12 research outputs found
Raskausajan lääkkeiden käyttö ja syntyneiden lasten terveys 1996−2019
Raskaudenaikaisen lääkkeiden käytön vaikutuksista sikiöön tiedetään vähän. Vain muutaman kymmenen lääkkeen tiedetään varmasti aiheuttavan sikiövaurioita. Toisaalta vain harvan lääkkeen tiedetään olevan turvallisia raskausaikana käytettynä. Käyttöön tulee jatkuvasti uusia lääkkeitä, joilla tieto raskaudenaikaisen käytön turvallisuudesta perustuu vain prekliinisiin tutkimuksiin. Tietoa raskaudenaikaisen lääkityksen mahdollisista sikiövaikutuksista kertyy vähitellen tilanteissa, joissa lääkehoito on välttämätöntä, tai kun altistuminen on tapahtunut aikana, jolloin raskaus ei ole vielä tiedossa. Tässä raportissa kuvataan Lääkealan turvallisuus- ja kehittämiskeskus Fimean, Kelan ja Terveyden ja hyvinvoinnin laitoksen (THL) tutkimusyhteistyön keskeiset tulokset raskaudenaikaisesta lääkkeiden käytöstä ja sen vaikutuksista vastasyntyneiden terveyteen ja epämuodostumien kokonaisesiintyvyyteen syntyneillä lapsilla tai raskauden-keskeytyksissä Suomessa vuosina 1996−2019. Raportti perustuu kansallisiin terveysrekistereihin: THL:n ylläpitämien syntyneiden lasten rekisterin, raskaudenkeskeyttämis- ja epämuodostumarekisterien sekä Kelan reseptitiedoston ja lääkekorvausoikeuksien tiedoston tietoihin
From Inception to ConcePTION: Genesis of a Network to Support Better Monitoring and Communication of Medication Safety During Pregnancy and Breastfeeding
In 2019, the Innovative Medicines Initiative (IMI) funded the ConcePTION project—Building an ecosystem for better monitoring and communicating safety of medicines use in pregnancy and breastfeeding: validated and regulatory endorsed workflows for fast, optimised evidence generation—with the vision that there is a societal obligation to rapidly reduce uncertainty about the safety of medication use in pregnancy and breastfeeding. The present paper introduces the set of concepts used to describe the European data sources involved in the ConcePTION project and illustrates the ConcePTION Common Data Model (CDM), which serves as the keystone of the federated ConcePTION network. Based on data availability and content analysis of 21 European data sources, the ConcePTION CDM has been structured with six tables designed to capture data from routine healthcare, three tables for data from public health surveillance activities, three curated tables for derived data on population (e.g., observation time and mother-child linkage), plus four metadata tables. By its first anniversary, the ConcePTION CDM has enabled 13 data sources to run common scripts to contribute to major European projects, demonstrating its capacity to facilitate effective and transparent deployment of distributed analytics, and its potential to address questions about utilization, effectiveness, and safety of medicines in special populations, including during pregnancy and breastfeeding, and, more broadly, in the general population
Differentially private data sharing with deep generative models
Sharing data can lead to scientific discoveries, but it can hurt privacy of the people in the data. In this thesis we use deep generative models Generative adversarial network and Variational autoencoder to generate synthetic data, which could be shared instead of the original data. These models are also modified to satisfy the definition of Differential privacy (DP), which is a mathematically rigorous definition of privacy.
First we give some essential definitions for DP and proofs for some of them. Then we discuss data sharing and potential privacy risks related to it as well as methods for mitigating these risks. Then we introduce deep generative models and their DP-versions used for creating synthetic data and finally we measure the quality of synthetic data using several continuous or categorical valued data sets
Health-Related Costs of Intimate Partner Violence : Using Linked Police and Health Registers
First published online November 18, 202
Distinguishing between indications for use of medications in administrative data sources application to pregabalin and gabapentin A contribution from the ConcePTION project
A first step to study the use and safety of multiple sclerosis (MS) drugs during pregnancy: Identifying cases of MS and assessing MS prevalence. A pilot study in a French health care datasource. A contribution from the ConcePTION project
Identification and prevalence assessment of multiple sclerosis in women: A multicentre database study across Europe: A contribution from the IMI2 ConcePTION project
A first step in drug utilization and safety studies: Identifying indications for drug use in administrative data sources. A pilot study with gabapentinoids in a French healthcare data source. A contribution from the ConcePTION project
From Inception to ConcePTION: Genesis of a Network to Support Better Monitoring and Communication of Medication Safety During Pregnancy and Breastfeeding
In 2019, the Innovative Medicines Initiative (IMI) funded the ConcePTION project-Building an ecosystem for better monitoring and communicating safety of medicines use in pregnancy and breastfeeding: validated and regulatory endorsed workflows for fast, optimised evidence generation-with the vision that there is a societal obligation to rapidly reduce uncertainty about the safety of medication use in pregnancy and breastfeeding. The present paper introduces the set of concepts used to describe the European data sources involved in the ConcePTION project and illustrates the ConcePTION Common Data Model (CDM), which serves as the keystone of the federated ConcePTION network. Based on data availability and content analysis of 21 European data sources, the ConcePTION CDM has been structured with six tables designed to capture data from routine healthcare, three tables for data from public health surveillance activities, three curated tables for derived data on population (e.g., observation time and mother-child linkage), plus four metadata tables. By its first anniversary, the ConcePTION CDM has enabled 13 data sources to run common scripts to contribute to major European projects, demonstrating its capacity to facilitate effective and transparent deployment of distributed analytics, and its potential to address questions about utilization, effectiveness, and safety of medicines in special populations, including during pregnancy and breastfeeding, and, more broadly, in the general population