Browsing by Subject "prediction"

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  • Norberg, Anna; Abrego Antia, Nerea; Blanchet, F. Guillaume; Adler, Frederick R.; Anderson, Barbara J.; Anttila, Jani; Araújo, Miguel B.; Dallas, Tad Anthony; Dunson, David; Elith, Jane; Foster, Scott; Fox, Richard; Franklin, Janet; Godsoe, William; Guisan, Antoine; O'Hara, Bob; Hill, Nicole A.; Holt, Robert D.; Hui, Francis K.C; Husby, Magne; Kålås, John Atle; Lehikoinen, Aleksi; Luoto, Miska; Mod, Heidi K.; Newell, Graeme; Renner, Ian; Roslin, Tomas Valter; Soininen, Janne; Thuiller, Wilfried; Vanhatalo, Jarno Petteri; Warton, David; White, Matt; Zimmermann, Niklaus E.; Gravel, Dominique; Ovaskainen, Otso Tapio (2019)
    A large array of species distribution model (SDM) approaches has been developed for explaining and predicting the occurrences of individual species or species assemblages. Given the wealth of existing models, it is unclear which models perform best for interpolation or extrapolation of existing data sets, particularly when one is concerned with species assemblages. We compared the predictive performance of 33 variants of 15 widely applied and recently emerged SDMs in the context of multispecies data, including both joint SDMs that model multiple species together, and stacked SDMs that model each species individually combining the predictions afterward. We offer a comprehensive evaluation of these SDM approaches by examining their performance in predicting withheld empirical validation data of different sizes representing five different taxonomic groups, and for prediction tasks related to both interpolation and extrapolation. We measure predictive performance by 12 measures of accuracy, discrimination power, calibration, and precision of predictions, for the biological levels of species occurrence, species richness, and community composition. Our results show large variation among the models in their predictive performance, especially for communities comprising many species that are rare. The results do not reveal any major trade-offs among measures of model performance; the same models performed generally well in terms of accuracy, discrimination, and calibration, and for the biological levels of individual species, species richness, and community composition. In contrast, the models that gave the most precise predictions were not well calibrated, suggesting that poorly performing models can make overconfident predictions. However, none of the models performed well for all prediction tasks. As a general strategy, we therefore propose that researchers fit a small set of models showing complementary performance, and then apply a cross-validation procedure involving separate data to establish which of these models performs best for the goal of the study.
  • Shiri, Rahman; Heliövaara, Markku; Ahola, Kirsi; Kaila-Kangas, Leena; Haukka, Eija; Kausto, Johanna; Saastamoinen, Peppiina; Leino-Arjas, Päivi; Lallukka, Tea (2018)
    Objective This study aimed to develop and validate a risk screening tool using a points system to assess the risk of future disability retirement due to musculoskeletal disorders (MSD). Methods The development population, the Health 2000 Survey, consisted of a nationally representative sample of Finnish employees aged 30-60 years (N=3676), and the validation population, the Helsinki Health Study, consisted of employees of the City of Helsinki aged 40-60 years (N=6391). Both surveys were linked to data on disability retirement awards due to MSD from national register for an 11-year follow-up. Results The discriminative ability of the model with seven predictors was good (Gonen and Heller's K concordance statistic=0.821). We gave points to seven predictors: sex-dependent age, level of education, pain limiting daily activities, multisite musculoskeletal pain, history of arthritis, and surgery for a spinal disorder or carpal tunnel syndrome. A score of >= 3 out of 7 (top 30% of the index) had good sensitivity (83%) and specificity (70%). Individuals at the top 30% of the risk index were at 29 [95% confidence interval (CI) 15-55) times higher risk of disability retirement due to MSD than those at the bottom 40%. Conclusion This easy-to-use screening tool based on self-reported risk factor profiles can help identify individuals at high risk for disability retirement due to MSD.
  • Pöllänen, Petra M.; Härkönen, Taina; Ilonen, Jorma; Toppari, Jorma; Veijola, Riitta; Siljander, Heli; Knip, Mikael (2022)
    Objective To evaluate the role of autoantibodies to N-terminally truncated glutamic acid decarboxylase GAD(65)(96-585) (t-GADA) as a marker for type 1 diabetes (T1D) and to assess the potential human leukocyte antigen (HLA) associations with such autoantibodies. Design In this cross-sectional study combining data from the Finnish Pediatric Diabetes Register, the Type 1 Diabetes Prediction and Prevention study, the DIABIMMUNE study, and the Early Dietary Intervention and Later Signs of Beta-Cell Autoimmunity study, venous blood samples from 760 individuals (53.7% males) were analyzed for t-GADA, autoantibodies to full-length GAD(65) (f-GADA), and islet cell antibodies. Epitope-specific GAD autoantibodies were analyzed from 189 study participants. Results T1D had been diagnosed in 174 (23%) participants. Altogether 631 (83%) individuals tested positive for f-GADA and 451 (59%) for t-GADA at a median age of 9.0 (range 0.2-61.5) years. t-GADA demonstrated higher specificity (46%) and positive predictive value (30%) for T1D than positivity for f-GADA alone (15% and 21%, respectively). Among participants positive for f-GADA, those who tested positive for t-GADA carried more frequently HLA genotypes conferring increased risk for T1D than those who tested negative for t-GADA (77% vs 53%; P < 0.001). Conclusions Autoantibodies to N-terminally truncated GAD improve the screening for T1D compared to f-GADA and may facilitate the selection of participants for clinical trials. HLA class II-mediated antigen presentation of GAD(96-585)-derived or structurally similar peptides might comprise an important pathomechanism in T1D.
  • Kumar, Mukkesh; Ang, Li Ting; Png, Hang; Ng, Maisie; Tan, Karen; Loy, See Ling; Tan, Kok Hian; Chan, Jerry Kok Yen; Godfrey, Keith M.; Chan, Shiao-yng; Chong, Yap Seng; Eriksson, Johan G.; Feng, Mengling; Karnani, Neerja (2022)
    The increasing prevalence of gestational diabetes mellitus (GDM) is contributing to the rising global burden of type 2 diabetes (T2D) and intergenerational cycle of chronic metabolic disorders. Primary lifestyle interventions to manage GDM, including second trimester dietary and exercise guidance, have met with limited success due to late implementation, poor adherence and generic guidelines. In this study, we aimed to build a preconception-based GDM predictor to enable early intervention. We also assessed the associations of top predictors with GDM and adverse birth outcomes. Our evolutionary algorithm-based automated machine learning (AutoML) model was implemented with data from 222 Asian multi-ethnic women in a preconception cohort study, Singapore Preconception Study of Long-Term Maternal and Child Outcomes (S-PRESTO). A stacked ensemble model with a gradient boosting classifier and linear support vector machine classifier (stochastic gradient descent training) was derived using genetic programming, achieving an excellent AUC of 0.93 based on four features (glycated hemoglobin A(1c) (HbA(1c)), mean arterial blood pressure, fasting insulin, triglycerides/HDL ratio). The results of multivariate logistic regression model showed that each 1 mmol/mol increase in preconception HbA(1c) was positively associated with increased risks of GDM (p = 0.001, odds ratio (95% CI) 1.34 (1.13-1.60)) and preterm birth (p = 0.011, odds ratio 1.63 (1.12-2.38)). Optimal control of preconception HbA(1c) may aid in preventing GDM and reducing the incidence of preterm birth. Our trained predictor has been deployed as a web application that can be easily employed in GDM intervention programs, prior to conception.
  • Vikkula, Sami (Helsingin yliopisto, 2021)
    Oil spills in aquatic environments are devastating disasters with both biological and economic impacts. Fish populations are among the many subjects of these impacts. In literature, there are numerous assessments of oil spill impacts on fish populations. From all applied research methods, the focus of this thesis is on Bayesian methods. In prior research, several Bayesian models have been developed for assessing oil spill impacts on fish populations. These models, however, have focused on the assessment of impacts from past spills. They have not been used for predicting impacts of possible future oil spills. Furthermore, the models have not utilized data from laboratory studies. Some examples can be found of models assessing economic impacts of oil spills on fish populations however, none of them assess the economic impacts that follow from decreases in biomass. The aim of this thesis is to develop a Bayesian bioeconomic prediction model, which would be able to predict oil spill impacts on Baltic Sea main basin herring population, and the consequential economic impacts on fishermen. The idea is to predict the impacts of several hypothetical oil spill scenarios. As a result of this thesis, a bioeconomic prediction model was developed, which can predict both biological and economic impacts of oil spills on Baltic Sea main basin herring through additional oil induced mortality of herring eggs. The model can be applied to other fish populations in other regions as well. The model utilizes laboratory studies for assessing population level impacts. The model can be used for both assessing risks of the impacts of possible future oil spills, and for decision analysis after a spill has already occurred. Furthermore, the model can be used for assessing unknown aspects of past oil spills. The economic predictions can be used, for example, to estimate the compensations that could possibly be paid to fishermen. In the future, the prediction model should be developed further, especially regarding its stock-recruitment relationship assumptions. In addition, the model’s assumptions regarding the calculation of oil induced additional mortality and the economic impacts, should be expanded.
  • Pöllänen, Petra M.; Ryhänen, Samppa J.; Toppari, Jorma; Ilonen, Jorma; Vähäsalo, Paula; Veijola, Riitta; Siljander, Heli; Knip, Mikael (2020)
    Context: We set out to characterize the dynamics of islet autoantibodies over the first 15 years of life in children carrying genetic susceptibility to type 1 diabetes (T1D). We also assessed systematically the role of zinc transporter 8 autoantibodies (ZnT8A) in this context. Design: HLA-predisposed children (N = 1006, 53.0% boys) recruited from the general population during 1994 to 1997 were observed from birth over a median time of 14.9 years (range, 1.9-15.5 years) for ZnT8A, islet cell (ICA), insulin (IAA), glutamate decarboxylase (GADA), and islet antigen-2 (IA-2A) antibodies, and for T1D. Results: By age 15.5 years, 35 (3.5%) children had progressed to T1D. Islet autoimmunity developed in 275 (27.3%) children at a median age of 7.4 years (range, 0.3-15.1 years). The ICA seroconversion rate increased toward puberty, but the biochemically defined autoantibodies peaked at a young age. Before age 2 years, ZnT8A and IAA appeared commonly as the first autoantibody, but in the preschool years IA-2A- and especially GADA-initiated autoimmunity increased. Thereafter, GADA-positive seroconversions continued to appear steadily until ages 10 to 15 years. Inverse IAA seroconversions occurred frequently (49.3% turned negative) and marked a prolonged delay from seroconversion to diagnosis compared to persistent IAA (8.2 vs 3.4 years; P = .01). Conclusions: In HLA-predisposed children, the primary autoantibody is characteristic of age and might reflect the events driving the disease process toward clinical T1D. Autoantibody persistence affects the risk of T1D. These findings provide a framework for identifying disease subpopulations and for personalizing the efforts to predict and prevent T1D.
  • Pitkänen, Timo P.; Sirro, Laura; Häme, Lauri; Häme, Tuomas; Törmä, Markus; Kangas, Annika (ScienceDirect, 2020)
    International Journal of Applied Earth Observation and Geoinformation 86 (2020)
    The majority of the boreal forests in Finland are regularly thinned or clear-cut, and these actions are regulated by the Forest Act. To generate a near-real time tool for monitoring management actions, an automatic change detection modelling chain was developed using Sentinel-2 satellite images. In this paper, we focus mainly on the error evaluation of this automatized workflow to understand and mitigate incorrect change detections. Validation material related to clear-cut, thinned and unchanged areas was collected by visual evaluation of VHR images, which provided a feasible and relatively accurate way of evaluating forest characteristics without a need for prohibitively expensive fieldwork. This validation data was then compared to model predictions classified in similar change categories. The results indicate that clear-cuts can be distinguished very reliably, but thinned stands exhibit more variation. For thinned stands, coverage of broadleaved trees and detections from certain single dates were found to correlate with the success of the modelling results. In our understanding, this relates mainly to image quality regarding haziness and translucent clouds. However, if the growing season is short and cloudiness frequent, there is a clear trade-off between the availability of good-quality images and their preferred annual span. Gaining optimal results therefore depends both on the targeted change types, and the requirements of the mapping frequency.
  • Niemi, Tanja (Helsingin yliopisto, 2018)
    Itsemurha on edelleen tärkeä kuolinsyy Suomessa. Itsemurhariskissä olevan potilaan tunnistaminen ja itsemurhien ennaltaehkäisy on haastavaa. Viime aikoina Implisiittistä assosiaatiotestiä (IAT) on yhä enemmän sovellettu psykologisissa ja psykiatrisissa tutkimuksissa, joissa on saatu näyttöä psykiatristen potilaiden automatisoituneiden mielleyhtymien ja itsetuhokäyttäytymisen yhteydestä. Halusimme tutkimuksellamme selvittää, onko aiemmin itsemurhaa yrittäneillä suomalaisilla masennuspotilailla vahvempi implisiittinen assosiaatio itsensä ja kuoleman/itsemurhan välillä, kuin potilailla, joilla ei ole itsemurhayritystä taustalla. Rekrytoimme tutkimukseen yhteensä 31 masennuspotilasta HYKS Mielialalinjan aluepoliklinikoilta ja vanhuspsykiatrian avohoidosta. Tutkittavista 8 oli aikaisemmin yrittänyt itsemurhaa. IAT:n reaktioaikojen perusteella jokaiselle tutkittavalle laskettiin D-arvo osoitukseksi implisiittisestä assosiaatiosta itsensä ja itsemurhan/kuoleman välillä. Negatiivinen D-arvo osoittaa vahvempaa assosiaatiota itsensä ja elämän välillä, kun taas positiivinen D-arvo osoittaa vahvempaa assosiaatiota itsemurhaan/kuolemaan. Ryhmien välisiä suorituksia verrattiin T-testin avulla. Tuloksissa molempien ryhmien keskimääräinen D-arvo oli negatiivinen. Itsemurhaa yrittäneiden D-arvo oli kuitenkin keskimäärin vähemmän negatiivinen kuin verrokkiryhmän. Ero ei ollut tilastollisesti merkittävä N-määrällä 31. Tutkimuksemme aineisto ei tukenut käsitystä IAT:n hyödyllisyydestä itsemurhariskin arvioinnissa. Lopulliseen päätelmään menetelmän hyödyllisyydestä kliinisessä työssä tarvitaan vielä lisää replikaatiotutkimuksia.
  • Suvisaari, Jaana; Mantere, Outi; Keinänen, Jaakko; Mäntylä, Teemu; Rikandi, Eva; Lindgren, Maija; Kieseppä, Tuula; Raij, Tuukka T. (2018)
    The outcome of first-episode psychosis (FEP) is highly variable, ranging from early sustained recovery to antipsychotic treatment resistance from the onset of illness. For clinicians, a possibility to predict patient outcomes would be highly valuable for the selection of antipsychotic treatment and in tailoring psychosocial treatments and psychoeducation. This selective review summarizes current knowledge of prognostic markers in FEP. We sought potential outcome predictors from clinical and sociodemographic factors, cognition, brain imaging, genetics, and blood-based biomarkers, and we considered different outcomes, like remission, recovery, physical comorbidities, and suicide risk. Based on the review, it is currently possible to predict the future for FEP patients to some extent. Some clinical features-like the longer duration of untreated psychosis (DUP), poor premorbid adjustment, the insidious mode of onset, the greater severity of negative symptoms, comorbid substance use disorders (SUDs), a history of suicide attempts and suicidal ideation and having non-affective psychosis-are associated with a worse outcome. Of the social and demographic factors, male gender, social disadvantage, neighborhood deprivation, dysfunctional family environment, and ethnicity may be relevant. Treatment non-adherence is a substantial risk factor for relapse, but a small minority of patients with acute onset of FEP and early remission may benefit from antipsychotic discontinuation. Cognitive functioning is associated with functional outcomes. Brain imaging currently has limited utility as an outcome predictor, but this may change with methodological advancements. Polygenic risk scores (PRSs) might be useful as one component of a predictive tool, and pharmacogenetic testing is already available and valuable for patients who have problems in treatment response or with side effects. Most blood-based biomarkers need further validation. None of the currently available predictive markers has adequate sensitivity or specificity used alone. However, personalized treatment of FEP will need predictive tools. We discuss some methodologies, such as machine learning (ML), and tools that could lead to the improved prediction and clinical utility of different prognosticmarkers in FEP. Combination of differentmarkers inMLmodels with a user friendly interface, or novel findings from e.g., molecular genetics or neuroimaging, may result in computer-assisted clinical applications in the near future.
  • Poropudas, Jirka (Helsingfors universitet, 2011)
    The Thesis presents a state-space model for a basketball league and a Kalman filter algorithm for the estimation of the state of the league. In the state-space model, each of the basketball teams is associated with a rating that represents its strength compared to the other teams. The ratings are assumed to evolve in time following a stochastic process with independent Gaussian increments. The estimation of the team ratings is based on the observed game scores that are assumed to depend linearly on the true strengths of the teams and independent Gaussian noise. The team ratings are estimated using a recursive Kalman filter algorithm that produces least squares optimal estimates for the team strengths and predictions for the scores of the future games. Additionally, if the Gaussianity assumption holds, the predictions given by the Kalman filter maximize the likelihood of the observed scores. The team ratings allow probabilistic inference about the ranking of the teams and their relative strengths as well as about the teams’ winning probabilities in future games. The predictions about the winners of the games are correct 65-70% of the time. The team ratings explain 16% of the random variation observed in the game scores. Furthermore, the winning probabilities given by the model are concurrent with the observed scores. The state-space model includes four independent parameters that involve the variances of noise terms and the home court advantage observed in the scores. The Thesis presents the estimation of these parameters using the maximum likelihood method as well as using other techniques. The Thesis also gives various example analyses related to the American professional basketball league, i.e., National Basketball Association (NBA), and regular seasons played in year 2005 through 2010. Additionally, the season 2009-2010 is discussed in full detail, including the playoffs.
  • Luoma, Ville (Helsingfors universitet, 2013)
    There develops heartwood in the stems of the Scots pines (Pinus sylvestris L.) that differs by its natural characteristics from the other sections of the wood material in the pine stem. Pine heartwood is natural-ly decay resistant and it can be used in conditions where the normal wood products can’t be used. The aim of this study was to develop a method, which can be used for predicting the diameter and volume of heartwood. There is a need for this kind of method, because it still is not possible to estimate the amount of heartwood in a standing tree without damaging the tree itself. The variables measured from single trees describing the diameter of the heartwood on eight relative heights were analysed by using linear regression. When the best explanatory variables were selected, a mixed linear model was created for each of the relative heights. The mixed linear models could also be used for predicting the diameter of pine heartwood at those relative heights. With the help of the pre-dicted diameters a taper curve could be created for the heartwood. The pine heartwood taper curve describes the tapering of the heartwood as function of the tree height. By integrating the taper curve, it was also possible to predict the total volume of the heartwood in a single tree. The models that used tree diameter at breast height and the length of the tree as explanatory variables were able to explain the variation of heartwood diameter on relative heights between 2,5 % and 70 % with coefficient of determination ranging from 0,84 to 0,95 and also recorded a relative RMSE from 15 % to 35 %. Models for relative heights of 85 % and 95 % were not as good as the others (R2-values 0,65 and 0,06 as well as RMSE-values of 74 % and 444 %). Despite not succeeding on all the relative heights, the most important thing is that the models worked best on that area of the stem where most of the heart-wood is located. The volume predictions for single trees based on the heartwood diameter models rec-orded relative RMSE of 35 % and bias of -5 %. Based on the results of the study it shows that exact prediction of pine heartwood diameter is much easier in the base of the stem than in the top part of it. A great deal of variation could be observed whether there was heartwood or not in the top parts of the stem. The volume of heartwood can already be estimated for single trees, but the amount of heartwood can be predicted also in larger scale, such as forest stands. But to get more accurate results in the future, there is a need for more detailed and com-prehensive research data, which would help to determine the still unknown parts of the behaviour of pine heartwood.
  • Kormi, Immi; Nieminen, Mikko T.; Havulinna, Aki S.; Zeller, Tanja; Blankenberg, Stefan; Tervahartiala, Taina; Sorsa, Timo; Salomaa, Veikko; Pussinen, Pirkko J. (2017)
    Background Extracellular matrix degrading proteases and their regulators play an important role in atherogenesis and subsequent plaque rupture leading to acute cardiovascular manifestations. Design and methods In this prospective cohort study, we investigated the prognostic value of circulating matrix metalloproteinase-8, tissue inhibitor of matrix metalloproteinase-1 concentrations, the ratio of matrix metalloproteinase-8/ tissue inhibitor of matrix metalloproteinase-1 and, for comparison, myeloperoxidase and C-reactive protein concentrations for incident cardiovascular disease endpoints. The population-based FINRISK97 cohort comprised 7928 persons without cardiovascular disease at baseline. The baseline survey included a clinical examination and blood sampling. During a 13-year follow-up the endpoints were ascertained through national healthcare registers. The associations of measured biomarkers with the endpoints, including cardiovascular disease event, coronary artery disease, acute myocardial infarction, stroke and all-cause death, were analysed using Cox regression models. Discrimination and reclassification models were used to evaluate the clinical implications of the biomarkers. Results Serum tissue inhibitor of matrix metalloproteinase-1 and C-reactive protein concentrations were associated significantly with increased risk for all studied endpoints. Additionally, matrix metalloproteinase-8 concentration was associated with the risk for a coronary artery disease event, myocardial infarction and death, and myeloperoxidase concentration with the risk for cardiovascular disease events, stroke and death. The only significant association for the matrix metalloproteinase-8/ tissue inhibitor of matrix metalloproteinase-1 ratio was observed with the risk for myocardial infarction. Adding tissue inhibitor of matrix metalloproteinase-1 to the established risk profile improved risk discrimination of myocardial infarction (p=0.039) and death (0.001). Both matrix metalloproteinase-8 (5.2%, p <0.001) and tissue inhibitor of matrix metalloproteinase-1 (12.9%, p <0.001) provided significant clinical net reclassification improvement for death. Conclusions Serum matrix metalloproteinase-8 and tissue inhibitor of matrix metalloproteinase-1 can be considered as biomarkers of incident cardiovascular disease events and death.
  • Ribalta, Carla; Koivisto, Antti J.; Salmatonidis, Apostolos; López-Lilao, Ana; Monfort, Eliseo; Viana, Mar (2019)
    Mass balance models have proved to be effective tools for exposure prediction in occupational settings. However, they are still not extensively tested in real-world scenarios, or for particle number concentrations. An industrial scenario characterized by high emissions of unintentionally-generated nanoparticles (NP) was selected to assess the performance of a one-box model. Worker exposure to NPs due to thermal spraying was monitored, and two methods were used to calculate emission rates: the convolution theorem, and the cyclic steady state equation. Monitored concentrations ranged between 4.2 x 10(4)-2.5 x 10(5) cm(-3). Estimated emission rates were comparable with both methods: 1.4 x 10(11)-1.2 x 10(13) min(-1) (convolution) and 1.3 x 10(12)-1.4 x 10(13) min(-1) (cyclic steady state). Modeled concentrations were 1.4-6 x 10(4) cm(-3) (convolution) and 1.7-7.1 x 10(4) cm(-3) (cyclic steady state). Results indicated a clear underestimation of measured particle concentrations, with ratios modeled/measured between 0.2-0.7. While both model parametrizations provided similar results on average, using convolution emission rates improved performance on a case-by-case basis. Thus, using cyclic steady state emission rates would be advisable for preliminary risk assessment, while for more precise results, the convolution theorem would be a better option. Results show that one-box models may be useful tools for preliminary risk assessment in occupational settings when room air is well mixed.
  • Larsson, Aron (Helsingin yliopisto, 2021)
    The science of fish stock assessment is one that is very resource and labor intensive, with stock assessment models historically being based on data that causes a model to overestimate the strength of a population, sometimes with drastic consequences. The need of cost-effective assessment models and approaches increases, which is why I looked into using Bayesian modeling and networks as an approach not often used in fisheries science. I wanted to determine if it could be used to predict both recruitment and spawning stock biomass of four fish species in the north Atlantic, cod, haddock, pollock and capelin, based on no other evidence other than the recruitment or biomass data of the other species and if these results could be used to lower the uncertanties of fish stock models. I used data available on the RAM legacy database to produce four different models with the statistical software R, based on four different Bayes algorithms found in the R-package bnlearn, two based on continuous data and two based on discrete data. What I found was that there is much potential in the Bayesian approach to stock prediction and forecasting, as our prediction error percentage ranged between 1 and 40 percent. The best predictions were made when the species used as evidence had a high correlation coefficient with the target species, which was the case with cod and haddock biomass, which had a unusually high correlation of 0.96. As such, this approach could be used to make preliminary models of interactions between a high amount of species in a specific area, where there is data abundantly available and these models could be used to lower the uncertanties of the stock assessments. However, more research into the applicability for this approach to other species and areas needs to be conducted.
  • Järvinen, T. L. N.; Michaelsson, K.; Aspenberg, P.; Sievanen, H. (2015)
    Current prevention strategies for low-trauma fractures amongst older persons depend on the notions that fractures are mainly caused by osteoporosis (pathophysiology), that patients at high risk can be identified (screening) and that the risk is amenable to bone-targeted pharmacotherapy (treatment). However, all these three notions can be disputed. PathophysiologyMost fracture patients have fallen, but actually do not have osteoporosis. A high likelihood of falling, in turn, is attributable to an ageing-related decline in physical functioning and general frailty. ScreeningCurrently available fracture risk prediction strategies including bone densitometry and multifactorial prediction tools are unable to identify a large proportion of patients who will sustain a fracture, whereas many of those with a high fracture risk score will not sustain a fracture. TreatmentThe evidence for the viability of bone-targeted pharmacotherapy in preventing hip fracture and other clinical fragility fractures is mainly limited to women aged 65-80years with osteoporosis, whereas the proof of hip fracture-preventing efficacy in women over 80years of age and in men at all ages is meagre or absent. Further, the antihip fracture efficacy shown in clinical trials is absent in real-life studies. Many drugs for the treatment of osteoporosis have also been associated with increased risks of serious adverse events. There are also considerable uncertainties related to the efficacy of drug therapy in preventing clinical vertebral fractures, whereas the efficacy for preventing other fractures (relative risk reductions of 20-25%) remains moderate, particularly in terms of the low absolute risk reduction in fractures with this treatment.
  • Vaara, Suvi T.; Glassford, Neil; Eastwood, Glenn M.; Canet, Emmanuel; Mårtensson, Johan; Bellomo, Rinaldo (2020)
    Abstract Background Plasma creatinine (Cr) is a marker of kidney function and typically measured once daily. We hypothesized that Cr measured by point-of-care technology early after ICU admission would be a good predictor of acute kidney injury (AKI) the next day in critically ill patients. Methods We conducted a retrospective database audit in a single tertiary ICU database. We included patients with normal first admission Cr (CrF) and identified a Cr value (CrP) obtained within 6 to 12 hrs from ICU admission. We used their difference converted into percentage (delta-Cr-%) to predict subsequent AKI (based on Cr and/or need for renal replacement therapy) the next day. We assessed predictive value by calculating area under the receiver characteristic curve (AUC), logistic regression models for AKI with and without delta-Cr-%, and the category-free net reclassifying index (cfNRI). Results We studied 780 patients. Overall, 70 (9.0%) fulfilled the Cr AKI definition by CrP measurement. On day 2, 148 patients (19.0%) were diagnosed with AKI. AUC (95% CI) for delta-Cr-% to predict AKI on day 2 was 0.82 (95% CI 0.78-0.86), and 0.74 (95% CI 0.69-0.80) when patients with AKI based on the CrP were excluded. Using a cut-off of 17% increment, the positive likelihood ratio (95% CI) for delta-Cr-% to predict AKI was 3.5 (2.9 ? 4.2). The cfNRI was 90.0 (74.9-106.1). Conclusions Among patients admitted with normal Cr, early changes in Cr help predict AKI the following day.
  • Hellas, Arto; Ihantola, Petri; Petersen, Andrew; Ajanovski, Vangel V.; Gutica, Mirela; Hynninen, Timo; Knutas, Antti; Leinonen, Juho; Messom, Chris; Liao, Soohyun Nam (ACM, 2018)
    ITiCSE 2018 Companion
    The ability to predict student performance in a course or program creates opportunities to improve educational outcomes. With effective performance prediction approaches, instructors can allocate resources and instruction more accurately. Research in this area seeks to identify features that can be used to make predictions, to identify algorithms that can improve predictions, and to quantify aspects of student performance. Moreover, research in predicting student performance seeks to determine interrelated features and to identify the underlying reasons why certain features work better than others. This working group report presents a systematic literature review of work in the area of predicting student performance. Our analysis shows a clearly increasing amount of research in this area, as well as an increasing variety of techniques used. At the same time, the review uncovered a number of issues with research quality that drives a need for the community to provide more detailed reporting of methods and results and to increase efforts to validate and replicate work.
  • Kiczko, Adam; Västilä, Kaisa; Kozioł, Adam; Kubrak, Janusz; Kubrak, Elzbieta; Krukowski, Marcin (EGU, 2020)
    Hydrology and Earth System Sciences 24 8 (2020)
    Despite the development of advanced process-based methods for estimating the discharge capacity of vegetated river channels, most of the practical one-dimensional modeling is based on a relatively simple divided channel method (DCM) with the Manning flow resistance formula. This study is motivated by the need to improve the reliability of modeling in practical applications while acknowledging the limitations on the availability of data on vegetation properties and related parameters required by the process-based methods. We investigate whether the advanced methods can be applied to modeling of vegetated compound channels by identifying the missing characteristics as parameters through the formulation of an inverse problem. Six models of channel discharge capacity are compared in respect of their uncertainty using a probabilistic approach. The model with the lowest estimated uncertainty in explaining differences between computed and observed values is considered the most favorable. Calculations were performed for flume and field settings varying in floodplain vegetation submergence, density, and flexibility, and in hydraulic conditions. The output uncertainty, estimated on the basis of a Bayes approach, was analyzed for a varying number of observation points, demonstrating the significance of the parameter equifinality. The results showed that very reliable predictions with low uncertainties can be obtained for process-based methods with a large number of parameters. The equifinality affects the parameter identification but not the uncertainty of a model. The best performance for sparse, emergent, rigid vegetation was obtained with the Mertens method and for dense, flexible vegetation with a simplified two-layer method, while a generalized two-layer model with a description of the plant flexibility was the most universally applicable to different vegetative conditions. In many cases, the Manning-based DCM performed satisfactorily but could not be reliably extrapolated to higher flows.
  • Konttinen, Hanna; Sjöholm, Kajsa; Jacobson, Peter; Svensson, Per-Arne; Carlsson, Lena; Peltonen, Markku (2021)
    Objective: To identify preoperative sociodemographic and health-related factors that predict higher risk of nonfatal self-harm and suicide after bariatric surgery. Background: Evidence is emerging that bariatric surgery is related to an increased risk of suicide and self-harm, but knowledge on whether certain preoperative characteristics further enhance the excess risk is scarce. Methods: The nonrandomized, prospective, controlled Swedish Obese Subjects study was linked to 2 Nationwide Swedish registers. The bariatric surgery group (N = 2007, per-protocol) underwent gastric bypass, banding or vertical banded gastroplasty, and matched controls (N = 2040) received usual care. Participants were recruited from 1987 to 2001, and information on the outcome (a death by suicide or nonfatal self-harm event) was retrieved until the end of 2016. Subhazard ratios (sub-HR) were calculated using competing risk regression analysis. Results: The risk for self-harm/suicide was almost twice as high in surgical patients compared to control patients both before and after adjusting for various baseline factors [adjusted sub-HR = 1.98, 95% confidence interval (CI) = 1.34-2.93]. Male sex, previous healthcare visits for self-harm or mental disorders, psychiatric drug use, and sleep difficulties predicted higher risk of self-harm/suicide in the multivariate models conducted in the surgery group. Interaction tests further indicated that the excess risk for self-harm/suicide related to bariatric surgery was stronger in men (sub-HR = 3.31, 95% CI = 1.73-6.31) than in women (sub-HR = 1.54, 95% CI = 1.02-2.32) (P = 0.007 for adjusted interaction). A simple-to-use score was developed to identify those at highest risk of these events in the surgery group. Conclusions: Our findings suggest that male sex, psychiatric disorder history, and sleep difficulties are important predictors for nonfatal self-harm and suicide in postbariatric patients. High-risk patients who undergo surgery might require regular postoperative psychosocial monitoring to reduce the risk for future self-harm behaviors.
  • Lilja, Markus; Koskinen, Anni; Julkunen-Iivari, Anna; Mäkitie, Antti; Numminen, Jura; Rautiainen, Markus; Myller, Jyri P.; Markkola, Antti; Suvinen, Mikko; Mäkelä, Mika; Renkonen, Risto; Pekkanen, Juha; Toppila-Salmi, Sanna K. (2022)
    Objective. Evaluate computed tomography (CT) signs that predict need for revision endoscopic sinus surgery (ESS) of chronic rhinosinusitis (CRS). Methods. CRS patients (n = 48) underwent routine sinus CT scans and baseline ESS in 2006-2011. Lund-Mackay (LM) scores and 43 other CT signs were analysed blinded from both sides. Patients filled in a questionnaire during the day of CT scanning. Follow-up data were collected from hospital records until January 2018. Associations were analysed by Fisher's exact, Mann Whitney U, Kaplan-Meier method with logrank test and Cox's proportional hazard model. Results. Total LM score was not significantly associated with the need for revision ESS. The best predictive model was a sum of CT signs of non-detectable anatomy of inferior/middle turbinates, obstructed frontal recess, and previous sinus surgery. Using these CT findings, we formed a Radiological Score (RS) (min-max, 0-3 points). Having at least one RS point was significantly associated with the need for revision ESS during the average follow-up of 10.7 years (p = 0.008, Logrank test). Conclusion. We identified a radiologic score that was able to predict the need for revision ESS, which is probably useful in predicting CRS outcomes.