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"Muu" projekt SLTAT16358T
SLTAT16358T "EU48684 4.3 titled “Personalised Health-IT Solutions”" (1.09.2015−31.08.2021); Vastutav täitja: Jaak Vilo; Tartu Ülikool, Loodus- ja täppisteaduste valdkond, arvutiteaduse instituut (partner); Finantseerija: Ettevõtluse Arendamise Sihtasutus (EAS); Eraldatud summa: 0 EUR.
SLTAT16358T
EU48684 4.3 titled “Personalised Health-IT Solutions”
EU48684 4.3 titled “Personalised Health-IT Solutions”
AP 4.3
1.09.2015
31.08.2021
Teadus- ja arendusprojekt
Muu
Tehnoloogia arenduskeskuste (TAK) toetamine
ETIS klassifikaatorAlamvaldkondCERCS klassifikaatorFrascati Manual’i klassifikaatorProtsent
4. Loodusteadused ja tehnika4.6. ArvutiteadusedP170 Arvutiteadus, arvutusmeetodid, süsteemid, juhtimine (automaatjuhtimisteooria)1.1. Matemaatika ja arvutiteadus (matemaatika ja teised sellega seotud teadused: arvutiteadus ja sellega seotud teadused (ainult tarkvaraarendus, riistvara arendus kuulub tehnikavaldkonda)100,0
AsutusRiikTüüp
Ettevõtluse Arendamise Sihtasutus (EAS)
PerioodSumma
01.09.2015−31.08.20210,00 EUR
0,00 EUR

The main impact of the future Health-IT solutions will be ultimately achieved when the IT systems offer a crucial advice in the cure of an individual patient at the critical time of treatment, or at least provide the risk assessments that could be followed up to reduce such personalised risks or increase the schedules of screening. Historically, the expert systems were designed to include the rules formulated by the people and deployed in the decision support. This is, however, complex, imprecise and uncomplete. Moreover, a lot of know-how and intiuition was not possible to formulate as simple rules. In the current data-rich era, the statistical inference from the large data can be achieved. The outcome is either the “rules” derived from the past analyses or (near)realtime analysis of the sufficient cohorts for detecting signals and statistical trends in actual data. Objective in this SP is to facilitate the statistical decision making, collection and management of inference rules and building the main components of the automated decision support system. A large part of the underlying information must be digested electronically from the patient past histories, and the various laboratory measurements, molecular and genetic profiles. In order to deliver the diagnostic or decision support, the respective models should be carefully collected and implemented in such a system with the appropriate technical architectures and the data protection measures. To assess the collection of actionable predictive models, the continuous data analysis cycle needs to be designed that evaluates which recommendations have been provided, which ones were delivered by doctors, and what are the follow-ups of patients, presumed and achieved effect on the population and the respective cost benefit for the health system as well. This information will be used to further the knowledge on treatments efficacy and cost structures. We are collaborating with the Estonian Genome Center of University of Tartu (Estonian Biobank), that currently collects a rich questionnaire and follow up data of the gene donors, as well as the access to their genetic profiles.
The main impact of the future Health-IT solutions will be ultimately achieved when the IT systems offer a crucial advice in the cure of an individual patient at the critical time of treatment, or at least provide the risk assessments that could be followed up to reduce such personalised risks or increase the schedules of screening. Historically, the expert systems were designed to include the rules formulated by the people and deployed in the decision support. This is, however, complex, imprecise and uncomplete. Moreover, a lot of know-how and intiuition was not possible to formulate as simple rules. In the current data-rich era, the statistical inference from the large data can be achieved. The outcome is either the “rules” derived from the past analyses or (near)realtime analysis of the sufficient cohorts for detecting signals and statistical trends in actual data. Objective in this SP is to facilitate the statistical decision making, collection and management of inference rules and building the main components of the automated decision support system. A large part of the underlying information must be digested electronically from the patient past histories, and the various laboratory measurements, molecular and genetic profiles. In order to deliver the diagnostic or decision support, the respective models should be carefully collected and implemented in such a system with the appropriate technical architectures and the data protection measures. To assess the collection of actionable predictive models, the continuous data analysis cycle needs to be designed that evaluates which recommendations have been provided, which ones were delivered by doctors, and what are the follow-ups of patients, presumed and achieved effect on the population and the respective cost benefit for the health system as well. This information will be used to further the knowledge on treatments efficacy and cost structures. We are collaborating with the Estonian Genome Center of University of Tartu (Estonian Biobank), that currently collects a rich questionnaire and follow up data of the gene donors, as well as the access to their genetic profiles.
KirjeldusProtsent
Alusuuring0,0
Katse- ja arendustöö50,0
Rakendusuuring50,0
AsutusRollRiikTüüpKommentaar
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