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Description
Surgery as a profession requires significant training to improve both clinical decision making and psychomotor proficiency. In the medical knowledge domain, tools have been developed, validated, and accepted for evaluation of surgeons' competencies. However, assessment of the psychomotor skills still relies on the Halstedian model of apprenticeship, wherein surgeons are observed during residency for judgment of their skills. Although the value of this method of skills assessment cannot be ignored, novel methodologies of objective skills assessment need to be designed, developed, and evaluated that augment the traditional approach. Several sensor-based systems have been developed to measure a user's skill quantitatively, but use of sensors could interfere with skill execution and thus limit the potential for evaluating real-life surgery. However, having a method to judge skills automatically in real-life conditions should be the ultimate goal, since only with such features that a system would be widely adopted. This research proposes a novel video-based approach for observing surgeons' hand and surgical tool movements in minimally invasive surgical training exercises as well as during laparoscopic surgery. Because our system does not require surgeons to wear special sensors, it has the distinct advantage over alternatives of offering skills assessment in both learning and real-life environments. The system automatically detects major skill-measuring features from surgical task videos using a computing system composed of a series of computer vision algorithms and provides on-screen real-time performance feedback for more efficient skill learning. Finally, the machine-learning approach is used to develop an observer-independent composite scoring model through objective and quantitative measurement of surgical skills. To increase effectiveness and usability of the developed system, it is integrated with a cloud-based tool, which automatically assesses surgical videos upload to the cloud.
ContributorsIslam, Gazi (Author) / Li, Baoxin (Thesis advisor) / Liang, Jianming (Thesis advisor) / Dinu, Valentin (Committee member) / Greenes, Robert (Committee member) / Smith, Marshall (Committee member) / Kahol, Kanav (Committee member) / Patel, Vimla L. (Committee member) / Arizona State University (Publisher)
Created2013
Description
This work involved the analysis of a public health system, and the design, development and deployment of enterprise informatics architecture, and sustainable community methods to address problems with the current public health system. Specifically, assessment of the Nationally Notifiable Disease Surveillance System (NNDSS) was instrumental in forming the design of the current implementation at the Southern Nevada Health District (SNHD). The result of the system deployment at SNHD was considered as a basis for projecting the practical application and benefits of an enterprise architecture. This approach has resulted in a sustainable platform to enhance the practice of public health by improving the quality and timeliness of data, effectiveness of an investigation, and reporting across the continuum.
ContributorsKriseman, Jeffrey Michael (Author) / Dinu, Valentin (Thesis advisor) / Greenes, Robert (Committee member) / Johnson, William (Committee member) / Arizona State University (Publisher)
Created2012
Description
Social media is becoming increasingly popular as a platform for sharing personal health-related information. This information can be utilized for public health monitoring tasks such as pharmacovigilance via the use of Natural Language Processing (NLP) techniques. One of the critical steps in information extraction pipelines is Named Entity Recognition (NER), where the mentions of entities such as diseases are located in text and their entity type are identified. However, the language in social media is highly informal, and user-expressed health-related concepts are often non-technical, descriptive, and challenging to extract. There has been limited progress in addressing these challenges, and advanced machine learning-based NLP techniques have been underutilized. This work explores the effectiveness of different machine learning techniques, and particularly deep learning, to address the challenges associated with extraction of health-related concepts from social media. Deep learning has recently attracted a lot of attention in machine learning research and has shown remarkable success in several applications particularly imaging and speech recognition. However, thus far, deep learning techniques are relatively unexplored for biomedical text mining and, in particular, this is the first attempt in applying deep learning for health information extraction from social media.
This work presents ADRMine that uses a Conditional Random Field (CRF) sequence tagger for extraction of complex health-related concepts. It utilizes a large volume of unlabeled user posts for automatic learning of embedding cluster features, a novel application of deep learning in modeling the similarity between the tokens. ADRMine significantly improved the medical NER performance compared to the baseline systems.
This work also presents DeepHealthMiner, a deep learning pipeline for health-related concept extraction. Most of the machine learning methods require sophisticated task-specific manual feature design which is a challenging step in processing the informal and noisy content of social media. DeepHealthMiner automatically learns classification features using neural networks and utilizing a large volume of unlabeled user posts. Using a relatively small labeled training set, DeepHealthMiner could accurately identify most of the concepts, including the consumer expressions that were not observed in the training data or in the standard medical lexicons outperforming the state-of-the-art baseline techniques.
This work presents ADRMine that uses a Conditional Random Field (CRF) sequence tagger for extraction of complex health-related concepts. It utilizes a large volume of unlabeled user posts for automatic learning of embedding cluster features, a novel application of deep learning in modeling the similarity between the tokens. ADRMine significantly improved the medical NER performance compared to the baseline systems.
This work also presents DeepHealthMiner, a deep learning pipeline for health-related concept extraction. Most of the machine learning methods require sophisticated task-specific manual feature design which is a challenging step in processing the informal and noisy content of social media. DeepHealthMiner automatically learns classification features using neural networks and utilizing a large volume of unlabeled user posts. Using a relatively small labeled training set, DeepHealthMiner could accurately identify most of the concepts, including the consumer expressions that were not observed in the training data or in the standard medical lexicons outperforming the state-of-the-art baseline techniques.
ContributorsNikfarjam, Azadeh (Author) / Gonzalez, Graciela (Thesis advisor) / Greenes, Robert (Committee member) / Scotch, Matthew (Committee member) / Arizona State University (Publisher)
Created2016
Description
With the recent rise in opioid overdose and death1<br/><br/>, chronic opioid therapy (COT) programs using<br/>Center of Disease Control (CDC) guidelines have been implemented across the United States8<br/>.<br/>Primary care clinicians at Mayo Clinic initiated a COT program in September of 2017, during the<br/>use of Cerner Electronic Health Record (EHR) system. Study metrics included provider<br/>satisfaction and perceptions regarding opioid prescription. Mayo Clinic transitioned its EHR<br/>system from Cerner to Epic in October 2018. This study aims to understand if provider perceptions<br/>about COT changed after the EHR transition and the reasons underlying those perceptions.
ContributorsPonnapalli, Sravya (Author) / Murcko, Anita (Thesis director) / Wallace, Mark (Committee member) / College of Health Solutions (Contributor) / Barrett, The Honors College (Contributor)
Created2021-05
Description
Colorectal cancer is the second-highest cause of cancer-related deaths in the United States with approximately 50,000 estimated deaths in 2015. The advanced stages of colorectal cancer has a poor five-year survival rate of 10%, whereas the diagnosis in early stages of development has showed a more favorable five-year survival rate of 90%. Early diagnosis of colorectal cancer is achievable if colorectal polyps, a possible precursor to cancer, are detected and removed before developing into malignancy.
The preferred method for polyp detection and removal is optical colonoscopy. A colonoscopic procedure consists of two phases: (1) insertion phase during which a flexible endoscope (a flexible tube with a tiny video camera at the tip) is advanced via the anus and then gradually to the end of the colon--called the cecum, and (2) withdrawal phase during which the endoscope is gradually withdrawn while colonoscopists examine the colon wall to find and remove polyps. Colonoscopy is an effective procedure and has led to a significant decline in the incidence and mortality of colon cancer. However, despite many screening and therapeutic advantages, 1 out of every 4 polyps and 1 out of 13 colon cancers are missed during colonoscopy.
There are many factors that contribute to missed polyps and cancers including poor colon preparation, inadequate navigational skills, and fatigue. Poor colon preparation results in a substantial portion of colon covered with fecal content, hindering a careful examination of the colon. Inadequate navigational skills can prevent a colonoscopist from examining hard-to-reach regions of the colon that may contain a polyp. Fatigue can manifest itself in the performance of a colonoscopist by decreasing diligence and vigilance during procedures. Lack of vigilance may prevent a colonoscopist from detecting the polyps that briefly appear in the colonoscopy videos. Lack of diligence may result in hasty examination of the colon that is likely to miss polyps and lesions.
To reduce polyp and cancer miss rates, this research presents a quality assurance system with 3 components. The first component is an automatic polyp detection system that highlights the regions with suspected polyps in colonoscopy videos. The goal is to encourage more vigilance during procedures. The suggested polyp detection system consists of several novel modules: (1) a new patch descriptor that characterizes image appearance around boundaries more accurately and more efficiently than widely-used patch descriptors such HoG, LBP, and Daisy; (2) A 2-stage classification framework that is able to enhance low level image features prior to classification. Unlike the traditional way of image classification where a single patch undergoes the processing pipeline, our system fuses the information extracted from a pair of patches for more accurate edge classification; (3) a new vote accumulation scheme that robustly localizes objects with curvy boundaries in fragmented edge maps. Our voting scheme produces a probabilistic output for each polyp candidate but unlike the existing methods (e.g., Hough transform) does not require any predefined parametric model of the object of interest; (4) and a unique three-way image representation coupled with convolutional neural networks (CNNs) for classifying the polyp candidates. Our image representation efficiently captures a variety of features such as color, texture, shape, and temporal information and significantly improves the performance of the subsequent CNNs for candidate classification. This contrasts with the exiting methods that mainly rely on a subset of the above image features for polyp detection. Furthermore, this research is the first to investigate the use of CNNs for polyp detection in colonoscopy videos.
The second component of our quality assurance system is an automatic image quality assessment for colonoscopy. The goal is to encourage more diligence during procedures by warning against hasty and low quality colon examination. We detect a low quality colon examination by identifying a number of consecutive non-informative frames in videos. We base our methodology for detecting non-informative frames on two key observations: (1) non-informative frames
most often show an unrecognizable scene with few details and blurry edges and thus their information can be locally compressed in a few Discrete Cosine Transform (DCT) coefficients; however, informative images include much more details and their information content cannot be summarized by a small subset of DCT coefficients; (2) information content is spread all over the image in the case of informative frames, whereas in non-informative frames, depending on image artifacts and degradation factors, details may appear in only a few regions. We use the former observation in designing our global features and the latter in designing our local image features. We demonstrated that the suggested new features are superior to the existing features based on wavelet and Fourier transforms.
The third component of our quality assurance system is a 3D visualization system. The goal is to provide colonoscopists with feedback about the regions of the colon that have remained unexamined during colonoscopy, thereby helping them improve their navigational skills. The suggested system is based on a new 3D reconstruction algorithm that combines depth and position information for 3D reconstruction. We propose to use a depth camera and a tracking sensor to obtain depth and position information. Our system contrasts with the existing works where the depth and position information are unreliably estimated from the colonoscopy frames. We conducted a use case experiment, demonstrating that the suggested 3D visualization system can determine the unseen regions of the navigated environment. However, due to technology limitations, we were not able to evaluate our 3D visualization system using a phantom model of the colon.
The preferred method for polyp detection and removal is optical colonoscopy. A colonoscopic procedure consists of two phases: (1) insertion phase during which a flexible endoscope (a flexible tube with a tiny video camera at the tip) is advanced via the anus and then gradually to the end of the colon--called the cecum, and (2) withdrawal phase during which the endoscope is gradually withdrawn while colonoscopists examine the colon wall to find and remove polyps. Colonoscopy is an effective procedure and has led to a significant decline in the incidence and mortality of colon cancer. However, despite many screening and therapeutic advantages, 1 out of every 4 polyps and 1 out of 13 colon cancers are missed during colonoscopy.
There are many factors that contribute to missed polyps and cancers including poor colon preparation, inadequate navigational skills, and fatigue. Poor colon preparation results in a substantial portion of colon covered with fecal content, hindering a careful examination of the colon. Inadequate navigational skills can prevent a colonoscopist from examining hard-to-reach regions of the colon that may contain a polyp. Fatigue can manifest itself in the performance of a colonoscopist by decreasing diligence and vigilance during procedures. Lack of vigilance may prevent a colonoscopist from detecting the polyps that briefly appear in the colonoscopy videos. Lack of diligence may result in hasty examination of the colon that is likely to miss polyps and lesions.
To reduce polyp and cancer miss rates, this research presents a quality assurance system with 3 components. The first component is an automatic polyp detection system that highlights the regions with suspected polyps in colonoscopy videos. The goal is to encourage more vigilance during procedures. The suggested polyp detection system consists of several novel modules: (1) a new patch descriptor that characterizes image appearance around boundaries more accurately and more efficiently than widely-used patch descriptors such HoG, LBP, and Daisy; (2) A 2-stage classification framework that is able to enhance low level image features prior to classification. Unlike the traditional way of image classification where a single patch undergoes the processing pipeline, our system fuses the information extracted from a pair of patches for more accurate edge classification; (3) a new vote accumulation scheme that robustly localizes objects with curvy boundaries in fragmented edge maps. Our voting scheme produces a probabilistic output for each polyp candidate but unlike the existing methods (e.g., Hough transform) does not require any predefined parametric model of the object of interest; (4) and a unique three-way image representation coupled with convolutional neural networks (CNNs) for classifying the polyp candidates. Our image representation efficiently captures a variety of features such as color, texture, shape, and temporal information and significantly improves the performance of the subsequent CNNs for candidate classification. This contrasts with the exiting methods that mainly rely on a subset of the above image features for polyp detection. Furthermore, this research is the first to investigate the use of CNNs for polyp detection in colonoscopy videos.
The second component of our quality assurance system is an automatic image quality assessment for colonoscopy. The goal is to encourage more diligence during procedures by warning against hasty and low quality colon examination. We detect a low quality colon examination by identifying a number of consecutive non-informative frames in videos. We base our methodology for detecting non-informative frames on two key observations: (1) non-informative frames
most often show an unrecognizable scene with few details and blurry edges and thus their information can be locally compressed in a few Discrete Cosine Transform (DCT) coefficients; however, informative images include much more details and their information content cannot be summarized by a small subset of DCT coefficients; (2) information content is spread all over the image in the case of informative frames, whereas in non-informative frames, depending on image artifacts and degradation factors, details may appear in only a few regions. We use the former observation in designing our global features and the latter in designing our local image features. We demonstrated that the suggested new features are superior to the existing features based on wavelet and Fourier transforms.
The third component of our quality assurance system is a 3D visualization system. The goal is to provide colonoscopists with feedback about the regions of the colon that have remained unexamined during colonoscopy, thereby helping them improve their navigational skills. The suggested system is based on a new 3D reconstruction algorithm that combines depth and position information for 3D reconstruction. We propose to use a depth camera and a tracking sensor to obtain depth and position information. Our system contrasts with the existing works where the depth and position information are unreliably estimated from the colonoscopy frames. We conducted a use case experiment, demonstrating that the suggested 3D visualization system can determine the unseen regions of the navigated environment. However, due to technology limitations, we were not able to evaluate our 3D visualization system using a phantom model of the colon.
ContributorsTajbakhsh, Nima (Author) / Liang, Jianming (Thesis advisor) / Greenes, Robert (Committee member) / Scotch, Matthew (Committee member) / Arizona State University (Publisher)
Created2015
Description
Unstructured texts containing biomedical information from sources such as electronic health records, scientific literature, discussion forums, and social media offer an opportunity to extract information for a wide range of applications in biomedical informatics. Building scalable and efficient pipelines for natural language processing and extraction of biomedical information plays an important role in the implementation and adoption of applications in areas such as public health. Advancements in machine learning and deep learning techniques have enabled rapid development of such pipelines. This dissertation presents entity extraction pipelines for two public health applications: virus phylogeography and pharmacovigilance. For virus phylogeography, geographical locations are extracted from biomedical scientific texts for metadata enrichment in the GenBank database containing 2.9 million virus nucleotide sequences. For pharmacovigilance, tools are developed to extract adverse drug reactions from social media posts to open avenues for post-market drug surveillance from non-traditional sources. Across these pipelines, high variance is observed in extraction performance among the entities of interest while using state-of-the-art neural network architectures. To explain the variation, linguistic measures are proposed to serve as indicators for entity extraction performance and to provide deeper insight into the domain complexity and the challenges associated with entity extraction. For both the phylogeography and pharmacovigilance pipelines presented in this work the annotated datasets and applications are open source and freely available to the public to foster further research in public health.
ContributorsMagge, Arjun (Author) / Scotch, Matthew (Thesis advisor) / Gonzalez-Hernandez, Graciela (Thesis advisor) / Greenes, Robert (Committee member) / Arizona State University (Publisher)
Created2019