Use Case Oriented Medical Visual Information Retrieval & System Evaluation

Large amounts of medical visual data are produced daily in hospitals, while new imaging techniques continue to emerge. In addition, many images are made available continuously via publications in the scientific literature and can also be valuable for clinical routine, research and education. Information retrieval systems are useful tools to provide access to the biomedical literature and fulfil the information needs of medical professionals. The tools developed in this thesis can potentially help clinicians make decisions about difficult diagnoses via a case-based retrieval system based on a use case associated with a specific evaluation task. This system retrieves articles from the biomedical literature when querying with a case description and attached images. This thesis proposes a multimodal approach for medical case-based retrieval with focus on the integration of visual information connected to text. Furthermore, the ImageCLEFmed evaluation campaign was organised during this thesis promoting medical retrieval system evaluation

Managing UIC Medical Center Policies Using DSpace

The University of Illinois at Chicago’s University Archives found that using the DSpace institutional repository software is an effective, if not elegant, solution for the submission, search, and retrieval of a set of vital university records. This case study discusses the process of using the institutional repository to manage the University of Illinois Medical Center’s electronic policies and procedures documents

Multimodal medical case retrieval using the Dezert-Smarandache theory.

International audienceMost medical images are now digitized and stored with semantic information, leading to medical case databases. They may be used for aid to diagnosis, by retrieving similar cases to those in examination. But the information are often incomplete, uncertain and sometimes conflicting, so difficult to use. In this paper, we present a Case Based Reasoning (CBR) system for medical case retrieval, derived from the Dezert-Smarandache theory, which is well suited to handle those problems. We introduce a case retrieval specific frame of discernment theta, which associates each element of theta with a case in the database; we take advantage of the flexibility offered by the DSmT's hybrid models to finely model the database. The system is designed so that heterogeneous sources of information can be integrated in the system: in particular images, indexed by their digital content, and symbolic information. The method is evaluated on two classified databases: one for diabetic retinopathy follow-up (DRD) and one for screening mammography (DDSM). On these databases, results are promising: the retrieval precision at five reaches 81.8% on DRD and 84.8% on DDSM

Shangri-La: a medical case-based retrieval tool

Large amounts of medical visual data are produced in hospitals daily and made available continuously via publications in the scientific literature, representing the medical knowledge. However, it is not always easy to find the desired information and in clinical routine the time to fulfil an information need is often very limited. Information retrieval systems are a useful tool to provide access to these documents/images in the biomedical literature related to information needs of medical professionals. Shangri–La is a medical retrieval system that can potentially help clinicians to make decisions on difficult cases. It retrieves articles from the biomedical literature when querying a case description and attached images. The system is based on a multimodal retrieval approach with a focus on the integration of visual information connected to text. The approach includes a query–adaptive multimodal fusion criterion that analyses if visual features are suitable to be fused with text for the retrieval. Furthermore, image modality information is integrated in the retrieval step. The approach is evaluated using the ImageCLEFmed 2013 medical retrieval benchmark and can thus be compared to other approaches. Results show that the final approach outperforms the best multimodal approach submitted to ImageCLEFmed 2013

Medical Case Retrieval

ABSTRACT The proposed PhD project addresses the problem of finding descriptions of diseases or patients' health records that are relevant for a given description of patient's symptoms, also known as medical case retrieval (MCR). Designing an automatic multimodal MCR system applicable to general medical data sets still presents an open research problem, as indicated by the ImageCLEF 2013 MCR challenge, where the best submitted runs achieved only moderate retrieval performance and used purely textual techniques. This project therefore aims at designing a multimodal MCR model that is capable of achieving a substantially better retrieval performance on the ImageCLEF data set than state-of-the-art techniques. Moreover, the potential of further improvement by leveraging relevance feedback of medical expert users for long-term learning will be investigated

Impact of a physician – critical care practitioner pre‐hospital service in Wales on trauma survival: a retrospective analysis of linked registry data

The Emergency Medical Retrieval and Transfer Service for Wales launched in 2015. This service delivers senior pre-hospital doctors and advanced critical care practitioners to the scene of time-critical life- and limb-threatening incidents to provide advanced decision-making and pre-hospital clinical care. The impact of the service on 30-day mortality was evaluated retrospectively using a data linkage system. The study included patients who sustained moderate-to-severe blunt traumatic injuries (injury severity score ≥ 9) between 27 April 2015 and 30 November 2018. The association between pre-hospital management by the Emergency Medical Retrieval and Transfer Service and 30-day mortality was assessed using multivariable logistic regression. In total, data from 4035 patients were analysed, of which 412 (10%) were treated by the Emergency Medical Retrieval and Transfer Service. A greater proportion of patients treated by the Emergency Medical Retrieval and Transfer Service had an injury severity score ≥ 16 and Glasgow coma scale ≤ 12 (288 (70%) vs. 1435 (40%) and 126 (31%) vs. 325 (9%), respectively). The unadjusted 30-day mortality rate was 11.7% for patients managed by the Emergency Medical Retrieval and Transfer Service compared with 9.6% for patients managed by standard pre-hospital care services. However, after adjustment for differences in case-mix, the 30-day mortality rate for patients treated by the Emergency Medical Retrieval and Transfer Service was 37% lower (adjusted odds ratio 0.63 (95%CI 0.41–0.97); p = 0.037). The introduction of an emergency medical retrieval service was associated with a reduction in 30-day mortality for patients with blunt traumatic injury

Optimal search strategies for identifying sound clinical prediction studies in EMBASE

BACKGROUND: Clinical prediction guides assist clinicians by pointing to specific elements of the patient's clinical presentation that should be considered when forming a diagnosis, prognosis or judgment regarding treatment outcome. The numbers of validated clinical prediction guides are growing in the medical literature, but their retrieval from large biomedical databases remains problematic and this presents a barrier to their uptake in medical practice. We undertook the systematic development of search strategies ("hedges") for retrieval of empirically tested clinical prediction guides from EMBASE. METHODS: An analytic survey was conducted, testing the retrieval performance of search strategies run in EMBASE against the gold standard of hand searching, using a sample of all 27,769 articles identified in 55 journals for the 2000 publishing year. All articles were categorized as original studies, review articles, general papers, or case reports. The original and review articles were then tagged as 'pass' or 'fail' for methodologic rigor in the areas of clinical prediction guides and other clinical topics. Search terms that depicted clinical prediction guides were selected from a pool of index terms and text words gathered in house and through request to clinicians, librarians and professional searchers. A total of 36,232 search strategies composed of single and multiple term phrases were trialed for retrieval of clinical prediction studies. The sensitivity, specificity, precision, and accuracy of search strategies were calculated to identify which were the best. RESULTS: 163 clinical prediction studies were identified, of which 69 (42.3%) passed criteria for scientific merit. A 3-term strategy optimized sensitivity at 91.3% and specificity at 90.2%. Higher sensitivity (97.1%) was reached with a different 3-term strategy, but with a 16% drop in specificity. The best measure of specificity (98.8%) was found in a 2-term strategy, but with a considerable fall in sensitivity to 60.9%. All single term strategies performed less well than 2- and 3-term strategies. CONCLUSION: The retrieval of sound clinical prediction studies from EMBASE is supported by several search strategies

Automatic annotation of X-ray images: a study on attribute selection

Advances in the medical imaging technology has lead to an exponential growth in the number of digital images that need to be acquired, analyzed, classified, stored and retrieved in medical centers. As a result, medical image classification and retrieval has recently gained high interest in the scientific community. Despite several attempts, the proposed solutions are still far from being sufficiently accurate for real-life implementations. In a previous work, performance of different feature types were investigated in a SVM-based learning framework for classification. of X-Ray images into classes corresponding to body parts and local binary patterns were observed to outperform others. In this paper, we extend that work by exploring the effect of attribute selection on the classification performance. Our experiments show that principal component analysis based attribute selection manifests prediction values that are comparable to the baseline (all-features case) with considerably smaller subsets of original features, inducing lower processing times and reduced storage space