Building Data-Driven Pathways From Routinely Collected Hospital Data:A Case Study on Prostate Cancer

Background: Routinely collected data in hospitals is complex, typically heterogeneous, and scattered across multiple Hospital Information Systems (HIS). This big data, created as a byproduct of health care activities, has the potential to provide a better understanding of diseases, unearth hidden patterns, and improve services and cost. The extent and uses of such data rely on its quality, which is not consistently checked, nor fully understood. Nevertheless, using routine data for the construction of data-driven clinical pathways, describing processes and trends, is a key topic receiving increasing attention in the literature. Traditional algorithms do not cope well with unstructured processes or data, and do not produce clinically meaningful visualizations. Supporting systems that provide additional information, context, and quality assurance inspection are needed. Objective: The objective of the study is to explore how routine hospital data can be used to develop data-driven pathways that describe the journeys that patients take through care, and their potential uses in biomedical research; it proposes a framework for the construction, quality assessment, and visualization of patient pathways for clinical studies and decision support using a case study on prostate cancer. Methods: Data pertaining to prostate cancer patients were extracted from a large UK hospital from eight different HIS, validated, and complemented with information from the local cancer registry. Data-driven pathways were built for each of the 1904 patients and an expert knowledge base, containing rules on the prostate cancer biomarker, was used to assess the completeness and utility of the pathways for a specific clinical study. Software components were built to provide meaningful visualizations for the constructed pathways. Results: The proposed framework and pathway formalism enable the summarization, visualization, and querying of complex patient-centric clinical information, as well as the computation of quality indicators and dimensions. A novel graphical representation of the pathways allows the synthesis of such information. Conclusions: Clinical pathways built from routinely collected hospital data can unearth information about patients and diseases that may otherwise be unavailable or overlooked in hospitals. Data-driven clinical pathways allow for heterogeneous data (ie, semistructured and unstructured data) to be collated over a unified data model and for data quality dimensions to be assessed. This work has enabled further research on prostate cancer and its biomarkers, and on the development and application of methods to mine, compare, analyze, and visualize pathways constructed from routine data. This is an important development for the reuse of big data in hospitals

On the Use of XML in Medical Imaging Web-Based Applications

The rapid growth of digital technology in medical fields over recent years has increased the need for applications able to manage patient medical records, imaging data, and chart information. Web-based applications are implemented with the purpose to link digital databases, storage and transmission protocols, management of large volumes of data and security concepts, allowing the possibility to read, analyze, and even diagnose remotely from the medical center where the information was acquired. The objective of this paper is to analyze the use of the Extensible Markup Language (XML) language in web-based applications that aid in diagnosis or treatment of patients, considering how this protocol allows indexing and exchanging the huge amount of information associated with each medical case. The purpose of this paper is to point out the main advantages and drawbacks of the XML technology in order to provide key ideas for future web-based applicationsPeer ReviewedPostprint (author's final draft

Web-application for gathering, analyzing, and processing health information about allergy data

Dissertation submitted in partial fulfillment of the requirements for the Degree of Master of Science in Geospatial Technologies.Geoinformation technologies (GIS) are becoming increasingly popular and can be valuable in a large numbers of disciplines, both in the scientific and the commercial sector. Particularly, GIS technologies have a big potential in the health sector. One of the research challenges in the field of health is how pollen allergy variation depends on the geographic location of the patient. In this work we are motivated to show capabilities of geoinformation technologies in studies of pollen allergy. An analysis of the phenomenon of pollen allergy based on the project Alergologia-2005 and the example of allergy monitoring in the Hospital De Sagunto in Spain, allows to formulate requirements to the technologies used. The client application and server system, integrated in a web service, help scientists explore pollen allergy and attempt to handle it, as well as help patients to be able to control the disease. The designed client interface provides a platform for collecting volunteered geographic information (VGI) from people suffering from allergies and supply them with information about pollen counts. At the same time, the server side of the application provides secure storage of geoinformation and opportunities for its retrieval for analysis, including visualization on a map. Health practitioners can display information from patients and compare it with other characteristics of the place, such as weather conditions. The thesis contributes to the field of geospatial technologies, offering unique solutions for studies of pollen allergy in the province of Castellón, Spain

Deep Lesion Graphs in the Wild: Relationship Learning and Organization of Significant Radiology Image Findings in a Diverse Large-scale Lesion Database

Radiologists in their daily work routinely find and annotate significant abnormalities on a large number of radiology images. Such abnormalities, or lesions, have collected over years and stored in hospitals' picture archiving and communication systems. However, they are basically unsorted and lack semantic annotations like type and location. In this paper, we aim to organize and explore them by learning a deep feature representation for each lesion. A large-scale and comprehensive dataset, DeepLesion, is introduced for this task. DeepLesion contains bounding boxes and size measurements of over 32K lesions. To model their similarity relationship, we leverage multiple supervision information including types, self-supervised location coordinates and sizes. They require little manual annotation effort but describe useful attributes of the lesions. Then, a triplet network is utilized to learn lesion embeddings with a sequential sampling strategy to depict their hierarchical similarity structure. Experiments show promising qualitative and quantitative results on lesion retrieval, clustering, and classification. The learned embeddings can be further employed to build a lesion graph for various clinically useful applications. We propose algorithms for intra-patient lesion matching and missing annotation mining. Experimental results validate their effectiveness.Comment: Accepted by CVPR2018. DeepLesion url adde

Neuroblastoma patient outcomes, tumor differentiation, and ERK activation are correlated with expression levels of the ubiquitin ligase UBE4B.

BackgroundUBE4B is an E3/E4 ubiquitin ligase whose gene is located in chromosome 1p36.22. We analyzed the associations of UBE4B gene and protein expression with neuroblastoma patient outcomes and with tumor prognostic features and histology.MethodsWe evaluated the association of UBE4B gene expression with neuroblastoma patient outcomes using the R2 Platform. We screened neuroblastoma tumor samples for UBE4B protein expression using immunohistochemistry. FISH for UBE4B and 1p36 deletion was performed on tumor samples. We then evaluated UBE4B expression for associations with prognostic factors and with levels of phosphorylated ERK in neuroblastoma tumors and cell lines.ResultsLow UBE4B gene expression is associated with poor outcomes in patients with neuroblastoma and with worse outcomes in all patient subgroups. UBE4B protein expression was associated with neuroblastoma tumor differentiation, and decreased UBE4B protein levels were associated with high-risk features. UBE4B protein levels were also associated with levels of phosphorylated ERK.ConclusionsWe have demonstrated associations between UBE4B gene expression and neuroblastoma patient outcomes and prognostic features. Reduced UBE4B protein expression in neuroblastoma tumors was associated with high-risk features, a lack of differentiation, and with ERK activation. These results suggest UBE4B may contribute to the poor prognosis of neuroblastoma tumors with 1p36 deletions and that UBE4B expression may mediate neuroblastoma differentiation