Browsing by Author "Gichoya, Judy"

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    An Evaluation of the Rates of Repeat Notifiable Disease Reporting and Patient Crossover Using a Health Information Exchange-based Automated Electronic Laboratory Reporting System
    (American Medical Informatics Association, 2012) Gichoya, Judy; Gamache, Roland E.; Vreeman, Daniel J.; Dixon, Brian E.; Finnell, John T.; Grannis, Shaun; Family Medicine, School of Medicine
    Patients move across healthcare organizations and utilize services with great frequency and variety. This fact impacts both health information technology policy and patient care. To understand the challenges faced when developing strategies for effective health information exchange, it is important to understand patterns of patient movement and utilization for many healthcare contexts, including managing public-health notifiable conditions. We studied over 10 years of public-health notifiable diseases using the nation's most comprehensive operational automatic electronic laboratory reporting system to characterize patient utilization patterns. Our cohort included 412,699 patients and 833,710 reportable cases. 11.3% of patients had multiple notifiable case reports, and 19.5% had notifiable disease data distributed across 2 or more institutions. This evidence adds to the growing body of evidence that patient data resides in many organizations and suggests that to fully realize the value of HIT in public health, cross-organizational data sharing must be meaningfully incentivized.
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    Conversion of JPG Image into DICOM Image Format with One Click Tagging
    (Springer, 2017) Oladiran, Olakunle; Gichoya, Judy; Purkayastha, Saptarshi; BioHealth Informatics, School of Informatics and Computing
    DICOM images are the centerpiece of radiological imaging. They contain a lot of metadata information about the patient, procedure, sequence of images, device and location. To modify, annotate or simply anonymize images for distribution, we often need to convert DICOM images to another format like jpeg since there are a number of image manipulation tools available for jpeg images compared to DICOM. As part of a research at our institution to customize radiology images to assess cognitive ability of multiple user groups, we created an open-source tool called Jpg2DicomTags, which is able to extract DICOM metadata tags, convert images to lossless jpg that can be manipulated and subsequently reconvert jpg images to DICOM by adding back the metadata tags. This tool provides a simple, easy to use user-interface for a tedious manual task that providers, researchers and patients might often need to do.
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    Full Training versus Fine Tuning for Radiology Images Concept Detection Task for the ImageCLEF 2019 Challenge
    (2019) Sinha, Priyanshu; Purkayastha, Saptarshi; Gichoya, Judy; BioHealth Informatics, School of Informatics and Computing
    Concept detection from medical images remains a challenging task that limits implementation of clinical ML/AI pipelines because of the scarcity of the highly trained experts to annotate images. There is a need for automated processes that can extract concrete textual information from image data. ImageCLEF 2019 provided us a set of images with labels as UMLS concepts. We participated for the rst time for the concept detection task using transfer learning. Our approach involved an experiment of layerwise ne tuning (full training) versus ne tuning based on previous reported recommendations for training classi cation, detection and segmentation tasks for medical imaging. We ranked number 9 in this year's challenge, with an F1 result of 0.05 after three entries. We had a poor result from performing layerwise tuning (F1 score of 0.014) which is consistent with previous authors who have described the bene t of full training for transfer learning. However when looking at the results by a radiologist, the terms do not make clinical sense and we hypothesize that we can achieve better performance when using medical pretrained image models for example PathNet and utilizing a hierarchical training approach which is the basis of our future work on this dataset.
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    Optimizing Medical Image Classification Models for Edge Devices
    (Springer, 2021-09) Abid, Areeba; Sinha, Priyanshu; Harpale, Aishwarya; Gichoya, Judy; Purkayastha, Saptarshi; BioHealth Informatics, School of Informatics and Computing
    Machine learning algorithms for medical diagnostics often require resource-intensive environments to run, such as expensive cloud servers or high-end GPUs, making these models impractical for use in the field. We investigate the use of model quantization and GPU-acceleration for chest X-ray classification on edge devices. We employ 3 types of quantization (dynamic range, float-16, and full int8) which we tested on models trained on the Chest-XRay14 Dataset. We achieved a 2–4x reduction in model size, offset by small decreases in the mean AUC-ROC score of 0.0%–0.9%. On ARM architectures, integer quantization was shown to improve inference latency by up to 57%. However, we also observe significant increases in latency on x86 processors. GPU acceleration also improved inference latency, but this was outweighed by kernel launch overhead. We show that optimization of diagnostic models has the potential to expand their utility to day-to-day devices used by patients and healthcare workers; however, these improvements are context- and architecture-dependent and should be tested on the relevant devices before deployment in low-resource environments.
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    Toward better public health reporting using existing off the shelf approaches: The value of medical dictionaries in automated cancer detection using plaintext medical data
    (Elsevier, 2017-05) Kasthurirathne, Suranga N.; Dixon, Brian E.; Gichoya, Judy; Xu, Huiping; Xia, Yuni; Mamlin, Burke; Grannis, Shaun J.; Department of Epidemiology, Richard M. Fairbanks School of Public Health
    Objectives Existing approaches to derive decision models from plaintext clinical data frequently depend on medical dictionaries as the sources of potential features. Prior research suggests that decision models developed using non-dictionary based feature sourcing approaches and “off the shelf” tools could predict cancer with performance metrics between 80% and 90%. We sought to compare non-dictionary based models to models built using features derived from medical dictionaries. Materials and methods We evaluated the detection of cancer cases from free text pathology reports using decision models built with combinations of dictionary or non-dictionary based feature sourcing approaches, 4 feature subset sizes, and 5 classification algorithms. Each decision model was evaluated using the following performance metrics: sensitivity, specificity, accuracy, positive predictive value, and area under the receiver operating characteristics (ROC) curve. Results Decision models parameterized using dictionary and non-dictionary feature sourcing approaches produced performance metrics between 70 and 90%. The source of features and feature subset size had no impact on the performance of a decision model. Conclusion Our study suggests there is little value in leveraging medical dictionaries for extracting features for decision model building. Decision models built using features extracted from the plaintext reports themselves achieve comparable results to those built using medical dictionaries. Overall, this suggests that existing “off the shelf” approaches can be leveraged to perform accurate cancer detection using less complex Named Entity Recognition (NER) based feature extraction, automated feature selection and modeling approaches.