Mоlimо vаs kоristitе оvај idеntifikаtоr zа citirаnjе ili оvај link dо оvе stаvkе:
https://open.uns.ac.rs/handle/123456789/1079
Nаziv: | Machine Learning for Predicting Cognitive Diseases: Methods, Data Sources and Risk Factors | Аutоri: | Bratić, Brankica Kurbalija, Vladimir Ivanović, Mirjana Oder I. Bosnić Z. |
Dаtum izdаvаnjа: | 1-дец-2018 | Čаsоpis: | Journal of Medical Systems | Sažetak: | © 2018, Springer Science+Business Media, LLC, part of Springer Nature. Machine learning and data mining approaches are being successfully applied to different fields of life sciences for the past 20 years. Medicine is one of the most suitable application domains for these techniques since they help model diagnostic information based on causal and/or statistical data and therefore reveal hidden dependencies between symptoms and illnesses. In this paper we give a detailed overview of the recent machine learning research and its applications for predicting cognitive diseases, especially the Alzheimer’s disease, mild cognitive impairment and the Parkinson’s disease. We survey different state-of-the-art methodological approaches, data sources and public data, and provide their comparative analysis. We conclude by identifying the open problems within the field that include an early detection of the cognitive diseases and inclusion of machine learning tools into diagnostic practice and therapy planning. | URI: | https://open.uns.ac.rs/handle/123456789/1079 | ISSN: | 01485598 | DOI: | 10.1007/s10916-018-1071-x |
Nаlаzi sе u kоlеkciјаmа: | PMF Publikacije/Publications |
Prikаzаti cеlоkupаn zаpis stаvki
SCOPUSTM
Nаvоđеnjа
43
prоvеrеnо 10.05.2024.
Prеglеd/i stаnicа
36
Prоtеklа nеdеljа
1
1
Prоtеkli mеsеc
0
0
prоvеrеnо 10.05.2024.
Google ScholarTM
Prоvеritе
Аlt mеtrikа
Stаvkе nа DSpace-u su zаštićеnе аutоrskim prаvimа, sа svim prаvimа zаdržаnim, оsim аkо nije drugačije naznačeno.