Matching Items (16)
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- All Subjects: Informatics
- All Subjects: Biomedical Informatics
- All Subjects: Diagnostic imaging
- Creators: Scotch, Matthew
Description
Mosquito population data is a valuable resource for researchers and public health officials working to limit the spread of deadly zoonotic viruses such as Zika Virus and West Nile Virus. Unfortunately, this data is currently difficult to obtain and aggregate across the United States. Obtaining historical data often requires filing requests to individual States or Counties and hoping for a response. Current online systems available for accessing aggregated data are lacking essential features, or limited in scope. In order to make mosquito population data more accessible for United States researchers, epidemiologists, and public health officials, the MosquitoDB system has been developed. MosquitoDB consists of a JavaScript Web Application, connected to a SQL database, that makes submitting and retrieving United States mosquito population data much simpler and straight forward than alternative systems. The MosquitoDB software project is open source and publically available on GitHub, allowing community scrutiny and contributions to add or improve necessary features. For this Creative Project, the core MosquitoDB system was designed and developed with 3 main features: 1) Web Interface for querying mosquito data. 2) Web Interface for submitting mosquito data. 3) Web Services for querying/retrieving and submitting mosquito data. The Web Interface is essential for common end users, such as researchers and public health officials, to access historical data or submit new data. The Web Services provide building blocks for Web Applications that other developers can use to incorporate data into new applications. The current MosquitoDB system is live at https://zodo.asu.edu/mosquito and the public code repository is available at https://github.com/developerDemetri/mosquitodb.
ContributorsJones-Shargani, Demetrius Paul (Author) / Scotch, Matthew (Thesis director) / Weissenbacher, Davy (Committee member) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2016-12
Description
Text mining of biomedical literature and clinical notes is a very active field of research in biomedical science. Semantic analysis is one of the core modules for different Natural Language Processing (NLP) solutions. Methods for calculating semantic relatedness of two concepts can be very useful in solutions solving different problems such as relationship extraction, ontology creation and question / answering [1–6]. Several techniques exist in calculating semantic relatedness of two concepts. These techniques utilize different knowledge sources and corpora. So far, researchers attempted to find the best hybrid method for each domain by combining semantic relatedness techniques and data sources manually. In this work, attempts were made to eliminate the needs for manually combining semantic relatedness methods targeting any new contexts or resources through proposing an automated method, which attempted to find the best combination of semantic relatedness techniques and resources to achieve the best semantic relatedness score in every context. This may help the research community find the best hybrid method for each context considering the available algorithms and resources.
ContributorsEmadzadeh, Ehsan (Author) / Gonzalez, Graciela (Thesis advisor) / Greenes, Robert (Committee member) / Scotch, Matthew (Committee member) / Arizona State University (Publisher)
Created2016
Description
Accurate quantitative information of tumor/lesion volume plays a critical role
in diagnosis and treatment assessment. The current clinical practice emphasizes on efficiency, but sacrifices accuracy (bias and precision). In the other hand, many computational algorithms focus on improving the accuracy, but are often time consuming and cumbersome to use. Not to mention that most of them lack validation studies on real clinical data. All of these hinder the translation of these advanced methods from benchside to bedside.
In this dissertation, I present a user interactive image application to rapidly extract accurate quantitative information of abnormalities (tumor/lesion) from multi-spectral medical images, such as measuring brain tumor volume from MRI. This is enabled by a GPU level set method, an intelligent algorithm to learn image features from user inputs, and a simple and intuitive graphical user interface with 2D/3D visualization. In addition, a comprehensive workflow is presented to validate image quantitative methods for clinical studies.
This application has been evaluated and validated in multiple cases, including quantifying healthy brain white matter volume from MRI and brain lesion volume from CT or MRI. The evaluation studies show that this application has been able to achieve comparable results to the state-of-the-art computer algorithms. More importantly, the retrospective validation study on measuring intracerebral hemorrhage volume from CT scans demonstrates that not only the measurement attributes are superior to the current practice method in terms of bias and precision but also it is achieved without a significant delay in acquisition time. In other words, it could be useful to the clinical trials and clinical practice, especially when intervention and prognostication rely upon accurate baseline lesion volume or upon detecting change in serial lesion volumetric measurements. Obviously, this application is useful to biomedical research areas which desire an accurate quantitative information of anatomies from medical images. In addition, the morphological information is retained also. This is useful to researches which require an accurate delineation of anatomic structures, such as surgery simulation and planning.
in diagnosis and treatment assessment. The current clinical practice emphasizes on efficiency, but sacrifices accuracy (bias and precision). In the other hand, many computational algorithms focus on improving the accuracy, but are often time consuming and cumbersome to use. Not to mention that most of them lack validation studies on real clinical data. All of these hinder the translation of these advanced methods from benchside to bedside.
In this dissertation, I present a user interactive image application to rapidly extract accurate quantitative information of abnormalities (tumor/lesion) from multi-spectral medical images, such as measuring brain tumor volume from MRI. This is enabled by a GPU level set method, an intelligent algorithm to learn image features from user inputs, and a simple and intuitive graphical user interface with 2D/3D visualization. In addition, a comprehensive workflow is presented to validate image quantitative methods for clinical studies.
This application has been evaluated and validated in multiple cases, including quantifying healthy brain white matter volume from MRI and brain lesion volume from CT or MRI. The evaluation studies show that this application has been able to achieve comparable results to the state-of-the-art computer algorithms. More importantly, the retrospective validation study on measuring intracerebral hemorrhage volume from CT scans demonstrates that not only the measurement attributes are superior to the current practice method in terms of bias and precision but also it is achieved without a significant delay in acquisition time. In other words, it could be useful to the clinical trials and clinical practice, especially when intervention and prognostication rely upon accurate baseline lesion volume or upon detecting change in serial lesion volumetric measurements. Obviously, this application is useful to biomedical research areas which desire an accurate quantitative information of anatomies from medical images. In addition, the morphological information is retained also. This is useful to researches which require an accurate delineation of anatomic structures, such as surgery simulation and planning.
ContributorsXue, Wenzhe (Author) / Kaufman, David (Thesis advisor) / Mitchell, J. Ross (Thesis advisor) / Johnson, William (Committee member) / Scotch, Matthew (Committee member) / Arizona State University (Publisher)
Created2016
ContributorsMendoza, Daniel (Author) / Grando, Adela (Thesis director) / Scotch, Matthew (Committee member) / Barrett, The Honors College (Contributor) / College of Health Solutions (Contributor) / School of Life Sciences (Contributor)
Created2023-05
ContributorsMendoza, Daniel (Author) / Grando, Adela (Thesis director) / Scotch, Matthew (Committee member) / Barrett, The Honors College (Contributor) / College of Health Solutions (Contributor) / School of Life Sciences (Contributor)
Created2023-05
ContributorsMendoza, Daniel (Author) / Grando, Adela (Thesis director) / Scotch, Matthew (Committee member) / Barrett, The Honors College (Contributor) / College of Health Solutions (Contributor) / School of Life Sciences (Contributor)
Created2023-05
ContributorsMendoza, Daniel (Author) / Grando, Adela (Thesis director) / Scotch, Matthew (Committee member) / Barrett, The Honors College (Contributor) / College of Health Solutions (Contributor) / School of Life Sciences (Contributor)
Created2023-05
ContributorsMendoza, Daniel (Author) / Grando, Adela (Thesis director) / Scotch, Matthew (Committee member) / Barrett, The Honors College (Contributor) / College of Health Solutions (Contributor) / School of Life Sciences (Contributor)
Created2023-05
ContributorsMendoza, Daniel (Author) / Grando, Adela (Thesis director) / Scotch, Matthew (Committee member) / Barrett, The Honors College (Contributor) / College of Health Solutions (Contributor) / School of Life Sciences (Contributor)
Created2023-05
ContributorsMendoza, Daniel (Author) / Grando, Adela (Thesis director) / Scotch, Matthew (Committee member) / Barrett, The Honors College (Contributor) / College of Health Solutions (Contributor) / School of Life Sciences (Contributor)
Created2023-05