: http://societyofdatascientists.com/controlling-informative-features-for-improved-accuracy-and-faster-predictions-in-omentum-cancer-models/?src=slideshare <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/mingled-controllinginformativefeaturesforimprovedaccuracyandfasterpredictionsinomentumcancermodels-170107144021-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Identification of suitable biomarkers for accurate prediction of phenotypic outcomes is a goal for personalized medicine. However, current machine learning approaches are either too complex or perform poorly. For more information: http://societyofdatascientists.com/controlling-informative-features-for-improved-accuracy-and-faster-predictions-in-omentum-cancer-models/?src=slideshare
Controlling informative features for improved accuracy and faster predictions in omentum cancer models from Damian R. Mingle, MBA
]]> 646 5 https://cdn.slidesharecdn.com/ss_thumbnails/mingled-controllinginformativefeaturesforimprovedaccuracyandfasterpredictionsinomentumcancermodels-170107144021-thumbnail.jpg?width=120&height=120&fit=bounds document Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 The evolving definition of sepsis /drmingle/the-evolving-definition-of-sepsis theevolvingdefinitionofsepsisgarymingleandyenamandrainternationalclinicalpathologyjournal-161022153315
This paper reviews the evolution of the definition of sepsis and the controversy surrounding the sepsis-3 definition and the sepsis screening tool, qSOFA.]]>
This paper reviews the evolution of the definition of sepsis and the controversy surrounding the sepsis-3 definition and the sepsis screening tool, qSOFA.]]> Sat, 22 Oct 2016 15:33:15 GMT /drmingle/the-evolving-definition-of-sepsis drmingle@slideshare.net(drmingle) The evolving definition of sepsis drmingle This paper reviews the evolution of the definition of sepsis and the controversy surrounding the sepsis-3 definition and the sepsis screening tool, qSOFA. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/theevolvingdefinitionofsepsisgarymingleandyenamandrainternationalclinicalpathologyjournal-161022153315-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> This paper reviews the evolution of the definition of sepsis and the controversy surrounding the sepsis-3 definition and the sepsis screening tool, qSOFA.
The evolving definition of sepsis from Damian R. Mingle, MBA
]]> 258 5 https://cdn.slidesharecdn.com/ss_thumbnails/theevolvingdefinitionofsepsisgarymingleandyenamandrainternationalclinicalpathologyjournal-161022153315-thumbnail.jpg?width=120&height=120&fit=bounds document Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Data and the Changing Role of the Tech Savvy CFO /slideshow/data-and-the-changing-role-of-the-tech-savvy-cfo/67481128 wpchealthcare-lbmc-techsavvycfo-17oct16-161021024028
A talk on the changing role of the CFO and the importance of utilizing data in making educated business decisions.]]>
A talk on the changing role of the CFO and the importance of utilizing data in making educated business decisions.]]> Fri, 21 Oct 2016 02:40:28 GMT /slideshow/data-and-the-changing-role-of-the-tech-savvy-cfo/67481128 drmingle@slideshare.net(drmingle) Data and the Changing Role of the Tech Savvy CFO drmingle A talk on the changing role of the CFO and the importance of utilizing data in making educated business decisions. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/wpchealthcare-lbmc-techsavvycfo-17oct16-161021024028-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> A talk on the changing role of the CFO and the importance of utilizing data in making educated business decisions.
Data and the Changing Role of the Tech Savvy CFO from Damian R. Mingle, MBA
]]> 159 2 https://cdn.slidesharecdn.com/ss_thumbnails/wpchealthcare-lbmc-techsavvycfo-17oct16-161021024028-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 A discriminative-feature-space-for-detecting-and-recognizing-pathologies-of-the-vertebral-column-2090-4924-1000114 /slideshow/a-discriminativefeaturespacefordetectingandrecognizingpathologiesofthevertebralcolumn209049241000114/59988117 a-discriminative-feature-space-for-detecting-and-recognizing-pathologies-of-the-vertebral-column-209-160324124742
Each year it has become more and more difficult for healthcare providers to determine if a patient has a pathology related to the vertebral column. There is great potential to become more efficient and effective in terms of quality of care provided to patients through the use of automated systems. However, in many cases automated systems can allow for misclassification and force providers to have to review more causes than necessary. In this study, we analyzed methods to increase the True Positives and lower the False Positives while comparing them against stateof-the-art techniques in the biomedical community. We found that by applying the studied techniques of a data-driven model, the benefits to healthcare providers are significant and align with the methodologies and techniques utilized in the current research community.]]>
Each year it has become more and more difficult for healthcare providers to determine if a patient has a pathology related to the vertebral column. There is great potential to become more efficient and effective in terms of quality of care provided to patients through the use of automated systems. However, in many cases automated systems can allow for misclassification and force providers to have to review more causes than necessary. In this study, we analyzed methods to increase the True Positives and lower the False Positives while comparing them against stateof-the-art techniques in the biomedical community. We found that by applying the studied techniques of a data-driven model, the benefits to healthcare providers are significant and align with the methodologies and techniques utilized in the current research community.]]> Thu, 24 Mar 2016 12:47:42 GMT /slideshow/a-discriminativefeaturespacefordetectingandrecognizingpathologiesofthevertebralcolumn209049241000114/59988117 drmingle@slideshare.net(drmingle) A discriminative-feature-space-for-detecting-and-recognizing-pathologies-of-the-vertebral-column-2090-4924-1000114 drmingle Each year it has become more and more difficult for healthcare providers to determine if a patient has a pathology related to the vertebral column. There is great potential to become more efficient and effective in terms of quality of care provided to patients through the use of automated systems. However, in many cases automated systems can allow for misclassification and force providers to have to review more causes than necessary. In this study, we analyzed methods to increase the True Positives and lower the False Positives while comparing them against stateof-the-art techniques in the biomedical community. We found that by applying the studied techniques of a data-driven model, the benefits to healthcare providers are significant and align with the methodologies and techniques utilized in the current research community. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/a-discriminative-feature-space-for-detecting-and-recognizing-pathologies-of-the-vertebral-column-209-160324124742-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Each year it has become more and more difficult for healthcare providers to determine if a patient has a pathology related to the vertebral column. There is great potential to become more efficient and effective in terms of quality of care provided to patients through the use of automated systems. However, in many cases automated systems can allow for misclassification and force providers to have to review more causes than necessary. In this study, we analyzed methods to increase the True Positives and lower the False Positives while comparing them against stateof-the-art techniques in the biomedical community. We found that by applying the studied techniques of a data-driven model, the benefits to healthcare providers are significant and align with the methodologies and techniques utilized in the current research community.
A discriminative-feature-space-for-detecting-and-recognizing-pathologies-of-the-vertebral-column-2090-4924-1000114 from Damian R. Mingle, MBA
]]> 363 5 https://cdn.slidesharecdn.com/ss_thumbnails/a-discriminative-feature-space-for-detecting-and-recognizing-pathologies-of-the-vertebral-column-209-160324124742-thumbnail.jpg?width=120&height=120&fit=bounds document Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Practical Data Science the WPC Healthcare Strategy for Delivering Meaningful Data Science Projects /slideshow/practical-data-science-the-wpc-healthcare-strategy-for-delivering-meaningful-data-science-projects/53195747 practicaldatasciencethewpchealthcarestrategyfordeliveringmeaningfuldatascienceprojects-150925135329-lva1-app6891
Learning to make use of Jupyter to document your Data Science process - real time - and in whatever programming language you want! Using this methodology will allow you to provide insights that help your organization make better decisions to solve their business problems. ]]>
Learning to make use of Jupyter to document your Data Science process - real time - and in whatever programming language you want! Using this methodology will allow you to provide insights that help your organization make better decisions to solve their business problems. ]]> Fri, 25 Sep 2015 13:53:29 GMT /slideshow/practical-data-science-the-wpc-healthcare-strategy-for-delivering-meaningful-data-science-projects/53195747 drmingle@slideshare.net(drmingle) Practical Data Science the WPC Healthcare Strategy for Delivering Meaningful Data Science Projects drmingle Learning to make use of Jupyter to document your Data Science process - real time - and in whatever programming language you want! Using this methodology will allow you to provide insights that help your organization make better decisions to solve their business problems. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/practicaldatasciencethewpchealthcarestrategyfordeliveringmeaningfuldatascienceprojects-150925135329-lva1-app6891-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Learning to make use of Jupyter to document your Data Science process - real time - and in whatever programming language you want! Using this methodology will allow you to provide insights that help your organization make better decisions to solve their business problems.
Practical Data Science the WPC Healthcare Strategy for Delivering Meaningful Data Science Projects from Damian R. Mingle, MBA
]]> 695 5 https://cdn.slidesharecdn.com/ss_thumbnails/practicaldatasciencethewpchealthcarestrategyfordeliveringmeaningfuldatascienceprojects-150925135329-lva1-app6891-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 A Multi-Pronged Approach to Data Mining Post-Acute Care Episodes /drmingle/a-multipronged-approach-to-data-mining-postacute-care-episodes amulti-prongedapproachtodataminingpost-acutecareepisodesminglegreen-150925122134-lva1-app6891
This study evaluates the opportunities available to Post-Acute Care providers who want to participate in redesigning their segment of the care continuum, speci鍖c to the Bundled Payments for Care Improvement Initiative (BPCI). We clarify how the BPCI Model 3 episodes of care are de鍖ned, the 鍖nancial risk assumed by applicants, and the partnerships needed to mitigate risk by care coordination and redesign of clinical strategy. Furthermore, using data mining techniques, applied statistics, and applied contextual science, we present 鍖ndings through visualizations enabling data discovery and accountability.]]>
This study evaluates the opportunities available to Post-Acute Care providers who want to participate in redesigning their segment of the care continuum, speci鍖c to the Bundled Payments for Care Improvement Initiative (BPCI). We clarify how the BPCI Model 3 episodes of care are de鍖ned, the 鍖nancial risk assumed by applicants, and the partnerships needed to mitigate risk by care coordination and redesign of clinical strategy. Furthermore, using data mining techniques, applied statistics, and applied contextual science, we present 鍖ndings through visualizations enabling data discovery and accountability.]]> Fri, 25 Sep 2015 12:21:34 GMT /drmingle/a-multipronged-approach-to-data-mining-postacute-care-episodes drmingle@slideshare.net(drmingle) A Multi-Pronged Approach to Data Mining Post-Acute Care Episodes drmingle This study evaluates the opportunities available to Post-Acute Care providers who want to participate in redesigning their segment of the care continuum, speci鍖c to the Bundled Payments for Care Improvement Initiative (BPCI). We clarify how the BPCI Model 3 episodes of care are de鍖ned, the 鍖nancial risk assumed by applicants, and the partnerships needed to mitigate risk by care coordination and redesign of clinical strategy. Furthermore, using data mining techniques, applied statistics, and applied contextual science, we present 鍖ndings through visualizations enabling data discovery and accountability. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/amulti-prongedapproachtodataminingpost-acutecareepisodesminglegreen-150925122134-lva1-app6891-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> This study evaluates the opportunities available to Post-Acute Care providers who want to participate in redesigning their segment of the care continuum, speci鍖c to the Bundled Payments for Care Improvement Initiative (BPCI). We clarify how the BPCI Model 3 episodes of care are de鍖ned, the 鍖nancial risk assumed by applicants, and the partnerships needed to mitigate risk by care coordination and redesign of clinical strategy. Furthermore, using data mining techniques, applied statistics, and applied contextual science, we present 鍖ndings through visualizations enabling data discovery and accountability.
A Multi-Pronged Approach to Data Mining Post-Acute Care Episodes from Damian R. Mingle, MBA
]]> 526 4 https://cdn.slidesharecdn.com/ss_thumbnails/amulti-prongedapproachtodataminingpost-acutecareepisodesminglegreen-150925122134-lva1-app6891-thumbnail.jpg?width=120&height=120&fit=bounds document Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 https://cdn.slidesharecdn.com/profile-photo-drmingle-48x48.jpg?cb=1618432325 If you want to raise new questions and new possibilities to your old healthcare problems, we should connect. Intermedix brings the creative imagination and expert technical skills required to make a real advancement for your organization. We can help no matter your company's size. Our products create better patient outcomes and impact the bottom-line. We help democratize analytics, accelerate time-to-value, and communicate with non-technical personnel. At our core, we provide high-technology solutions that can be accessed in low-technology ways. .. SPECIALTIES deep learning, game theory, topic modeling, natural language processing, time series, bayesian inference, reinforcem... www.SocietyofDataScientists.com https://cdn.slidesharecdn.com/ss_thumbnails/artoaj-210426172020-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/classify-rice-disease-using-selfoptimizing-models-and-edge-computing-with-agricultural-implications/247049368 Classify Rice Disease ... https://cdn.slidesharecdn.com/ss_thumbnails/mingledpredictingdiabeticreadmissionrates-movingbeyondhba1c-170330140220-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/predicting-diabetic-readmission-rates-moving-beyond-hba1c/73970688 Predicting Diabetic Re... https://cdn.slidesharecdn.com/ss_thumbnails/greekletterswithlatex-damianmingle-170128221818-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/greek-letters-with-latex-cheat-sheet/71499492 Greek Letters with LaT...