Live delivery of neurosurgical operating theater experience in virtual reality

A system for assisting in microneurosurgical training and for delivering interactive mixed reality surgical experience live was developed and experimented in hospital premises. An interactive experience from the neurosurgical operating theater was presented together with associated medical content on virtual reality eyewear of remote users. Details of the stereoscopic 360-degree capture, surgery imaging equipment, signal delivery, and display systems are presented, and the presence experience and the visual quality questionnaire results are discussed. The users reported positive scores on the questionnaire on topics related to the user experience achieved in the trial.Peer reviewe

Identification and machine learning prediction of knee-point and knee-onset in capacity degradation curves of lithium-ion cells

ABSTRACT: High-performance batteries greatly benefit from accurate, early predictions of future capacity loss, to advance the management of the battery and sustain desirable application-specific performance characteristics for as long as possible. Li-ion cells exhibit a slow capacity degradation up to a knee-point, after which the degradation accelerates rapidly until the cell’s End-of-Life. Using capacity degradation data, we propose a robust method to identify the knee-point within capacity fade curves. In a new approach to knee research, we propose the concept ‘knee-onset’, marking the beginning of the nonlinear degradation, and provide a simple and robust identification mechanism for it. We link cycle life, knee-point and knee-onset, where predicting/identifying one promptly reveals the others. On data featuring continuous high C-rate cycling (1C–8C), we show that, on average, the knee-point occurs at 95% capacity under these conditions and the knee-onset at 97.1% capacity, with knee and its onset on average 108 cycles apart. After the critical identification step, we employ machine learning (ML) techniques for early prediction of the knee-point and knee-onset. Our models predict knee-point and knee-onset quantitatively with 9.4% error using only information from the first 50 cycles of the cells’ life. Our models use the knee-point predictions to classify the cells’ expected cycle lives as short, medium or long with 88–90% accuracy using only information from the first 3–5 cycles. Our accuracy levels are on par with existing literature for End-of-Life prediction (requiring information from 100-cycles), nonetheless, we address the more complex problem of knee prediction. All estimations are enriched with confidence/credibility metrics. The uncertainty regarding the ML model’s estimations is quantified through prediction intervals. These yield risk-criteria insurers and manufacturers of energy storage applications can use for battery warranties. Our classification model provides a tool for cell manufacturers to speed up the validation of cell production techniques

Risk Variants Associated With Normal Pressure Hydrocephalus Genome-Wide Association Study in the FinnGen Cohort

Peer reviewe

Partial information framework: Basic theory and applications

Many real-world decisions depend on accurate predictions of some future outcome. In such cases the decision-maker often seeks to consult multiple people or/and models for their forecasts. These forecasts are then aggregated into a consensus that is inputted in the final decision-making process. Principled aggregation requires an understanding of why the forecasts are different. Historically, such forecast heterogeneity has been explained by measurement error. This dissertation, however, first shows that measurement error is not appropriate for modeling forecast heterogeneity and then introduces information diversity as a more appropriate yet fundamentally different alternative. Under information diversity differences in the forecasts stem purely from differences in the information that is used in the forecasts. This is made mathematically precise in a new modeling framework called the partial information framework. At its most general level, the partial information framework is a very reasonable model of multiple forecasts and hence offers an ideal platform for theoretical analysis. For one, it explains the empirical phenomenon known as extremization. This is a popular technique that often improves the out-of-sample performance of simple aggregators, such as the average or median, by transforming them directly away from the marginal mean of the outcome. Unfortunately, the general framework is too abstract for practical applications. To apply the framework in practice one needs to choose a parametric distribution for the forecasts and outcome. This dissertation motivates and chooses the multivariate Gaussian distribution. The result, known as the Gaussian partial information model, is a very close yet practical specification of the framework. The optimal aggregator under the Gaussian model is shown to outperform the state-of-the-art measurement error aggregators on both synthetic and many different types of real-world forecasts

Partial information framework: Basic theory and applications

Many real-world decisions depend on accurate predictions of some future outcome. In such cases the decision-maker often seeks to consult multiple people or/and models for their forecasts. These forecasts are then aggregated into a consensus that is inputted in the final decision-making process. Principled aggregation requires an understanding of why the forecasts are different. Historically, such forecast heterogeneity has been explained by measurement error. This dissertation, however, first shows that measurement error is not appropriate for modeling forecast heterogeneity and then introduces information diversity as a more appropriate yet fundamentally different alternative. Under information diversity differences in the forecasts stem purely from differences in the information that is used in the forecasts. This is made mathematically precise in a new modeling framework called the partial information framework. At its most general level, the partial information framework is a very reasonable model of multiple forecasts and hence offers an ideal platform for theoretical analysis. For one, it explains the empirical phenomenon known as extremization. This is a popular technique that often improves the out-of-sample performance of simple aggregators, such as the average or median, by transforming them directly away from the marginal mean of the outcome. Unfortunately, the general framework is too abstract for practical applications. To apply the framework in practice one needs to choose a parametric distribution for the forecasts and outcome. This dissertation motivates and chooses the multivariate Gaussian distribution. The result, known as the Gaussian partial information model, is a very close yet practical specification of the framework. The optimal aggregator under the Gaussian model is shown to outperform the state-of-the-art measurement error aggregators on both synthetic and many different types of real-world forecasts

Comparison of all 19 published prognostic scores for intracerebral hemorrhage

Background and aims: We evaluated the accuracy of 19 published prognostic scores to find the best tool for predicting mortality after intracerebral hemorrhage (ICH). Methods: A retrospective single-center analysis of consecutive patients with ICH (n = 1013). After excluding patients with missing data (n = 131), we analyzed 882 patients for 3-month (primary outcome), in-hospital, and 12-month mortality. We analyzed the strength of the individual score components and calculated the c-statistics, Youden index, sensitivity, specificity, negative and positive predictive value (NPV and PPV) for the scores. Finally, we included every score component in a multivariable model to analyze the maximum predictive value of the data elements combined. Results: Observed in-hospital mortality was 23.6%, 3-month mortality was 31.0%, and 12-month mortality was 35.3%. For in-hospital mortality, the National Institutes of Health Stroke Scale (NIHSS) performed equally good as the best score for the other outcomes, the ICH Functional Outcome Score (ICH-FOS). The c-statistics of the scores varied from 0.6293 (95% CI 0.587-0.672) to 0.8802 (0.855-0.906). With all variables from all the scores in a multivariable regression model, the c-statistics did not improve, being 0.89 (0.867-0.913). Using the Youden index cutoff for the ICH-FOS score, the sensitivity (73%), specificity (90%), PPV (76%), and NPV (88%) for the primary outcome were good. Conclusions: A plethora of scores exists to help clinicians estimate the prognosis of an acute ICH patient. The NIHSS can be used to quantify the risk of in-hospital death while the ICH-FOS performed best for the other outcomes. (C) 2017 Elsevier B.V. All rights reserved.Peer reviewe

Skew-Adjusted Extremized-Mean : A Simple Method for Identifying and Learning From Contrarian Minorities in Groups of Forecasters

Recent work in forecast aggregation has demonstrated that paying attention to contrarian minorities among larger groups of forecasters can improve aggregated probabilistic forecasts. In those papers, the minorities are identified using `meta-questions' that ask forecasters about their forecasting abilities or those of others. In the current paper, we explain how contrarian minorities can be identified without the meta-questions by inspecting the skewness of the distribution of the forecasts. Inspired by this observation, we introduce a new forecast aggregation tool called \textit{Skew-Adjusted Extremized-Mean} and demonstrate its superior predictive power on a large set of geopolitical and general knowledge forecasting data