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Literature-related discovery and innovation: Chronic kidney disease

Authors
Journal
Technological Forecasting and Social Change
0040-1625
Publisher
Elsevier
Identifiers
DOI: 10.1016/j.techfore.2014.09.013
Keywords
  • Text Mining
  • Literature-Related Discovery
  • Information Technology
  • Chronic Kidney Disease
  • Chronic Renal Insufficiency
  • Chronic Renal Failure
  • Ckd Causes
  • Ckd Treatments
  • Ckd Prevention
Disciplines
  • Biology
  • Computer Science
  • Ecology
  • Geography
  • Medicine
  • Pharmacology
  • Physics

Abstract

Abstract Although different approaches for preventing, reducing, halting, and reversing chronic kidney disease (CKD) have been described in ther medical literature, all related factors have not been identified together. We used an LRDI-based methodology that is potentially applicable to any disease, and is based on the holistic principle that a necessary, but not sufficient, condition for restorative treatment effectiveness is that potential causes must be removed initially or in parallel with treatment. Literature-Related Discovery and Innovation (LRDI) is a text mining approach that integrates discovery generation from disparate literatures with the wealth of knowledge contained in prior scientific publications. LRDI seeks to identify foundational causes that, if eliminated, could potentially reverse chronic and infectious diseases. We implemented LRDI in three steps by: 1) identifying major symptoms of CKD, 2) identifying and removing foundational causes that drive the symptoms identified, then 3) identifying treatment(s) to reduce, halt, or reverse the progression of CKD and eliminate the remaining symptoms and damage caused by CKD (if not irreversible). We presumed that identifying and eliminating all of the foundational causes as comprehensively, thoroughly, and rapidly as possible may potentially achieve the desired medical goals and obviate the need for any pharmacologic treatments in selected patients. There were two major types of advances made in this study: information technology and medical. The major information technology advance was development of a query to identify the full-spectrum of foundational causes for CKD, and substantially upgrading a query used previously to identify the full spectrum of treatments. The major medical advance was identification of over 900 potential CKD direct and indirect foundational causes that encompass discovery and innovation, along with over 900 CKD direct and indirect treatments that encompass discovery and innovation. The foundational causes were comprised of environmental and occupational exposures, biotoxins, iatrogenic, and lifestyle factors. The myriad treatments ranged from foods, food extracts, drugs, biological, biophysical, and lifestyle changes. A limitation of the LRDI method is that the magnitude of these associations cannot be determined. Nonetheless, after prioritizing potentially relevant factors, eliminating as many upstream or foundational causes as possible may provide benefits to patients with CKD beyond the current emphasis on downstream pharmacologic approaches.

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