case studywhat disease is thatfigure lupus third searchwith enough symptoms to determine it must be lupus handling spelling mistakesdamerau-levenshtein say someone typed "lupsuinstead of "lupus spelling mistakes happen all the time and in all types of human-crafted documents to deal with this data scientists often use damerau-levenshtein the damerau-levenshtein distance between two strings is the number of operations required to turn one string into the other four operations are allowed to calculate the distancedeletion--delete character from the string insertion--add character to the string substitution--substitute one character for another without the substitution counted as one operationchanging one character into another would take two operationsone deletion and one insertion transposition of two adjacent characters--swap two adjacent characters this last operation (transpositionis what makes the difference between traditional levenshtein distance and the damerau-levenshtein distance it' this last operation that makes our dyslexic spelling mistake fall within acceptable limits dameraulevenshtein is forgiving of these transposition mistakeswhich makes it great for search enginesbut it' also used for other things such as calculating the differences between dna strings figure shows how the transformation from "lupsuto "lupusis performed with single transposition lupsu lupsu lupus figure adjacent character transposition is one of the operations in damerau-levenshtein distance the other three are insertiondeletionand substitution with just this you've achieved your first objectivediagnosing disease but let' not forget about your secondary project objectivedisease profiling project secondary objectivedisease profiling what you want is list of keywords fitting your selected disease for this you'll use the significant terms aggregation the score calculation to determine which words are significant is once again combination of factorsbut it roughly boils down to comparison