step revisiteddata exploration for disease profiling you've once again arrived at data exploration you can adapt the aggregations query and use your new field to give you bigram key concepts related to diabeteslisting significant terms aggregation on "diabeteswith bigrams searchbody="fields":["name"]"query":"filtered"filter"'term'{'name':'diabetes'}"aggregations"diseasekeywords"significant_terms"field"fulltext""size }"diseasebigrams""significant_terms"field"fulltext shingles""size client search(index=indexname,doc_type=doctypebody=searchbodyfrom_ size= your new aggregatecalled diseasebigramsuses the fulltext shingles field to provide few new insights into diabetes these new key terms show upexcessive discharge-- diabetes patient needs to urinate frequently causes polyuria--this indicates the same thingdiabetes causes the patient to urinate frequently deprivation test--this is actually trigram"water deprivation test"but it recognized deprivation test because you have only bigrams it' test to determine whether patient has diabetes excessive thirst--you already found "thirstwith your unigram keyword searchbut technically at that point it could have meant "no thirst there are other interesting bigramsunigramsand probably also trigrams taken as wholethey can be used to analyze text or collection of texts before reading them notice that you achieved the desired results without getting to the modeling stage sometimes there' at least an equal amount of valuable information to be found in data exploration as in data modeling now that you've fully achieved your secondary objectiveyou can move on to step of the data science processpresentation and automation