The Comprehensive Cancer Center

This open access book provides a valuable resource for hospitals, institutions, and health authorities worldwide in their plans to set up and develop comprehensive cancer care centers. The development and implementation of a comprehensive cancer program allows for a systematic approach to evidence-based strategies of prevention, early detection, diagnosis, treatment, and palliation. Comprehensive cancer programs also provide a nexus for the running of clinical trials and implementation of novel cancer therapies with the overall aim of optimizing comprehensive and holistic care of cancer patients and providing them with the best opportunity to improve quality of life and overall survival. This book's self-contained chapter format aims to reinforce the critical importance of comprehensive cancer care centers while providing a practical guide for the essential components needed to achieve them, such as operational considerations, guidelines for best clinical inpatient and outpatient care, and research and quality management structures. Intended to be wide-ranging and applicable at a global level for both high and low income countries, this book is also instructive for regions with limited resources. The Comprehensive Cancer Center: Development, Integration, and Implementation is an essential resource for oncology physicians including hematologists, medical oncologists, radiation oncologists, surgical oncologists, and oncology nurses as well as hospitals, health departments, university authorities, governments and legislators

All-healing weapon: the value of Oplopanax horridus root bark in the treatment of type 2 diabetes

While Indigenous Peoples live in an incredibly diverse geographical array with significant differences in language, culture, and history, there is a shared experience of an increased prevalence of type 2 diabetes and impaired glucose tolerance as compared to the dominant or colonizer populations. Indigenous patients with type 2 diabetes face multiple barriers to disease self-management: poverty, chronic stress, cultural oppression, limited access to healthy food or exercise, inadequate housing and limited resources to pay for medications. Epidemiological models of type 2 diabetes disregard the social determinants that play a prominent role in the disease’s predominance among the world’s Indigenous Peoples, creating a chasm between health care providers and the sick. This division can be reconciled through the recognition of cultural and spiritual connotations in disease management and the incorporation of sacred foods and medicinal plants in diabetes treatment care programs. For millennia, Indigenous Peoples of the Pacific Northwest have administered the inner bark of the stalk and roots of Oplopanax horridus (devil’s-club) to treat illness and disease; including difficult childbirth, skin infections, cancer, lung hemorrhages, tuberculosis, and diabetes. Devil’s-club is mentioned in written records of oral traditions and ethnographies, confirming the presence of this plant as a powerful symbol of medicine. These oral traditions, rooted in the culture for hundreds of years, serve as testimonies that speak to the sacred and medicinal value of this plant. The antidiabetic capability of this prickly shrub has been the object of Western pharmacological inquiry since 1938 when scientists recorded the extract to effect hypoglycemia in rabbits, validating the use of devil’s-club tea to remedy symptoms of diabetes. These findings propelled my independent research in which I gathered and prepared the root bark to be extracted and tested against hyperglycemia in vitro by conducting a series of tests, especially focusing on the extracts’ activity with the digestive enzymes that break down carbohydrates into the simple sugars used by the body for energy. By synthesizing a discussion of Indigenous Knowledge systems, ethnopharmacological inquiry, and biochemical analysis, I will demonstrate that the inner bark of Oplopanax horridus (devil’s-club) contains antidiabetic activity as affirmed by oral testimonies of Pacific Northwest Indigenous Peoples

Your Body, Your Cells? Direct-to-Consumer Marketing of Autologous Stem Cell Therapies in the United States, Japan, and Australia

Stem cell tourism has relocated; patients in search of stem cell therapies to treat nearly any disease can find clinics selling miracle cures without traveling beyond their national borders. Businesses marketing unproven autologous stem cell therapies are now plentiful in highly regulated, wealthy countries, including the United States, Japan, and Australia. Despite regulatory oversight of stem cell therapies and strong support for scientific innovation in these countries, the internet and social media have facilitated the rapid growth and success of a new industry selling unproven treatments directly to consumers. Clinics selling unproven autologous stem cell therapies have succeeded by developing persuasive strategies to appear legitimate and by promoting the perception that therapies using your own cells are inherently safer and give patients more ownership and control over their treatment. Despite recent reforms in these countries, national political tensions have rendered these reforms porous, creating new loopholes that commercial clinics can exploit. As such, the World Health Organization needs to implement an international solution that holds member states accountable to meaningfully protect patients and to ensure that stem cells can deliver on their therapeutic potential

META-REGRESSION: PROGNOSTIC MODELS AS OBJECTIVE PREDICTORS OF MORTALITY AMONG ICU CANCER PATIENTS

Cancer patients admitted to the intensive care unit (ICU) may be experiencing complications of disease or treatment-related effects. While acute complications related to disease and/or its therapeutic management vary in severity, the approach to ICU-centered care is complicated by actual versus perceived risks of poor outcomes. Prognostic models that inform clinical judgment of nurses and physicians may prove helpful in this population. The Acute Physiology and Chronic Health Evaluation II (APACHE II), Simplified Acute Physiology Score II (SAPS II) and Sequential Organ Failure Assessment (SOFA) are ICU-based models predicting 30-day mortality among the general ICU population. Although studies have been published on use of each model, prognostic accuracy for predicting 30-day, all-cause ICU mortality in the cancer population has yielded mixed results. The purpose of this study was to determine which prognostic model demonstrated greatest prognostic accuracy among oncology patients. Framed within a derived Prognostic Framework, a meta-analysis of prospective and retrospective cohort studies using literature searches of CINAHL, Cochrane, PubMed and Web of Science databases spanning 2000 to 2017 timeframe was performed. Meta-regression with a random-effects model was used to summarize area under the receiver-operating characteristic curves (AUCs) to estimate overall predictive accuracy for the APACHE II, SAPS II, and SOFA. After comparing performances, APACHE II demonstrated greatest predictive accuracy

Language modelling for clinical natural language understanding and generation

One of the long-standing objectives of Artificial Intelligence (AI) is to design and develop algorithms for social good including tackling public health challenges. In the era of digitisation, with an unprecedented amount of healthcare data being captured in digital form, the analysis of the healthcare data at scale can lead to better research of diseases, better monitoring patient conditions and more importantly improving patient outcomes. However, many AI-based analytic algorithms rely solely on structured healthcare data such as bedside measurements and test results which only account for 20% of all healthcare data, whereas the remaining 80% of healthcare data is unstructured including textual data such as clinical notes and discharge summaries which is still underexplored. Conventional Natural Language Processing (NLP) algorithms that are designed for clinical applications rely on the shallow matching, templates and non-contextualised word embeddings which lead to limited understanding of contextual semantics. Though recent advances in NLP algorithms have demonstrated promising performance on a variety of NLP tasks in the general domain with contextualised language models, most of these generic NLP algorithms struggle at specific clinical NLP tasks which require biomedical knowledge and reasoning. Besides, there is limited research to study generative NLP algorithms to generate clinical reports and summaries automatically by considering salient clinical information. This thesis aims to design and develop novel NLP algorithms especially clinical-driven contextualised language models to understand textual healthcare data and generate clinical narratives which can potentially support clinicians, medical scientists and patients. The first contribution of this thesis focuses on capturing phenotypic information of patients from clinical notes which is important to profile patient situation and improve patient outcomes. The thesis proposes a novel self-supervised language model, named Phenotypic Intelligence Extraction (PIE), to annotate phenotypes from clinical notes with the detection of contextual synonyms and the enhancement to reason with numerical values. The second contribution is to demonstrate the utility and benefits of using phenotypic features of patients in clinical use cases by predicting patient outcomes in Intensive Care Units (ICU) and identifying patients at risk of specific diseases with better accuracy and model interpretability. The third contribution is to propose generative models to generate clinical narratives to automate and accelerate the process of report writing and summarisation by clinicians. This thesis first proposes a novel summarisation language model named PEGASUS which surpasses or is on par with the state-of-the-art performance on 12 downstream datasets including biomedical literature from PubMed. PEGASUS is further extended to generate medical scientific documents from input tabular data.Open Acces