Open AccessTemporal and Causal Relations on Evidence Theory: an Application on Adverse Drug Reactions
Author(s) -
Luiz Alberto Pereira Afonso Ribeiro,
Ana Cristina Bicharra Garcia,
Paulo Sérgio Faro Santos
Publication year - 2021
Publication title -
proceedings of the ... international florida artificial intelligence research society conference
Language(s) - English
Resource type - Journals
eISSN - 2334-0762
pISSN - 2334-0754
DOI - 10.32473/flairs.v34i1.128546
Subject(s) - causal inference , inference , computer science , attribution , redundancy (engineering) , dempster–shafer theory , sensor fusion , identification (biology) , information fusion , health records , data mining , artificial intelligence , econometrics , psychology , mathematics , health care , biology , operating system , economics , economic growth , social psychology , botany
The use of big data and information fusion in electronichealth records (EHR) allowed the identification of adversedrug reactions(ADR) through the integration of heteroge-neous sources such as clinical notes (CN), medication pre-scriptions, and pathological examinations. This heterogene-ity of data sources entails the need to address redundancy,conflict, and uncertainty caused by the high dimensionalitypresent in EHR. The use of multisensor information fusion(MSIF) presents an ideal scenario to deal with uncertainty,especially when adding resources of the theory of evidence,also called Dempster–Shafer Theory (DST). In that scenariothere is a challenge which is to specify the attribution of be-lief through the mass function, from the datasets, named basicprobability assignment (BPA). The objective of the presentwork is to create a form of BPA generation using analy-sis of data regarding causal and time relationships betweensources, entities and sensors, not only through correlation, butby causal inference.