[HTML][HTML] Using machine learning for predicting cervical cancer from Swedish electronic health records by mining hierarchical representations
R Weegar, K Sundström - PloS one, 2020 - journals.plos.org
Electronic health records (EHRs) contain rich documentation regarding disease symptoms
and progression, but EHR data is challenging to use for diagnosis prediction due to its high …
and progression, but EHR data is challenging to use for diagnosis prediction due to its high …
Validation of automated sepsis surveillance based on the Sepsis-3 clinical criteria against physician record review in a general hospital population: observational …
JK Valik, L Ward, H Tanushi, K Müllersdorf… - BMJ quality & …, 2020 - qualitysafety.bmj.com
Background Surveillance of sepsis incidence is important for directing resources and
evaluating quality-of-care interventions. The aim was to develop and validate a fully …
evaluating quality-of-care interventions. The aim was to develop and validate a fully …
[HTML][HTML] The economic burden of prostate cancer–a Swedish prevalence-based register study
S Hao, E Östensson, M Eklund, H Grönberg… - BMC health services …, 2020 - Springer
Background Incidence and prevalence of prostate cancer in Sweden have increased
markedly due to prostate-specific antigen (PSA) testing. Moreover, new diagnostic tests and …
markedly due to prostate-specific antigen (PSA) testing. Moreover, new diagnostic tests and …
Blokzincir teknolojisinin sağlık bilgi sistemlerinde kullanımı
Blokzincir, şifrelenmiş bilginin zincir halindeki bloklar içerisinde dağıtık olarak saklanmasını
temel alan bir protokol konseptidir. Bu konsept, işlemleri daha verimli ve takip edilebilir hale …
temel alan bir protokol konseptidir. Bu konsept, işlemleri daha verimli ve takip edilebilir hale …
Exploiting complex medical data with interpretable deep learning for adverse drug event prediction
A variety of deep learning architectures have been developed for the goal of predictive
modelling and knowledge extraction from medical records. Several models have placed …
modelling and knowledge extraction from medical records. Several models have placed …
Automated labeling of terms in medical reports in Serbian
Nowadays, many electronic health reports (EHRs) are stored daily. They consist of the
structured part and of an unstructured section written in natural language. Due to the limited …
structured part and of an unstructured section written in natural language. Due to the limited …
Deep learning from heterogeneous sequences of sparse medical data for early prediction of sepsis
Sepsis is a life-threatening complication to infections, and early treatment is key for survival.
Symptoms of sepsis are difficult to recognize, but prediction models using data from …
Symptoms of sepsis are difficult to recognize, but prediction models using data from …
Mining disproportional frequent arrangements of event intervals for investigating adverse drug events
Adverse drug events are pervasive and costly medical conditions, in which novel research
approaches are needed to investigate the nature of such events further and ultimately …
approaches are needed to investigate the nature of such events further and ultimately …
A Semi-supervised Approach for De-identification of Swedish Clinical Text
H Berg, H Dalianis - … of the Twelfth Language Resources and …, 2020 - aclanthology.org
An abundance of electronic health records (EHR) is produced every day within healthcare.
The records possess valuable information for research and future improvement of …
The records possess valuable information for research and future improvement of …
Detecting adverse drug events from Swedish electronic health records using text mining
M Bampa, H Dalianis - Proceedings of the LREC 2020 Workshop …, 2020 - aclanthology.org
Abstract Electronic Health Records are a valuable source of patient information which can
be leveraged to detect Adverse Drug Events (ADEs) and aid post-mark drug-surveillance …
be leveraged to detect Adverse Drug Events (ADEs) and aid post-mark drug-surveillance …