Conceptual graph-based knowledge representation for supporting reasoning in African traditional medicine

Although African patients use both conventional or modern and traditional healthcare simultaneously, it has been proven that 80% of people rely on African traditional medicine (ATM). ATM includes medical activities stemming from practices, customs and traditions which were integral to the distinctive African cultures. It is based mainly on the oral transfer of knowledge, with the risk of losing critical knowledge. Moreover, practices differ according to the regions and the availability of medicinal plants. Therefore, it is necessary to compile tacit, disseminated and complex knowledge from various Tradi-Practitioners (TP) in order to determine interesting patterns for treating a given disease. Knowledge engineering methods for traditional medicine are useful to model suitably complex information needs, formalize knowledge of domain experts and highlight the effective practices for their integration to conventional medicine. The work described in this paper presents an approach which addresses two issues. First it aims at proposing a formal representation model of ATM knowledge and practices to facilitate their sharing and reusing. Then, it aims at providing a visual reasoning mechanism for selecting best available procedures and medicinal plants to treat diseases. The approach is based on the use of the Delphi method for capturing knowledge from various experts which necessitate reaching a consensus. Conceptual graph formalism is used to model ATM knowledge with visual reasoning capabilities and processes. The nested conceptual graphs are used to visually express the semantic meaning of Computational Tree Logic (CTL) constructs that are useful for formal specification of temporal properties of ATM domain knowledge. Our approach presents the advantage of mitigating knowledge loss with conceptual development assistance to improve the quality of ATM care (medical diagnosis and therapeutics), but also patient safety (drug monitoring)

Mining of patient data: towards better treatment strategies for depression

An intelligent system based on data-mining technologies that can be used to assist in the prevention and treatment of depression is described. The system integrates three different kinds of patient data as well as the data describing mental health of therapists and their interaction with the patients. The system allows for the different data to be analysed in a conjoint manner using both traditional data-mining techniques and tree-mining techniques. Interesting patterns can emerge in this way to explain various processes and dynamics involved in the onset, treatment and management of depression, and help practitioners develop better prevention and treatment strategies

Epistemological issues in the theory of Chinese medicine

Traditional Chinese Medicine (TCM) has been criticized for being unscientific because the theory on which it is based involves entities like qi and ’meridians’ that appear ambiguous and because the internal ‘organs’ like the kidney and the spleen are very different from those of modern anatomy and physiology. Even more so, TCM methods of therapy based on the yin-yang principle, the model of the five elements, and the classification of illnesses according to standard constellations of symptoms (TCM “syndromes”) are largely unproven by the protocols of modern evidence-based medicine. This dissertation attempts to reconstruct TCM theory by: (a) providing explanations of TCM entities as abstractions and constructs that relate to observable body functions and illness symptoms and (b) interpreting TCM theory as comprising heuristic models that were constructed from clinical experience to fit empirical observations of illnesses and their treatments with herbal medications and acupuncture. It suggests that scientists should be less concerned with the ontological status of TCM entities and the epistemic credentials of TCM models than with the ability of these concepts and models to guide physicians in therapy. More importantly, it makes the argument that these models are testable using the methods of evidence-based medicine. There are methodological difficulties associated with randomized controlled trials partly because TCM treatments tend to be individualized and syndromes are dynamic in nature; observational trials may be more appropriate in some situations. It is also possible that, for patients who are more culturally attuned to TCM, the placebo effect is strongly at play and may render the real effects of TCM treatments harder to tease out in clinical trials. The dissertation concludes that the main postulates of TCM should be put to rigorous test. The result may be a leaner but more robust theory, with parts that do not stand up to the test being rejected or modified, and a possible acceptance of its more modest therapeutic claims for a limited range of pathological conditions like pain and chronic illnesses

EFFICACY AND MECHANISMS OF MEDICINAL PLANTS AS IMMUNOTHERAPY IN TREATMENT OF ALLERGIC RHINITIS: A SYSTEMATIC REVIEW

Allergic rhinitis is a common disease of immune system that negatively affects general health, quality of life, and social relationships. In the recent years, many studies have been conducted to discover novel treatments for this disease particularly using natural products. Here, we review findings of recent studies that harness medicinal plants and phyto-therapies in oriental medicine that have effectively reduced allergic rhinitis complications. We also assess the use of medicinal plants and their derivatives in oriental medicine to treat allergic rhinitis. In addition, these agents have been reported to be used in combination with each other or separately as complementary therapies and even, in some cases, alternative therapies instead of chemical drugs. These plants display their anti-allergy effects through affecting immunoglobulin and inhibiting different cytokines and interleukins. Medicinal plants and traditional approaches can still offer new therapeutic alternatives to researchers and pharmacists so that these alternatives may further contribute to allergic rhinitis drug discovery

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

Faculty Of Education UNHI

Faculty Of Education UNH

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

Racially-Tailored Medicine Unraveled

In June 2005, the FDA approved BiDil, a heart failure medication that is labeled for use only by African-Americans and thus is the first treatment of its kind. The drug likely portends a future of growing interest in race-based medicine. This phenomenon is emerging at the same time that scientists, in light of the Human Genome Project, are reaching an understanding that race has no biological meaning, and consequently, racially-tailored medicine is both puzzling and troubling. This Article explores the reasons for the new focus on racial-profiling in medicine. It analyzes the risks and dangers of this approach, including medical mistakes, stigmatizations, discrimination, exacerbation of health disparities, and violation of anti-discrimination mandates. The author does not argue against the pursuit of attribute-based therapies, but cautions that the attribute or attributes at issue must be carefully determined and will not be equivalent to what is conventionally thought of as race. The article develops recommendations for safeguards that should be implemented by scientific review boards, IRBs, researchers, health care providers, and journalists involved with attribute-based research and therapeutic practices to ensure that this new approach promotes rather than diminishes public health and welfare