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Probabilistic Record Linkage System for Maternal Mortality

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dc.contributor.author Wai, Saw Yu Yu
dc.contributor.author Htike, Thin Thin
dc.date.accessioned 2019-07-22T03:07:34Z
dc.date.available 2019-07-22T03:07:34Z
dc.date.issued 2010-12-16
dc.identifier.uri http://onlineresource.ucsy.edu.mm/handle/123456789/1098
dc.description.abstract Computerized record linkage is increasing used in health information systems. Medical record linkage expresses the concept of collecting health care records into commutative personal file, starting with birth and ending with death. Probabilistic linkage methods produce more accurate, dynamic, and robust matching results than rule-based approaches, particularly when matching patient records that lack unique identifiers. Theoretically, the relative frequency of specific data elements can enhance the probabilistic record linkage method, including minimizing the false-positive or false-negative matches. This system estimates the maternal mortality ratio (MMR) using probabilistic record linkage method. This system uses three information systems on mortality data, live birth data and another hospital data, in year 2009. Finally, the maternal mortality ratio is calculated and information on declared maternal deaths was obtained. From these data, the Mortality Information System is probabilistically linked with the Live Birth Information System and Hospital Information System, with a multiple-step blocking strategy. For paired records, the diagnoses and hospital procedures brought together by the best known criteria for severe maternal morbidity were detailed. A total of 33 maternal deaths are recorded in 2009. The official and adjusted maternal mortality ratio is 9192.2(deaths per 100,000 live births). By correlating with data from mortality and hospital systems the most frequent age for maternal death was between 30 and 34. en_US
dc.language.iso en en_US
dc.publisher Fifth Local Conference on Parallel and Soft Computing en_US
dc.title Probabilistic Record Linkage System for Maternal Mortality en_US
dc.type Article en_US


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