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Title: A DATA ACQUISITION AND PROCESSING SYSTEM FOR THE STUDY OF PERIPHERAL VASCULAR DYNAMICS.
Creator: CIKIKCI, ISMAIL OGUZ., Florida Atlantic University, Shankar, Ravi
Abstract/Description: In this study, electrical impedance plethysmograph was used to measure the nonlinear elastic properties of the leg arteries. Two methods were used. In method one, a pressure cuff was wrapped around the lower leg and the recordings were made from under the cuff. Also a second set of recordings were made at a site distal to the cuff, to determine the attenuation of blood pressure pulse by the cuff at cuff pressures above diastolic. In method 2 an Inverter was used and recordings were made from...
Show moreIn this study, electrical impedance plethysmograph was used to measure the nonlinear elastic properties of the leg arteries. Two methods were used. In method one, a pressure cuff was wrapped around the lower leg and the recordings were made from under the cuff. Also a second set of recordings were made at a site distal to the cuff, to determine the attenuation of blood pressure pulse by the cuff at cuff pressures above diastolic. In method 2 an Inverter was used and recordings were made from the same segment. Also recordings were made from the upper arm at the heart level to define the blood pressure pulse, that causes the volume change in the leg arteries. A wide range of pressures were used and V-P and compliance curves were calculated with both the methods. In order to improve the accuracy and reduce operator errors, a personal computer based data acquisition and processing system was developed.
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Date Issued: 1986
PURL: http://purl.flvc.org/fcla/dt/14338
Subject Headings: Arteries, Electronic data processing--Medicine, Diagnosis, Noninvasive
Format: Document (PDF)

Title: Risk-evaluation in clinical diagnostic studies: ascertaining statistical bounds via logistic regression of medical informatics data.
Creator: Dupont, Alice Norm., College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description: The efforts addressed in this thesis refer to applying nonlinear risk predictive techniques based on logistic regression to medical diagnostic test data. This study is motivated and pursued to address the following: 1. To extend logistic regression model of biostatistics to medical informatics 2. Computational preemptive and predictive testing to determine the probability of occurrence (p) of an event by fitting a data set to a (logit function) logistic curve: Finding upper and lower bounds...
Show moreThe efforts addressed in this thesis refer to applying nonlinear risk predictive techniques based on logistic regression to medical diagnostic test data. This study is motivated and pursued to address the following: 1. To extend logistic regression model of biostatistics to medical informatics 2. Computational preemptive and predictive testing to determine the probability of occurrence (p) of an event by fitting a data set to a (logit function) logistic curve: Finding upper and lower bounds on p based on stochastical considerations 3. Using the model developed on available (clinical) data to illustrate the bounds-limited performance of the prediction. Relevant analytical methods, computational efforts and simulated results are presented. Using the results compiled, the risk evaluation in medical diagnostics is discussed with real-world examples. Conclusions are enumerated and inferences are made with directions for future studies.
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Date Issued: 2011
PURL: http://purl.flvc.org/FAU/3332187
Subject Headings: Medical informatics, Clinical medicine, Decision making, Data processing, Medical protocols, Medicine, Research, Statistical methods
Format: Document (PDF)

Title: An Algorithm for the Automated Interpretation of Cardiac Auscultation.
Creator: Lieber, Claude, Erdol, Nurgun, Florida Atlantic University, College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description: Cardiac auscultation, an important part of the physical examination, is difficult for many primary care providers. As a result, diagnoses are missed or auscultatory signs misinterpreted. A reliable, automated means of interpreting cardiac auscultation should be of benefit to both the primary care provider and to patients. This paper explores a novel approach to this problem and develops an algorithm that can be expanded to include all the necessary electronics and programming to develop such...
Show moreCardiac auscultation, an important part of the physical examination, is difficult for many primary care providers. As a result, diagnoses are missed or auscultatory signs misinterpreted. A reliable, automated means of interpreting cardiac auscultation should be of benefit to both the primary care provider and to patients. This paper explores a novel approach to this problem and develops an algorithm that can be expanded to include all the necessary electronics and programming to develop such a device. The algorithm is explained and its shortcomings exposed. The potential for further development is also expounded.
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Date Issued: 2016
PURL: http://purl.flvc.org/fau/fd/FA00004609, http://purl.flvc.org/fau/fd/FA00004609
Subject Headings: Phonocardiography., Signal processing., Pattern recognition systems., Imaging systems in medicine., Decision support systems., Medicine--Data processing.
Format: Document (PDF)

Title: Adaptive energy-aware real-time detection models for cardiac atrial fibrillation.
Creator: Bouhenguel, Redjem., College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description: Though several clinical monitoring ways exist and have been applied to detect cardiac atril fibrillation (A-Fib) and other arrhythmia, these medical interventions and the ensuing clinical treatments are after the fact and costly. Current portable healthcare monitoring systems come in the form of Ambulatory Event Monitors. They are small, battery-operated electrocardiograph devices used to record the heart's rhythm and activity. However, they are not energy-aware ; they are not personalized ;...
Show moreThough several clinical monitoring ways exist and have been applied to detect cardiac atril fibrillation (A-Fib) and other arrhythmia, these medical interventions and the ensuing clinical treatments are after the fact and costly. Current portable healthcare monitoring systems come in the form of Ambulatory Event Monitors. They are small, battery-operated electrocardiograph devices used to record the heart's rhythm and activity. However, they are not energy-aware ; they are not personalized ; they require long battery life, and ultimately fall short on delivering real-time continuous detection of arrhythmia and specifically progressive development of cardiac A-Fib. The focus of this dissertation is the design of a class of adaptive and efficient energy-aware real-time detection models for monitoring, early real-time detection and reporting of progressive development of cardiac A-Fib.... The design promises to have a greater positive public health impact from predicting A-Fib and providing a viable approach to meeting the energy needs of current and future real-time monitoring, detecting and reporting required in wearable computing healthcare applications that are constrained by scarce energy resources.
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Date Issued: 2012
PURL: http://purl.flvc.org/FAU/3358332
Subject Headings: Medical informatics, Medicine, Data processing, Imaging systems in medicine, Design and construction, Cardiovascular system, Diseases, Diagnosis, Bioinformatics
Format: Document (PDF)

Title: Empirical beam angle optimization for lung cancer intensity modulated radiation therapy.
Creator: Doozan, Brian, Pella, Silvia, Florida Atlantic University, Charles E. Schmidt College of Science, Department of Physics
Abstract/Description: Empirical methods of beam angle optimization (BAO) are tested against the BAO that is currently employed in Eclipse treatment planning software. Creating an improved BAO can decrease the amount of time a dosimetrist spends on making a treatment plan, improve the treatment quality and enhance the tools an inexperienced dosimetrist can use to develop planning techniques. Using empirical data created by experienced dosimetrists from 69 patients treated for lung cancer, the most frequently used...
Show moreEmpirical methods of beam angle optimization (BAO) are tested against the BAO that is currently employed in Eclipse treatment planning software. Creating an improved BAO can decrease the amount of time a dosimetrist spends on making a treatment plan, improve the treatment quality and enhance the tools an inexperienced dosimetrist can use to develop planning techniques. Using empirical data created by experienced dosimetrists from 69 patients treated for lung cancer, the most frequently used gantry angles were applied to four different regions in each lung to gather an optimal set of fields that could be used to treat future lung cancer patients. This method, given the moniker FAU BAO, is compared in 7 plans created with the Eclipse BAO choosing 5 fields and 9 fields. The results show that the conformality index improved by 30% or 3% when using the 5 and 9 fields. The conformation number was better by 12% from the 5 fields and 9% from the 9 fields. The organs at risk (OAR) were overall more protected to produce fewer nonstochastic effects from the radiation treatment with the FAU BAO.
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Date Issued: 2014
PURL: http://purl.flvc.org/fau/fd/FA00004280, http://purl.flvc.org/fau/fd/FA00004280
Subject Headings: Cancer -- Radiotherapy, Image guided radiation therapy, Lung cancer -- Treatment, Medical physics, Medical radiology -- Data processing, Medicine -- Mathematical models
Format: Document (PDF)

Title: Sparse Modeling Applied to Patient Identification for Safety in Medical Physics Applications.
Creator: Lewkowitz, Stephanie, Kalantzis, Georgios, Florida Atlantic University, Charles E. Schmidt College of Science, Department of Physics
Abstract/Description: Every scheduled treatment at a radiation therapy clinic involves a series of safety protocol to ensure the utmost patient care. Despite safety protocol, on a rare occasion an entirely preventable medical event, an accident, may occur. Delivering a treatment plan to the wrong patient is preventable, yet still is a clinically documented error. This research describes a computational method to identify patients with a novel machine learning technique to combat misadministration.The patient...
Show moreEvery scheduled treatment at a radiation therapy clinic involves a series of safety protocol to ensure the utmost patient care. Despite safety protocol, on a rare occasion an entirely preventable medical event, an accident, may occur. Delivering a treatment plan to the wrong patient is preventable, yet still is a clinically documented error. This research describes a computational method to identify patients with a novel machine learning technique to combat misadministration.The patient identification program stores face and fingerprint data for each patient. New, unlabeled data from those patients are categorized according to the library. The categorization of data by this face-fingerprint detector is accomplished with new machine learning algorithms based on Sparse Modeling that have already begun transforming the foundation of Computer Vision. Previous patient recognition software required special subroutines for faces and di↵erent tailored subroutines for fingerprints. In this research, the same exact model is used for both fingerprints and faces, without any additional subroutines and even without adjusting the two hyperparameters. Sparse modeling is a powerful tool, already shown utility in the areas of super-resolution, denoising, inpainting, demosaicing, and sub-nyquist sampling, i.e. compressed sensing. Sparse Modeling is possible because natural images are inherrently sparse in some bases, due to their inherrant structure. This research chooses datasets of face and fingerprint images to test the patient identification model. The model stores the images of each dataset as a basis (library). One image at a time is removed from the library, and is classified by a sparse code in terms of the remaining library. The Locally Competetive Algorithm, a truly neural inspired Artificial Neural Network, solves the computationally difficult task of finding the sparse code for the test image. The components of the sparse representation vector are summed by `1 pooling, and correct patient identification is consistently achieved 100% over 1000 trials, when either the face data or fingerprint data are implemented as a classification basis. The algorithm gets 100% classification when faces and fingerprints are concatenated into multimodal datasets. This suggests that 100% patient identification will be achievable in the clinal setting.
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Date Issued: 2016
PURL: http://purl.flvc.org/fau/fd/FA00004721, http://purl.flvc.org/fau/fd/FA00004721
Subject Headings: Computer vision in medicine, Diagnostic imaging -- Data processing, Mathematical models, Medical errors -- Prevention, Medical physics, Sampling (Statistics)
Format: Document (PDF)

Title: The Advantages of Collimator Optimization for Intensity Modulated Radiation Therapy.
Creator: Doozan, Brian, Leventouri, Theodora, Florida Atlantic University, Charles E. Schmidt College of Science, Department of Physics
Abstract/Description: The goal of this study was to improve dosimetry for pelvic, lung, head and neck, and other cancers sites with aspherical planning target volumes (PTV) using a new algorithm for collimator optimization for intensity modulated radiation therapy (IMRT) that minimizes the x-jaw gap (CAX) and the area of the jaws (CAA) for each treatment field. A retroactive study on the effects of collimator optimization of 20 patients was performed by comparing metric results for new collimator optimization...
Show moreThe goal of this study was to improve dosimetry for pelvic, lung, head and neck, and other cancers sites with aspherical planning target volumes (PTV) using a new algorithm for collimator optimization for intensity modulated radiation therapy (IMRT) that minimizes the x-jaw gap (CAX) and the area of the jaws (CAA) for each treatment field. A retroactive study on the effects of collimator optimization of 20 patients was performed by comparing metric results for new collimator optimization techniques in Eclipse version 11.0. Keeping all other parameters equal, multiple plans are created using four collimator techniques: CA0, all fields have collimators set to 0°, CAE, using the Eclipse collimator optimization, CAA, minimizing the area of the jaws around the PTV, and CAX, minimizing the x-jaw gap. The minimum area and the minimum x-jaw angles are found by evaluating each field beam’s eye view of the PTV with ImageJ and finding the desired parameters with a custom script. The evaluation of the plans included the monitor units (MU), the maximum dose of the plan, the maximum dose to organs at risk (OAR), the conformity index (CI) and the number of fields that are calculated to split. Compared to the CA0 plans, the monitor units decreased on average by 6% for the CAX method with a p-value of 0.01 from an ANOVA test. The average maximum dose remained within 1.1% difference between all four methods with the lowest given by CAX. The maximum dose to the most at risk organ was best spared by the CAA method, which decreased by 0.62% compared to the CA0. Minimizing the x-jaws significantly reduced the number of split fields from 61 to 37. In every metric tested the CAX optimization produced comparable or superior results compared to the other three techniques. For aspherical PTVs, CAX on average reduced the number of split fields, lowered the maximum dose, minimized the dose to the surrounding OAR, and decreased the monitor units. This is achieved while maintaining the same control of the PTV.
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Date Issued: 2017
PURL: http://purl.flvc.org/fau/fd/FA00004804, http://purl.flvc.org/fau/fd/FA00004804
Subject Headings: Radiation--Dosage., Optical engineering., Medical physics., Image-guided radiation therapy., Cancer--Radiotherapy., Medical radiology--Data processing., Medicine--Mathematical models.
Format: Document (PDF)

Title: A Clinical Decision Support System for the Identification of Potential Hospital Readmission Patients.
Creator: Baechle, Christopher, Agarwal, Ankur, Florida Atlantic University, College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description: Recent federal legislation has incentivized hospitals to focus on quality of patient care. A primary metric of care quality is patient readmissions. Many methods exist to statistically identify patients most likely to require hospital readmission. Correct identification of high-risk patients allows hospitals to intelligently utilize limited resources in mitigating hospital readmissions. However, these methods have seen little practical adoption in the clinical setting. This research attempts...
Show moreRecent federal legislation has incentivized hospitals to focus on quality of patient care. A primary metric of care quality is patient readmissions. Many methods exist to statistically identify patients most likely to require hospital readmission. Correct identification of high-risk patients allows hospitals to intelligently utilize limited resources in mitigating hospital readmissions. However, these methods have seen little practical adoption in the clinical setting. This research attempts to identify the many open research questions that have impeded widespread adoption of predictive hospital readmission systems. Current systems often rely on structured data extracted from health records systems. This data can be expensive and time consuming to extract. Unstructured clinical notes are agnostic to the underlying records system and would decouple the predictive analytics system from the underlying records system. However, additional concerns in clinical natural language processing must be addressed before such a system can be implemented. Current systems often perform poorly using standard statistical measures. Misclassification cost of patient readmissions has yet to be addressed and there currently exists a gap between current readmission system evaluation metrics and those most appropriate in the clinical setting. Additionally, data availability for localized model creation has yet to be addressed by the research community. Large research hospitals may have sufficient data to build models, but many others do not. Simply combining data from many hospitals often results in a model which performs worse than using data from a single hospital. Current systems often produce a binary readmission classification. However, patients are often readmitted for differing reasons than index admission. There exists little research into predicting primary cause of readmission. Furthermore, co-occurring evidence discovery of clinical terms with primary diagnosis has seen only simplistic methods applied. This research addresses these concerns to increase adoption of predictive hospital readmission systems.
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Date Issued: 2017
PURL: http://purl.flvc.org/fau/fd/FA00004880, http://purl.flvc.org/fau/fd/FA00004880
Subject Headings: Health services administration--Management., Medical care--Quality control--Statistical methods., Medical care--Quality control--Data processing., Medical care--Decision making., Evidence-based medicine., Outcome assessment (Medical care)
Format: Document (PDF)

Title: Modeling access control of medical information.
Creator: Sorgente, Tami W., Florida Atlantic University, Fernandez, Eduardo B., College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description: Medical information is very private and sensitive. With the digitization of medical data, it is becoming accessible through distributed systems, including the Internet. Access to all this information and appropriate exchange of data makes the job of health providers more effective, however, the number of people that can potentially access this information increases by orders of magnitude. Private health information is not well protected. We present guidelines for security models for medical...
Show moreMedical information is very private and sensitive. With the digitization of medical data, it is becoming accessible through distributed systems, including the Internet. Access to all this information and appropriate exchange of data makes the job of health providers more effective, however, the number of people that can potentially access this information increases by orders of magnitude. Private health information is not well protected. We present guidelines for security models for medical information systems. First, we model the structure of the medical information in the form of object-oriented patterns. Second, we study models and patterns in use today and compare them to our patterns. Next we define requirements necessary for controlling access, and describe the common policies and restrictions of security models for medical applications. We present some of the medical record access control restrictions directly in a conceptual model of the medical information.
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Date Issued: 2004
PURL: http://purl.flvc.org/fcla/dt/13163
Subject Headings: Medical records--Access control, Privacy, Right of, Freedom of information, Medical records--Data processing, Medicine--Research--Moral and ethical aspects, Confidential communications, Medical ethics, Information storage and retrieval systems--Medical care, Medical informatics, Computer security, Medicine--Computer networks
Format: Document (PDF)

Medical informatics (3)	+ -
Data processing (2)	+ -
Medical physics (2)	+ -
Medicine (2)	+ -
Arteries (1)	+ -

Bioinformatics (1)	+ -
Cancer -- Radiotherapy (1)	+ -
Cancer--Radiotherapy. (1)	+ -
Cardiovascular system (1)	+ -
Clinical medicine (1)	+ -
Computer security (1)	+ -
Computer vision in medicine (1)	+ -
Confidential communications (1)	+ -
Decision making (1)	+ -
Decision support systems. (1)	+ -
Design and construction (1)	+ -
Diagnosis (1)	+ -
Diagnosis, Noninvasive (1)	+ -
Diagnostic imaging -- Data processing (1)	+ -
Diseases (1)	+ -
Electronic data processing--Medicine (1)	+ -
Evidence-based medicine. (1)	+ -
Freedom of information (1)	+ -
Health services administration--Management. (1)	+ -
Image guided radiation therapy (1)	+ -
Image-guided radiation therapy. (1)	+ -
Imaging systems in medicine (1)	+ -
Imaging systems in medicine. (1)	+ -
Information storage and retrieval systems--Medical care (1)	+ -
Lung cancer -- Treatment (1)	+ -

Florida Atlantic University (7)	+ -
College of Engineering and Computer Science (5)	+ -
Department of Computer and Electrical Engineering and Computer Science (5)	+ -
Charles E. Schmidt College of Science (3)	+ -
Department of Physics (3)	+ -

Doozan, Brian (2)	+ -
Agarwal, Ankur (1)	+ -
Baechle, Christopher (1)	+ -
Bouhenguel, Redjem. (1)	+ -
CIKIKCI, ISMAIL OGUZ. (1)	+ -
Dupont, Alice Norm. (1)	+ -
Erdol, Nurgun (1)	+ -
Fernandez, Eduardo B. (1)	+ -
Kalantzis, Georgios (1)	+ -
Leventouri, Theodora (1)	+ -
Lewkowitz, Stephanie (1)	+ -
Lieber, Claude (1)	+ -
Pella, Silvia (1)	+ -
Shankar, Ravi (1)	+ -
Sorgente, Tami W. (1)	+ -

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