Literature-related discovery (LRD): Potential treatments for Multiple Sclerosis

• Published in: Technological forecasting & social change Vol. 75; no. 2; pp. 239 - 255
• Main Authors: Kostoff, Ronald N., Briggs, Michael B., Lyons, Terence J.
• Format: Journal Article
• Language: English
• Published: New York Elsevier Inc 01.02.2008; Elsevier Science Ltd
• Subjects: Discovery; Diseases; Disparate disciplines; Information; Information retrieval; Innovation; Innovations; Interdisciplinary; Knowledge; Literature; Literature-based discovery; Medical treatment; Multidisciplinary; Multiple sclerosis; Parkinson's disease; Problem solving; Radical discovery; Research Methodology; Science and technology; Studies; Text mining; Unconnected disciplines; Text mining; Innovation; Multiple sclerosis; Literature-based discovery; Disparate disciplines; Discovery; Unconnected disciplines; Information retrieval; Interdisciplinary; Radical discovery; Multidisciplinary; Science and technology
• ISSN: 0040-1625; 1873-5509
• DOI: 10.1016/j.techfore.2007.11.002

Literature-related discovery (LRD) is the linking of two or more literature concepts that have heretofore not been linked (i.e., disjoint), in order to produce novel, interesting, plausible, and intelligible knowledge (i.e., potential discovery). The open discovery systems (ODS) component of LRD starts with a problem to be solved, and generates solutions to that problem through potential discovery. We have been using ODS LRD to identify potential treatments or preventative actions for challenging medical problems, among myriad other applications. The previous three papers in this Special Issue describe the application of ODS LRD to Raynaud's Phenomenon (RP), cataracts, and Parkinson's Disease (PD). Multiple Sclerosis (MS) is a progressive neurodegenerative disorder (typically preceded by periods of remission and relapse), affecting mainly people in their early-mid life. MS is characterized by changes in sensation (hypoesthesia), muscle weakness, abnormal muscle spasms, or difficulty to move; difficulties with coordination and balance (ataxia); problems in speech (Dysarthria) or swallowing (Dysphagia), visual problems (Nystagmus, optic neuritis, or diplopia), fatigue and acute or chronic pain syndromes, bladder and bowel difficulties, cognitive impairment, or emotional symptomatology (mainly depression). We selected the subject of MS because of its global prevalence, and its apparent intractability to all treatments except for palliative remediation mainly through drugs or surgery. Our first goal was to identify non-drug non-surgical treatments that would 1) prevent the occurrence, or 2) reduce the progression rate, or 3) stop the progression, or 4) maybe even reverse the progression, of MS. Our second goal was to demonstrate that we could again solve an ODS problem (using LRD) with no prior knowledge of any results or prior work (unlike the case of the RP problem). As in the ‘cataract’ and PD examples, we used the MeSH taxonomy of MEDLINE to restrict potential discoveries to selected semantic classes, and to identify potential discoveries efficiently. Our third goal was to generate large amounts of potential discovery in more than an order of magnitude less time than required for the RP study. The discovery generation methodology has been developed to the point where ODS LRD problems can be solved with no results or knowledge of any prior work.