Search results for "ennustettavuus"
showing 10 items of 15 documents
Associations of parental physical activity trajectories with offspring's physical activity patterns from childhood to middle adulthood : The Young Fi…
2022
We investigated the association of parental physical activity (PA) trajectories with offspring's youth and adult PA. Self-reported PA data were extracted from the Young Finns Study with three follow-ups for parents between 1980 and 1986 and nine follow-ups for their offspring in youth between 1980 and 2011 (aged 9-39 years, n = 2402) and in adulthood in 2018. Accelerometer-derived PA was quantified in 2018-2020 (aged 43-58 years, n = 1134). Data were analyzed using mixture models and conducted in 2022. We identified three trajectories for fathers and mothers (high-stable activity, 20.2%/16.6%; moderate-stable activity, 50.5%/49.6%; and low-stable activity, 29.4%/33.7%) and four for youth ma…
Flexible Data Driven Inventory Management with Interactive Multiobjective Lot Size Optimization
2021
We study data-driven decision support and formalise a path from data to decision making. We focus on lot sizing in inventory management with stochastic demand and propose an interactive multi-objective optimisation approach. We forecast demand with a Bayesian model, which is based on sales data. After identifying relevant objectives relying on the demand model, we formulate an optimisation problem to determine lot sizes for multiple future time periods. Our approach combines different interactive multi-objective optimisation methods for finding the best balance among the objectives. For that, a decision maker with substance knowledge directs the solution process with one’s preference inform…
Predicting ACL Injury Using Machine Learning on Data From an Extensive Screening Test Battery of 880 Female Elite Athletes
2022
Background: Injury risk prediction is an emerging field in which more research is needed to recognize the best practices for accurate injury risk assessment. Important issues related to predictive machine learning need to be considered, for example, to avoid overinterpreting the observed prediction performance. Purpose: To carefully investigate the predictive potential of multiple predictive machine learning methods on a large set of risk factor data for anterior cruciate ligament (ACL) injury; the proposed approach takes into account the effect of chance and random variations in prediction performance. Study Design: Case-control study; Level of evidence, 3. Methods: The authors used 3-dime…
Newborn brain responses measuring feature and change detection and predicting later language development in children with and without familial risk f…
2003
Lukemisen erityisvaikeuksia eli kehityksellistä dysleksiaa on noin 3-10 prosentilla suomalaisista koululaisista. Vaikeudet haittaavat eri tavoin heidän koulusuoriutumistaan ja vaikuttavat näin myös tulevaisuuden urasuunnitelmiin.Tomi Guttorm on väitöskirjatyössään verrannut vastasyntyneiden riskiryhmään kuuluvien ja muiden vauvojen kielellistä prosessointia. Hän toteaa, että jo muutaman päivän iässä aivoista tehtyjen havaintojen perusteella voidaan ennustaa riskivauvojen heikompia kielellisiä taitoja. Tulokset aivovasteiden ennustearvosta ovat tutkijan mielestä rohkaisevia kielellisten pulmien varhaisen tunnistamisen sekä tuki- ja kuntoutustoimien suunnittelun kannalta. Event-related potent…
"Se on kumminki et ku tietäs niinku seurata ja muuta" : kielellisten vaikeuksien ennusmerkit ja varhainen tuki lasten vanhempien kuvaamana
2016
TIIVISTELMÄ Kankkio, Maria. 2016. ”Se on kumminki et ku tietäs niinku seurata ja muuta”. Kielellisten vaikeuksien ennusmerkit ja varhainen tuki lasten vanhempien kuvaamana. Jyväskylän yliopisto. Kasvatustieteen laitos. Erityispedagogiikan yksikkö. Kevät 2016. 103 sivua. Lapsen kielellistä vaikeutta voivat ennakoida vähäinen ääntely, konsonanttien puuttuminen jokeltelusta tai vastavuoroisuuden puute. Lapsen kehitys on kontekstisidonnaista, joten lapsi ja ympäristö vaikuttavat toinen toisiinsa. Geneettisiä riskitekijöitä lapsen kielelliselle kehitykselle ovat esimerkiksi suvussa esiintyvät lukivaikeudet. Myös ympäristöstä johtuvat riskitekijät saattavat vaikuttaa epäsuotuisasti lapsen kehityk…
Reading Difficulties Identification : A Comparison of Neural Networks, Linear, and Mixture Models
2022
Purpose We aim to identify the most accurate model for predicting adolescent (Grade 9) reading difficulties (RD) in reading fluency and reading comprehension using 17 kindergarten-age variables. Three models (neural networks, linear, and mixture) were compared based on their accuracy in predicting RD. We also examined whether the same or a different set of kindergarten-age factors emerge as the strongest predictors of reading fluency and comprehension difficulties across the models. Method RD were identified in a Finnish sample (N ≈ 2,000) based on Grade 9 difficulties in reading fluency and reading comprehension. The predictors assessed in kindergarten included gender, parental factors (e.…
Early prediction of reading : phonological awareness and related language and cognitive skills in children with a familial risk for dyslexia
2007
PsL Anne Puolakanaho selvitti väitöstutkimuksessaan, miten leikki-ikäisten 3 - 5-vuotiaiden lasten kielelliset ja kognitiiviset taidot ovat yhteydessä lukemisen virheettömyyteen ja sujuvuuteen kouluiässä. Puolakanaho tutki myös, missä määrin dysleksiaa eli sitkeää lukemisvaikeutta voidaan ennakoida lapsikohtaisesti, yksilöllisten riskitekijöiden perusteella jo huomattavasti ennen esikouluikää. Aiemmin lukutaitoa ennustavaa tutkimusta on tehty vanhemmilla lapsilla ja lähinnä kirjoitusjärjestelmältään epäsäännöllisessä englannin kielessä. - Myös kirjoitusjärjestelmältään säännönmukaisessa suomen kielessä leikki-ikäisten fonologis-kielelliset taidot kuten kyky tunnistaa puheesta tavuja ja äänt…
Magnetoencephalography Responses to Unpredictable and Predictable Rare Somatosensory Stimuli in Healthy Adult Humans
2021
Mismatch brain responses to unpredicted rare stimuli are suggested to be a neural indicator of prediction error, but this has rarely been studied in the somatosensory modality. Here, we investigated how the brain responds to unpredictable and predictable rare events. Magnetoencephalography responses were measured in adults frequently presented with somatosensory stimuli (FRE) that were occasionally replaced by two consecutively presented rare stimuli [unpredictable rare stimulus (UR) and predictable rare stimulus (PR); p = 0.1 for each]. The FRE and PR were electrical stimulations administered to either the little finger or the forefinger in a counterbalanced manner between the two conditio…
The “Seili-index” For The Prediction of Chlorophyll-α Levels In The Archipelago Sea of The Northern Baltic Sea, Southwest Finland
2021
AbstractTo build a forecasting tool for the state of eutrophication in the Archipelago Sea, we fitted a Generalized Additive Mixed Model (GAMM) to marine environmental monitoring data, which were collected over the years 2011–2019 by an automated profiling buoy at the Seili ODAS-station. The resulting “Seili-index” can be used to predict the chlorophyll-α (chl-a) concentration in the seawater a number of days ahead by using the temperature forecast as a covariate. An array of test predictions with two separate models on the 2019 data set showed that the index is adept at predicting the amount of chl-a especially in the upper water layer. The visualization with 10 days of chl-a level predict…
Comparison of machine learning and logistic regression as predictive models for adverse maternal and neonatal outcomes of preeclampsia: A retrospecti…
2022
IntroductionPreeclampsia, one of the leading causes of maternal and fetal morbidity and mortality, demands accurate predictive models for the lack of effective treatment. Predictive models based on machine learning algorithms demonstrate promising potential, while there is a controversial discussion about whether machine learning methods should be recommended preferably, compared to traditional statistical models.MethodsWe employed both logistic regression and six machine learning methods as binary predictive models for a dataset containing 733 women diagnosed with preeclampsia. Participants were grouped by four different pregnancy outcomes. After the imputation of missing values, statistic…