Patient-based Literature Retrieval and Integration: A Use Case for Diabetes and arterial Hypertension

Specialized search engines such as PubMed, MedScape or Cochrane have increased dramatically the visibility of biomedical scientific results. These web-based tools allow physicians to access scientific papers instantly. However, this decisive improvement had not a proportional impact in clinical practice due to the lack of advanced search methods. Even queries highly specified for a concrete pathology frequently retrieve too many information, with publications related to patients treated by the physician beyond the scope of the results examined. In this work we present a new method to improve scientific article search using patient information. Two pathologies have been used within the project to retrieve relevant literature to patient data and to be integrated with other sources. Promising results suggest the suitability of the approach, highlighting publications dealing with patient features and facilitating literature search to physicians

A New Method to Retrieve, Cluster And Annotate Clinical Literature Related To Electronic Health Records

The access to medical literature collections such as PubMed, MedScape or Cochrane has been increased notably in the last years by the web-based tools that provide instant access to the information. However, more sophisticated methodologies are needed to exploit efficiently all that information. The lack of advanced search methods in clinical domain produce that even using well-defined questions for a particular disease, clinicians receive too many results. Since no information analysis is applied afterwards, some relevant results which are not presented in the top of the resultant collection could be ignored by the expert causing an important loose of information. In this work we present a new method to improve scientific article search using patient information for query generation. Using federated search strategy, it is able to simultaneously search in different resources and present a unique relevant literature collection. And applying NLP techniques it presents semantically similar publications together, facilitating the identification of relevant information to clinicians. This method aims to be the foundation of a collaborative environment for sharing clinical knowledge related to patients and scientific publications

Doctor of Philosophy

dissertationThe objective of this work is to examine the efficacy of natural language processing (NLP) in summarizing bibliographic text for multiple purposes. Researchers have noted the accelerating growth of bibliographic databases. Information seekers using traditional information retrieval techniques when searching large bibliographic databases are often overwhelmed by excessive, irrelevant data. Scientists have applied natural language processing technologies to improve retrieval. Text summarization, a natural language processing approach, simplifies bibliographic data while filtering it to address a user's need. Traditional text summarization can necessitate the use of multiple software applications to accommodate diverse processing refinements known as "points-of-view." A new, statistical approach to text summarization can transform this process. Combo, a statistical algorithm comprised of three individual metrics, determines which elements within input data are relevant to a user's specified information need, thus enabling a single software application to summarize text for many points-of-view. In this dissertation, I describe this algorithm, and the research process used in developing and testing it. Four studies comprised the research process. The goal of the first study was to create a conventional schema accommodating a genetic disease etiology point-of-view, and an evaluative reference standard. This was accomplished through simulating the task of secondary genetic database curation. The second study addressed the development iv and initial evaluation of the algorithm, comparing its performance to the conventional schema using the previously established reference standard, again within the task of secondary genetic database curation. The third and fourth studies evaluated the algorithm's performance in accommodating additional points-of-view in a simulated clinical decision support task. The third study explored prevention, while the fourth evaluated performance for prevention and drug treatment, comparing results to a conventional treatment schema's output. Both summarization methods identified data that were salient to their tasks. The conventional genetic disease etiology and treatment schemas located salient information for database curation and decision support, respectively. The Combo algorithm located salient genetic disease etiology, treatment, and prevention data, for the associated tasks. Dynamic text summarization could potentially serve additional purposes, such as consumer health information delivery, systematic review creation, and primary research. This technology may benefit many user groups

Natural Language Processing of Clinical Notes on Chronic Diseases: Systematic Review

Novel approaches that complement and go beyond evidence-based medicine are required in the domain of chronic diseases, given the growing incidence of such conditions on the worldwide population. A promising avenue is the secondary use of electronic health records (EHRs), where patient data are analyzed to conduct clinical and translational research. Methods based on machine learning to process EHRs are resulting in improved understanding of patient clinical trajectories and chronic disease risk prediction, creating a unique opportunity to derive previously unknown clinical insights. However, a wealth of clinical histories remains locked behind clinical narratives in free-form text. Consequently, unlocking the full potential of EHR data is contingent on the development of natural language processing (NLP) methods to automatically transform clinical text into structured clinical data that can guide clinical decisions and potentially delay or prevent disease onset

FindZebra:a search engine for rare diseases

BACKGROUND: The web has become a primary information resource about illnesses and treatments for both medical and non-medical users. Standard web search is by far the most common interface to this information. It is therefore of interest to find out how well web search engines work for diagnostic queries and what factors contribute to successes and failures. Among diseases, rare (or orphan) diseases represent an especially challenging and thus interesting class to diagnose as each is rare, diverse in symptoms and usually has scattered resources associated with it. METHODS: We design an evaluation approach for web search engines for rare disease diagnosis which includes 56 real life diagnostic cases, performance measures, information resources and guidelines for customising Google Search to this task. In addition, we introduce FindZebra, a specialized (vertical) rare disease search engine. FindZebra is powered by open source search technology and uses curated freely available online medical information. RESULTS: FindZebra outperforms Google Search in both default set-up and customised to the resources used by FindZebra. We extend FindZebra with specialized functionalities exploiting medical ontological information and UMLS medical concepts to demonstrate different ways of displaying the retrieved results to medical experts. CONCLUSIONS: Our results indicate that a specialized search engine can improve the diagnostic quality without compromising the ease of use of the currently widely popular standard web search. The proposed evaluation approach can be valuable for future development and benchmarking. The FindZebra search engine is available at http://www.findzebra.com/

Evaluation, Validation & Implementation of a Computerized Diagnostic Decision Support System in Primary Practice

Background: Medical diagnosis may be the most complex task attempted by humans. Studies estimate that 95% of diagnoses in outpatient care are accurate, implying that the annual rate of inaccurate diagnoses is 12 million in the US alone, with the potential for patient harm in about half. A well-researched differential might reduce inaccurate diagnoses by offering alternatives matching the patient’s symptoms. This study searched the literature for articles evaluating the diagnostic performance of commercially available computerized diagnostic decision support systems. This search led to selecting Isabel Pro, developed by Isabel Healthcare, Ltd. of Haslemere, UK. Evaluation and Validation: A computerized diagnostic decision support system should respond adequately to four questions: What is the “diagnostic retrieval accuracy”? Does it perform as well as clinicians? When provided with the differential, do clinicians improve diagnostic accuracy? Is it easily incorporated into routine practice? The project validated the diagnostic retrieval accuracy of Isabel Pro using 46 cases with a previously confirmed diagnosis. The confirmed diagnosis appeared in Isabel Pro’s differential in 24 cases (52.2%), outperforming even internal medicine faculty (47%). Using those 24 cases and the differentials produced, the author conducted a diagnostic challenge that involved 120 McGovern Medical School residents. The residents produced 406 diagnoses, of which 105 (25.9%) were correct without the differentials, and 37 were correct post-consultation, a 9.1% absolute improvement. In responses, 75.1% of the participants agreed the differentials would be helpful in routine practice, and 64.1% agreed they would consult the differentials if available. Implementation: The project successfully proposed Isabel Pro as a solution to UT practice leadership on September 16, 2021, and incorporated the system into the Epic EHR as a menu line link on November 30, 2021. This system-wide integration also included a QR code for downloading Isabel Pro to a mobile device. Usage of Isabel Pro in the practices of UTPhysicians began on December 8, 2021. Results: The project concluded data collection after 86 days on March 4, 2022, with usage showing a steady increase in the final three weeks. The project produced 73 unique users (37 faculty and 36 residents). The user survey responses showed 83.3% agreeing they would consult the differential generated by Isabel Pro if available at every patient encounter (+19.2% compared to the challenge survey) and 77.8% agreeing that the suggestions would be helpful in routine practice (+2.7% compared to the challenge survey). More than one-third (36.8%) responded that they changed their diagnosis in response to the differential. Limitations: Only usage statistics were analyzed; the system records no reason for the clinician discontinuing a diagnostic session. Only 20 participants responded out of 73 (27.4%), so even though the respondents represented a spread of experience levels, the results may not represent the total number of potential users. The project covered a limited period of 86 days. Conclusions: Diagnostic inaccuracy is a significant patient safety concern. Studies show that computerized diagnostic decision support systems improve diagnostic accuracy, but they are not wide implementation lags despite these findings. This project demonstrated the feasibility of implementing such a well-known system in academic medical practice. The responses to the surveys demonstrate favorable opinions about the system’s perceived usefulness. Active communication and dissemination programs may be essential to improve and sustain use

Kidney Transplantation

Renal transplantation remains the optimal treatment for end stage renal disease. Compared with dialysis, it is associated with increased patient survival and better quality of life, and is cost effective. Kidney transplantation requires a multi-disciplinary approach in the pre-operative assessment and work-up of donors and recipients, and subsequent post-operative care. The classical surgical procedure for renal transplantation has changed little from the original pelvic operation originally described in 1951, but the surgical complexity however has been magnified by the increasing age of recipients, frequently with other comorbidities, and impetus to utilise kidneys from extended criteria donors, either as single or dual transplants. There have also been tremendous advances in the technical aspects of live-donation. This chapter details the surgical aspects of kidney donation and transplantation, including preparation of the graft, vessel reconstruction, urinary drainage and identification and management of post-donation and transplantation complications. It is hoped the reader is provided with a comprehensive account of the technical aspects of renal transplantation, with a description of variation in procedure based on anatomical aberrations. An overview of current practise with a look to the future is provided

Prev Chronic Dis

IntroductionThe Chronic Care Model (CCM) uses a systematic approach to restructuring medical care to create partnerships between health systems and communities. The objective of this study was to describe how researchers have applied CCM in US primary care settings to provide care for people who have diabetes and to describe outcomes of CCM implementation.MethodsWe conducted a literature review by using the Cochrane database of systematic reviews, CINAHL, and Health Source: Nursing/Academic Edition and the following search terms: \u201cchronic care model\u201d (and) \u201cdiabet*.\u201d We included articles published between January 1999 and October 2011. We summarized details on CCM application and health outcomes for 16 studies.ResultsThe 16 studies included various study designs, including 9 randomized controlled trials, and settings, including academic-affiliated primary care practices and private practices. We found evidence that CCM approaches have been effective in managing diabetes in US primary care settings. Organizational leaders in health care systems initiated system-level reorganizations that improved the coordination of diabetes care. Disease registries and electronic medical records were used to establish patient-centered goals, monitor patient progress, and identify lapses in care. Primary care physicians (PCPs) were trained to deliver evidence-based care, and PCP office\u2013based diabetes self-management education improved patient outcomes. Only 7 studies described strategies for addressing community resources and policies.ConclusionCCM is being used for diabetes care in US primary care settings, and positive outcomes have been reported. Future research on integration of CCM into primary care settings for diabetes management should measure diabetes process indicators, such as self-efficacy for disease management and clinical decision making.2013838

BioDEX: Large-Scale Biomedical Adverse Drug Event Extraction for Real-World Pharmacovigilance

Timely and accurate extraction of Adverse Drug Events (ADE) from biomedical literature is paramount for public safety, but involves slow and costly manual labor. We set out to improve drug safety monitoring (pharmacovigilance, PV) through the use of Natural Language Processing (NLP). We introduce BioDEX, a large-scale resource for Biomedical adverse Drug Event Extraction, rooted in the historical output of drug safety reporting in the U.S. BioDEX consists of 65k abstracts and 19k full-text biomedical papers with 256k associated document-level safety reports created by medical experts. The core features of these reports include the reported weight, age, and biological sex of a patient, a set of drugs taken by the patient, the drug dosages, the reactions experienced, and whether the reaction was life threatening. In this work, we consider the task of predicting the core information of the report given its originating paper. We estimate human performance to be 72.0% F1, whereas our best model achieves 62.3% F1, indicating significant headroom on this task. We also begin to explore ways in which these models could help professional PV reviewers. Our code and data are available: https://github.com/KarelDO/BioDEX.Comment: 28 page