crta
Hrvatska znanstvena Sekcija img
bibliografija
3 gif
 Naslovna
 O projektu
 FAQ
 Kontakt
4 gif
Pregledavanje radova
Jednostavno pretraživanje
Napredno pretraživanje
Skupni podaci
Upis novih radova
Upute
Ispravci prijavljenih radova
Ostale bibliografije
Slični projekti
 Bibliografske baze podataka

Pregled bibliografske jedinice broj: 644007

Rad u postupku objavljivanja

Autori: Ćosić, Krešimir; Popović, Siniša; Kovač, Bernard
Naslov: Big data approach to assessment of soldier stress resilience
Izvornik: 1st International Congress of the International College of Person-Centered Medicine (0000-0000) (2013)
Status rada: prihvaćen
Ključne riječi: big data; stress resilience; soldier
Sažetak:
The concept of new multidimensional and multimodal metrics for assessment of soldier stress resilience is presented, which is based on comprehensive analysis of a few hundred physiological, acoustic, facial and EEG features computed during soldier elicitation with specific multimodal stimulation using mission-relevant affective databases. Augmentation of this dynamic data streams with resilience-relevant biomarkers, like: COMT Val158Met polymorphism, catabolic regulation of synaptic dopamine, immune/inflammatory endocrine and autonomic response, as well as with fMRI/DTI images of key brain circuitry and their connectomics, including a variety of questionnaires like subjective self-reports, personality traits, phenotypes etc. makes the problem of soldier resilience assessment much more complex. Aggregation and cross-correlation longitudinal analysis of these unstructured multidimensional and multimodal datasets along soldier life cycle, including development of soldiers personalized predictive models, leads to real big data problem. Crunching, learning and reasoning on such huge biological, neural, behavioral and environmental personalized big datasets require multidisciplinary and interdisciplinary expert teams. In order to understand complex, nonlinear, and stochastic interactions among soldier’s environment, behavior and biology, we must move from group-based analysis toward individuals personalized big data analysis based on sophisticated machine learning mathematical methods and techniques which can extract valuable scientific and applicable results and knowledge.
Projekt / tema: 036-0000000-2029
Izvorni jezik: ENG
Kategorija: Znanstveni
Znanstvena područja:
Elektrotehnika,Računarstvo,Psihologija
Upisao u CROSBI: Siniša Popović (sinisa.popovic@fer.hr), 27. Ruj. 2013. u 15:04 sati



Verzija za printanje   za tiskati


upomoc
foot_4