340 research outputs found

    A scoping review of natural language processing of radiology reports in breast cancer

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    Various natural language processing (NLP) algorithms have been applied in the literature to analyze radiology reports pertaining to the diagnosis and subsequent care of cancer patients. Applications of this technology include cohort selection for clinical trials, population of large-scale data registries, and quality improvement in radiology workflows including mammography screening. This scoping review is the first to examine such applications in the specific context of breast cancer. Out of 210 identified articles initially, 44 met our inclusion criteria for this review. Extracted data elements included both clinical and technical details of studies that developed or evaluated NLP algorithms applied to free-text radiology reports of breast cancer. Our review illustrates an emphasis on applications in diagnostic and screening processes over treatment or therapeutic applications and describes growth in deep learning and transfer learning approaches in recent years, although rule-based approaches continue to be useful. Furthermore, we observe increased efforts in code and software sharing but not with data sharing

    Natural Language Processing – Finding the Missing Link for Oncologic Data, 2022

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    Oncology like most medical specialties, is undergoing a data revolution at the center of which lie vast and growing amounts of clinical data in unstructured, semi-structured and structed formats. Artificial intelligence approaches are widely employed in research endeavors in an attempt to harness electronic medical records data to advance patient outcomes. The use of clinical oncologic data, although collected on large scale, particularly with the increased implementation of electronic medical records, remains limited due to missing, incorrect or manually entered data in registries and the lack of resource allocation to data curation in real world settings. Natural Language Processing (NLP) may provide an avenue to extract data from electronic medical records and as a result has grown considerably in medicine to be employed for documentation, outcome analysis, phenotyping and clinical trial eligibility. Barriers to NLP persist with inability to aggregate findings across studies due to use of different methods and significant heterogeneity at all levels with important parameters such as patient comorbidities and performance status lacking implementation in AI approaches. The goal of this review is to provide an updated overview of natural language processing (NLP) and the current state of its application in oncology for clinicians and researchers that wish to implement NLP to augment registries and/or advance research projects

    A systematic review of natural language processing applied to radiology reports

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    NLP has a significant role in advancing healthcare and has been found to be key in extracting structured information from radiology reports. Understanding recent developments in NLP application to radiology is of significance but recent reviews on this are limited. This study systematically assesses recent literature in NLP applied to radiology reports. Our automated literature search yields 4,799 results using automated filtering, metadata enriching steps and citation search combined with manual review. Our analysis is based on 21 variables including radiology characteristics, NLP methodology, performance, study, and clinical application characteristics. We present a comprehensive analysis of the 164 publications retrieved with each categorised into one of 6 clinical application categories. Deep learning use increases but conventional machine learning approaches are still prevalent. Deep learning remains challenged when data is scarce and there is little evidence of adoption into clinical practice. Despite 17% of studies reporting greater than 0.85 F1 scores, it is hard to comparatively evaluate these approaches given that most of them use different datasets. Only 14 studies made their data and 15 their code available with 10 externally validating results. Automated understanding of clinical narratives of the radiology reports has the potential to enhance the healthcare process but reproducibility and explainability of models are important if the domain is to move applications into clinical use. More could be done to share code enabling validation of methods on different institutional data and to reduce heterogeneity in reporting of study properties allowing inter-study comparisons. Our results have significance for researchers providing a systematic synthesis of existing work to build on, identify gaps, opportunities for collaboration and avoid duplication

    Cost-effectiveness of innovations in pathology services in relation to cancer diagnosis and treatment management

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    Pathology plays an important role in cancer diagnosis and treatment management, results from the pathology lab guide clinicians’ diagnosis and inform patient care plans. Pathology digitisation is expected to maximise lab efficiency when handling tissue specimens, enhance speed, provide novel information to be used by clinicians when making treatment decisions and potentially improve test accuracy. Early cancer diagnosis and personalised treatment are key players in enhancing patients’ clinical outcomes and improving quality of life. Whilst research has shown digitisation of pathology labs to be an effective intervention for better management and reporting on tissue specimens, no evaluation has reported on the economic implications of the adoption of digital systems in an NHS with limited resources. Breast cancers are the most common cancer type in the UK so any advances in accuracy or time to diagnosis due to digital pathology are expected to have a large impact on this group of patients. This thesis investigates the cost-effectiveness of digital pathology through its impacts on breast cancer patients. A discrete event simulation model representing the breast cancer pathway was constructed and used to analyse the impacts of digitisation. There was evidence of both time and cost savings for breast cancer patients as a result of pathology digitisation. A systematic review and meta-analysis compared the diagnostic accuracy of the HER2 biomarker pre- and post- the introduction of digital pathology. There was evidence of reporting precision but not of improved accuracy. Finally, a cost-effectiveness analysis comparing the two approaches found digital pathology not to be cost-effective when compared to conventional microscopes for scoring the HER2 biomarker

    COMPUTATIONAL PHENOTYPING AND DRUG REPURPOSING FROM ELECTRONIC MEDICAL RECORDS

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    Using electronic medical records (EMR) for research involves selecting cohorts and manipulating data for tasks like predictive analysis. Computational phenotyping for cohort characterization and stratification is becoming increasingly important for researchers to produce clinically relevant findings. There are significant amounts of time and effort devoted to manual chart abstraction by subject matter experts and researchers, which creates a large bottleneck for progress in clinical research. I focus on developing computational phenotyping pipelines, and I also focus on using EMR for drug repurposing in breast cancer. Drug repurposing is defined as the process of applying known drugs that are already on the market to new disease indications. Using EMR data for drug repurposing has the unique advantage of being able to observe a patient cohort over time and see drug effects on outcomes. In this dissertation, I present work on computational phenotyping and EMR-based drug repurposing. First, I use embedding models and foundational natural language processing methods to predict oral cancer risk with pathology notes. Second, I use natural language processing methods and transfer learning for breast cancer cohort selection and information extraction. Third, I present a pipeline for producing drug repurposing candidates from EMR and provide supporting evidence for predictions with biomedical literature and existing clinical trials.Doctor of Philosoph

    The application of process mining to care pathway analysis in the NHS

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    Background: Prostate cancer is the most common cancer in men in the UK and the sixth-fastest increasing cancer in males. Within England survival rates are improving, however, these are comparatively poorer than other countries. Currently, information available on outcomes of care is scant and there is an urgent need for techniques to improve healthcare systems and processes. Aims: To provide prostate cancer pathway analysis, by applying concepts of process mining and visualisation and comparing the performance metrics against the standard pathway laid out by national guidelines. Methods: A systematic review was conducted to see how process mining has been used in healthcare. Appropriate datasets for prostate cancer were identified within Imperial College Healthcare NHS Trust London. A process model was constructed by linking and transforming cohort data from six distinct database sources. The cohort dataset was filtered to include patients who had a PSA from 2010-2015, and validated by comparing the medical patient records against a Case-note audit. Process mining techniques were applied to the data to analyse performance and conformance of the prostate cancer pathway metrics to national guideline metrics. These techniques were evaluated with stakeholders to ascertain its impact on user experience. Results: Case note audit revealed 90% match against patients found in medical records. Application of process mining techniques showed massive heterogeneity as compared to the homogenous path laid out by national guidelines. This also gave insight into bottlenecks and deviations in the pathway. Evaluation with stakeholders showed that the visualisation and technology was well accepted, high quality and recommended to be used in healthcare decision making. Conclusion: Process mining is a promising technique used to give insight into complex and flexible healthcare processes. It can map the patient journey at a local level and audit it against explicit standards of good clinical practice, which will enable us to intervene at the individual and system level to improve care.Open Acces

    Artificial intelligence applications in disease diagnosis and treatment: recent progress and outlook

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    The use of computers and other technologies to replicate human-like intelligent behaviour and critical thinking is known as artificial intelligence (AI).The development of AI-assisted applications and big data research has accelerated as a result of the rapid advancements in computing power, sensor technology, and platform accessibility that have accompanied advances in artificial intelligence. AI models and algorithms for planning and diagnosing endodontic procedures. The search engine evaluated information on artificial intelligence (AI) and its function in the field of endodontics, and it also incorporated databases like Google Scholar, PubMed, and Science Direct with the search criterion of original research articles published in English. Online appointment scheduling, online check-in at medical facilities, digitization of medical records, reminder calls for follow-up appointments and immunisation dates for children and pregnant women, as well as drug dosage algorithms and adverse effect warnings when prescribing multidrug combinations, are just a few of the tasks that already use artificial intelligence. Data from the review supported the conclusion that AI can play a significant role in endodontics, including the identification of apical lesions, classification and numbering of teeth, detection of dental caries, periodontitis, and periapical disease, diagnosis of various dental problems, aiding dentists in making referrals, and helping them develop more precise treatment plans for dental disorders. Although artificial intelligence (AI) has the potential to drastically alter how medicine is practised in ways that were previously unthinkable, many of its practical applications are still in their infancy and need additional research and development. Over the past ten years, artificial intelligence in ophthalmology has grown significantly and will continue to do so as imaging techniques and data processing algorithms improve
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