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P. Guleria and M. Sood
Table 4 bagOfNgrams on
input dataset
ans = 5 × 1 tokenizedDocument
5 tokens
chill due fever sore throat
7 tokens
cold symptom running nose
cough sneeze bodyache
headache
6 tokens
fatigue cause due symptom
common cold
6 tokens
bodyache tiredness due common
cold
2 tokens
prolong illness
bag = bagOfNgrams with properties
Counts
Vocabulary
Ngrams NgramLengths
NumNgrams
NumDocuments
[13 × 41 double]
[1 × 37 string]
[41 × 2 string]
2
41
13
Initial topic assignments sampled in 0.111156 s.
Fig. 8 Patient-centric Trigrams
models using a word cloud are visualized in Fig. 11 whereas the common n-grams
of length 3 are shown in Table 5.
The semantic IoT interoperability framework for Patient-centric diagnosis is
shown in Fig. 10. The results obtained in Fig. 9 are implemented in the framework for
diagnosis by doctor. The semantic interoperability understands the patients’ symptoms unambiguously and facilitates the exchange of meaningful information across