Calibration : the Achilles heel of predictive analytics

Acknowledgements This work was developed as part of the international STRengthening Analytical Thinking for Observational Studies (STRATOS) initiative. The objective of STRATOS is to provide accessible and accurate guidance in the design and analysis of observational studies (http://stratos-initiative.org/). Members of the STRATOS Topic Group ‘Evaluating diagnostic tests and prediction models’ are (alphabetically) Patrick Bossuyt, Gary S. Collins, Petra Macaskill, David J. McLernon, Karel G.M. Moons, Ewout W. Steyerberg, Ben Van Calster, Maarten van Smeden, and Andrew Vickers. Funding This work was funded by the Research Foundation – Flanders (FWO; grant G0B4716N) and Internal Funds KU Leuven (grant C24/15/037). The funders had no role in study design, data collection, data analysis, interpretation of results, or writing of the manuscript. Contributions All authors conceived of the study. BVC drafted the manuscript. All authors reviewed and edited the manuscript and approved the final version.Peer reviewedPublisher PD

PREDICTION MODELS FOR OLFACTORY METABOLIC AND SOWS %RNAreticulocyt (RNArt) BY MEASUREMENT OF ATMOSPHERIC AMMONIA EXPOSURE AND MICROCLIMATE LEVEL

Twenty sows housed indoors in individual stalls were used to determine the relationships between atmospheric ammonia exposure and microclimate on olfactory metabolic and sows RNAreticulocyt, and to know the prediction models of the olfactory metabolic and sows RNAreticulocyt by measurement of atmospheric ammonia exposure and microclimate level. Result indicated a significantly negative effect of ammonia on commonly olfactory metabolic parameters and %RNAreticulocyt. The results also showed that ammonia has been reduced the function of olfactory receptors and activities of Ca2+-gated chloride channel open and efflux of Cl- to depolarize cell, as soon as reducing an electrical signal to the brain, so gives impact to blood metabolism (especially RNAreticulocyt). Simultaneous effect between ammonia and humidity proved to be a good indicator for predicting model of olfactory metabolic, and %RNAreticulocyt especially for creatine kinase (=16.65+0.02H-0.59A), glucose (=21.55-0.10H-0.01A), lactate (=8,87-0.03H-0.20A), ATPase (=0.05+0.00H-0.02A), adenosine triphosphate (ATP) (=13.19-0.19H+0.86A)

Wind power prediction models

Investigations were performed to predict the power available from the wind at the Goldstone, California, antenna site complex. The background for power prediction was derived from a statistical evaluation of available wind speed data records at this location and at nearby locations similarly situated within the Mojave desert. In addition to a model for power prediction over relatively long periods of time, an interim simulation model that produces sample wind speeds is described. The interim model furnishes uncorrelated sample speeds at hourly intervals that reproduce the statistical wind distribution at Goldstone. A stochastic simulation model to provide speed samples representative of both the statistical speed distributions and correlations is also discussed

Analysis of variable parameters of prediction models

The article deals with the calculation of corrosion losses of weathering steels with doseresponse functions. The environmental parameters of atmospheric corrosion incoming the dose-response functions are analysed by statistic and probabilistic methods. Between main environmental parameters belong temperature T, concentration of sulphur dioxide, relative air humidity RH, and deposition of chloride. Long-term measurements of environmental characteristics at atmospheric test sites were used for the analysis. All the environmental parameters incoming the dose-response functions are considered random variables represented by corresponding histogram. Using the probabilistic analysis it is possible to predict the expected range of corrosion rates and to analyse the impact of particular environmental characteristic on corrosion process

Transferable Pedestrian Motion Prediction Models at Intersections

One desirable capability of autonomous cars is to accurately predict the pedestrian motion near intersections for safe and efficient trajectory planning. We are interested in developing transfer learning algorithms that can be trained on the pedestrian trajectories collected at one intersection and yet still provide accurate predictions of the trajectories at another, previously unseen intersection. We first discussed the feature selection for transferable pedestrian motion models in general. Following this discussion, we developed one transferable pedestrian motion prediction algorithm based on Inverse Reinforcement Learning (IRL) that infers pedestrian intentions and predicts future trajectories based on observed trajectory. We evaluated our algorithm on a dataset collected at two intersections, trained at one intersection and tested at the other intersection. We used the accuracy of augmented semi-nonnegative sparse coding (ASNSC), trained and tested at the same intersection as a baseline. The result shows that the proposed algorithm improves the baseline accuracy by 40% in the non-transfer task, and 16% in the transfer task

Prediction Models for Clinical Outcome After a Carotid Revascularization Procedure.

Background and Purpose- Prediction models may help physicians to stratify patients with high and low risk for periprocedural complications or long-term stroke risk after carotid artery stenting or carotid endarterectomy. We aimed to evaluate external performance of previously published prediction models for short- and long-term outcome after carotid revascularization in patients with symptomatic carotid artery stenosis. Methods- From a literature review, we selected all prediction models that used only readily available patient characteristics known before procedure initiation. Follow-up data from 2184 carotid artery stenting and 2261 carotid endarterectomy patients from 4 randomized trials (EVA-3S [Endarterectomy Versus Angioplasty in Patients With Symptomatic Severe Carotid Stenosis], SPACE [Stent-Protected Angioplasty Versus Carotid Endarterectomy], ICSS [International Carotid Stenting Study], and CREST [Carotid Revascularization Endarterectomy Versus Stenting Trial]) were used to validate 23 short-term outcome models to estimate stroke or death risk ≤30 days after the procedure and the original outcome measure for which the model was developed. Additionally, we validated 7 long-term outcome models for the original outcome measure. Predictive performance of the models was assessed with C statistics and calibration plots. Results- Stroke or death ≤30 days after the procedure occurred in 158 (7.2%) patients after carotid artery stenting and in 84 (3.7%) patients after carotid endarterectomy. Most models for short-term outcome after carotid artery stenting (n=4) or carotid endarterectomy (n=19) had poor discriminative performance (C statistics ranging from 0.49-0.64) and poor calibration with small absolute risk differences between the lowest and highest risk groups and overestimation of risk in the highest risk groups. Long-term outcome models (n=7) had a slightly better performance with C statistics ranging from 0.59 to 0.67 and reasonable calibration. Conclusions- Current models did not reliably predict outcome after carotid revascularization in a trial population of patients with symptomatic carotid stenosis. In particular, prediction of short-term outcome seemed to be difficult. Further external validation of existing prediction models or development of new prediction models is needed before such models can be used to support treatment decisions in individual patients