By José Bravo, Ramón Hervás, Vladimir Villarreal
This e-book constitutes the refereed convention lawsuits of the 1st foreign convention on Ambient Intelligence for overall healthiness, AmIHEALTH 2015, held in Puerto Varas, Chile, in December 2015.
The 20 revised complete papers and nine brief papers have been reviewed and chosen from 32 submissions and canopy subject matters on applied sciences for imposing AmIHealth environments; frameworks comparable with AmIHealth environments; utilized algorithms in e-Health structures; interactions in the AmIHealth environments; functions and case reviews of AmIHealth environments; and metrics for health and wellbeing environments.
Read Online or Download Ambient Intelligence for Health: First International Conference, AmIHEALTH 2015, Puerto Varas, Chile, December 1-4, 2015, Proceedings PDF
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Additional resources for Ambient Intelligence for Health: First International Conference, AmIHEALTH 2015, Puerto Varas, Chile, December 1-4, 2015, Proceedings
In the case that Obesity label was assigned to a record, the reviewer was asked to annotate the record with a label of the second classiﬁcation problem. We also asked the evaluators to provide information about keywords related to obesity, body weight or obesity comorbidities that were not considered in the list of keywords but were present in the records. When all the documents were annotated, we ﬁltered out records that reviewers deemed as possible false positives. g. records mentioning “low molecular weight”).
In terms of weighted average, ACC and F-measure of unigram representation together with SVM, are better than NB. Finally, we observe that bigram representation together with NB performs slightly better than SVM. Extracting Information from Electronic Medical Records 43 Table 3. Classiﬁers’ performance measures for the ﬁrst classiﬁcation problem Class N1 NB Accuracy F-measure FPR UW 97,63 NW 97,69 OW 89,73 O 88,42 Weighted average 89,70 SVM UW 99,32 NW 98,83 OW 90,93 O 89,95 Weighted average 91,21 N2 NB UW 98,82 NW 98,74 OW 92,90 O 92,48 Weighted average 93,31 SVM UW 99,65 NW 99,33 OW 92,95a O 92,19 Weighted average 93,19 a Not statistically better FNR 89,12 51,41 51,64 92,39 87,23 96,93 63,71 46,07 93,64 88,67 1,28 2,03 6,15 20,27 16,47 0,43 0,68 3,01 29,60 23,24 11,25 20,27 46,44 9,02 13,27 2,65 32,70 62,19 4,30 10,48 94,49 67,24 61,07 95,19 91,19a 98,38 78,74 54,75 95,10 91,08 0,42 1,02 2,70 20,78 16,40 0,14 0,38 1,20 28,01 21,79 7,39 16,22 45,63 3,62 8,53 2,08 19,19 58,34 1,87 7,94 From Table 4 we can observe that both unigram and bigram representations together with SVM, perform better than NB in terms of single and weighted ACC and F-measure.
Several studies show that caregivers experience signiﬁcant stress and depression [11, 13, 28]. In the case of informal caregivers, this situation imposes a deteriorating quality of life not only for the caregiver, but also for the patient’s support network . Nevertheless, innovations in mobile healthcare (mHealth) and electronic healthcare (eHealth) are revolutionizing the interaction among patients, caregivers, and the medical team in the modern healthcare system [5, 12, 21, 22]. On one hand, they create new opportunities for individuals to actively monitor themselves and improve their health.