3 research outputs found

    Quantifying and filtering knowledge generated by literature based discovery

    Get PDF
    Background Literature based discovery (LBD) automatically infers missed connections between concepts in literature. It is often assumed that LBD generates more information than can be reasonably examined. Methods We present a detailed analysis of the quantity of hidden knowledge produced by an LBD system and the effect of various filtering approaches upon this. The investigation of filtering combined with single or multi-step linking term chains is carried out on all articles in PubMed. Results The evaluation is carried out using both replication of existing discoveries, which provides justification for multi-step linking chain knowledge in specific cases, and using timeslicing, which gives a large scale measure of performance. Conclusions While the quantity of hidden knowledge generated by LBD can be vast, we demonstrate that (a) intelligent filtering can greatly reduce the number of hidden knowledge pairs generated, (b) for a specific term, the number of single step connections can be manageable, and (c) in the absence of single step hidden links, considering multiple steps can provide valid links

    Literature Based Discovery (LBD): Towards Hypothesis Generation and Knowledge Discovery in Biomedical Text Mining

    Full text link
    Biomedical knowledge is growing in an astounding pace with a majority of this knowledge is represented as scientific publications. Text mining tools and methods represents automatic approaches for extracting hidden patterns and trends from this semi structured and unstructured data. In Biomedical Text mining, Literature Based Discovery (LBD) is the process of automatically discovering novel associations between medical terms otherwise mentioned in disjoint literature sets. LBD approaches proven to be successfully reducing the discovery time of potential associations that are hidden in the vast amount of scientific literature. The process focuses on creating concept profiles for medical terms such as a disease or symptom and connecting it with a drug and treatment based on the statistical significance of the shared profiles. This knowledge discovery approach introduced in 1989 still remains as a core task in text mining. Currently the ABC principle based two approaches namely open discovery and closed discovery are mostly explored in LBD process. This review starts with general introduction about text mining followed by biomedical text mining and introduces various literature resources such as MEDLINE, UMLS, MESH, and SemMedDB. This is followed by brief introduction of the core ABC principle and its associated two approaches open discovery and closed discovery in LBD process. This review also discusses the deep learning applications in LBD by reviewing the role of transformer models and neural networks based LBD models and its future aspects. Finally, reviews the key biomedical discoveries generated through LBD approaches in biomedicine and conclude with the current limitations and future directions of LBD.Comment: 43 Pages, 5 Figures, 4 Table

    Prevention and Reversal of Peripheral Neuropathy/Peripheral Arterial Disease

    Get PDF
    This monograph presents a five-step treatment protocol to prevent and reverse Peripheral Neuropathy (PN)/Peripheral Arterial Disease (PAD), based on the following systemic medical principle: at the present time, removal of cause is a necessary, but not necessarily sufficient, condition for restorative treatment to be effective. Implementation of the five-step PN/PAD treatment protocol is as follows: Step 1: Obtain a detailed medical and habit/exposure history from the patient. Step 2: Administer written and clinical performance and behavioral tests to assess the severity of the higher-level symptoms and degradation of executive functions Step 3: Administer laboratory tests (blood, urine, imaging, etc) Step 4: Eliminate ongoing PN/PAD contributing factors Step 5: Implement PN/PAD treatments This individually-tailored PN/PAD treatment protocol can be implemented with the data currently available in the biomedical literature. Additionally, while the methodology developed for this study was applied to comprehensive identification of diagnostics, contributing factors, and treatments for PN/PAD, it is general and applicable to any chronic disease/condition that, like PN/PAD, has an associated substantial research literature. Thus, the protocol and methodology developed to prevent or reverse PN/PAD can be used to prevent or reverse any chronic disease (with the possible exceptions of individuals with strong genetic predispositions to the disease in question or who have suffered irreversible damage from the disease)
    corecore