Event series prediction via non-homogeneous Poisson process modelling

Data streams whose events occur at random arrival times rather than at the regular, tick-tock intervals of traditional time series are increasingly prevalent. Event series are continuous, irregular and often highly sparse, differing greatly in nature to the regularly sampled time series traditionally the concern of hard sciences. As mass sets of such data have become more common, so interest in predicting future events in them has grown. Yet repurposing of traditional forecasting approaches has proven ineffective, in part due to issues such as sparsity, but often due to inapplicable underpinning assumptions such as stationarity and ergodicity. In this paper we derive a principled new approach to forecasting event series that avoids such assumptions, based upon: 1. the processing of event series datasets in order to produce a parameterized mixture model of non-homogeneous Poisson processes; and 2. application of a technique called parallel forecasting that uses these processes’ rate functions to directly generate accurate temporal predictions for new query realizations. This approach uses forerunners of a stochastic process to shed light on the distribution of future events, not for themselves, but for realizations that subsequently follow in their footsteps

Multi-Level Spatial Comparative Judgement Models To Map Deprivation

While current comparative judgement models provide strong algorithmic efficiency, they remain data inefficient, often requiring days or weeks of extensive data collection to provide sufficient pair- wise comparisons for stable and accurate parameter estimation. This disparity between data and algorithm efficiency is preventing widespread adoption, especially so in challenging data-collection environments such as mapping human rights abuses. We address the data inefficiency challenge by introducing the finite element Gaussian process Bradley–Terry mixture model, an approach that significantly reduces the number of pairwise comparisons required by comparative judgement mod- els. This is achieved via integration of prior spatial assumptions, encoded as a mixture of functions, each function introducing a spatial smoothness constraint at a specific resolution. These functions are modelled nonparametrically, through Gaussian process prior distributions. We use our method to map deprivation in the city of Dar es Salaam, Tanzania and locate slums in the city where poverty reduction measures can be carried out

AMP: a new time-frequency feature extraction method for intermittent time-series data

The characterisation of time-series data via their most salient features is extremely important in a range of machine learning task, not least of all with regards to classification and clustering. While there exist many feature extraction techniques suitable for non-intermittent time-series data, these approaches are not always appropriate for intermittent time-series data, where intermittency is characterized by constant values for large periods of time punctuated by sharp and transient increases or decreases in value. Motivated by this, we present aggregation, mode decomposition and projection (AMP) a feature extraction technique particularly suited to intermittent time-series data which contain time-frequency patterns. For our method all individual time-series within a set are combined to form a non-intermittent aggregate. This is decomposed into a set of components which represent the intrinsic time-frequency signals within the data set. Individual time-series can then be _t to these components to obtain a set of numerical features that represent their intrinsic time-frequency patterns. To demonstrate the effectiveness of AMP, we evaluate against the real word task of clustering intermittent time-series data. Using synthetically generated data we show that a clustering approach which uses the features derived from AMP significantly outperforms traditional clustering methods. Our technique is further exemplified on a real world data set where AMP can be used to discover groupings of individuals which correspond to real world sub-populations

EFFECT OF HYPEROXIA ON CRITICAL POWER AND V ̇O2 KINETICS DURING UPRIGHT CYCLING

Introduction/Purpose: Critical power (CP) is a fundamental parameter defining high-intensity exercise tolerance, however its physiological determinants are incompletely understood. The present study determined the impact of hyperoxia on CP, the time constant of phase II pulmonary oxygen uptake kinetics (τ_V ̇ O2), and muscle oxygenation (assessed by near-infrared spectroscopy) in 9 healthy men performing upright cycle ergometry. Methods: CP was determined in normoxia and hyperoxia (fraction of inspired O2 = 0.5) via 4 severe-intensity constant load exercise tests to exhaustion on a cycle ergometer, repeated once in each condition. During each test, τ_V ̇ O2 and the time constant of muscle deoxyhaemoglobin kinetics (τ[HHb]), alongside absolute concentrations of muscle oxyhaemoglobin ([HbO2]), were determined. Results: CP was greater (hyperoxia: 216 ± 30 vs. normoxia: 197 ± 29W; P < 0.001) whereas W’ was reduced (hyperoxia: 15.4 ± 5.2 kJ, normoxia: 17.5 ± 4.3 W; P = 0.037) in hyperoxia compared to normoxia. τ_V ̇ O2 (hyperoxia: 35 ± 12 vs normoxia: 33 ± 10 s; P = 0.33) and τ[HHb] (hyperoxia: 11 ± 5 vs. normoxia: 14 ± 5 s; P = 0.65) were unchanged between conditions, whereas [HbO2] during exercise was greater in hyperoxia compared to normoxia (hyperoxia: 73 ± 20 vs. normoxia: 66 ± 15 μM; P = 0.001). Conclusion: This study provides novel insights into the physiological determinants of CP and by extension, exercise tolerance. Microvascular oxygenation and CP were improved during exercise in hyperoxia compared with normoxia. Importantly, the improved microvascular oxygenation afforded by hyperoxia did not alter τ_V ̇ O2, suggesting that microvascular O2 availability is an independent determinant of the upper limit for steady-state exercise, i.e. CP

Prior exercise speeds pulmonary oxygen uptake kinetics and increases critical power during supine but not upright cycling

Critical power (CP) is a fundamental parameter defining high-intensity exercise tolerance and is related to the time constant of phase II pulmonary oxygen uptake kinetics (τV̇O2). To test the hypothesis that this relationship is causal we determined the impact of prior exercise (“priming”) on CP and τV̇O2 in the upright and supine positions. 17 healthy men were assigned to either upright or supine exercise groups, whereby CP, τV̇O2 and muscle deoxyhaemoglobin kinetics (τ[HHb]) were determined via constant-power tests to exhaustion at four work-rates with (primed) and without (control) priming exercise at ∼31%Δ. During supine exercise, priming reduced τV̇O2 (control: 54 ± 18 vs. primed: 39 ± 11 s; P < 0.001), increased τ[HHb] (control: 8 ± 4 vs. primed: 12 ± 4 s; P = 0.003) and increased CP (control: 177 ± 31 vs. primed: 185 ± 30 W, P = 0.006) compared to control. However, priming exercise had no effect on τV̇O2 (control: 37 ± 12 vs. primed: 35 ± 8 s; P = 0.82), τ[HHb] (CON: 10 ± 5 s vs. PRI: 14 ± 10; P = 0.10), or CP (control: 235 ± 42 vs. primed: 232 ± 35 W; P = 0.57) during upright exercise. The concomitant reduction of τV̇O2 and increased CP following priming in the supine group, effects that were absent in the upright group, provides the first experimental evidence that τV̇O2 is mechanistically related to critical power. The increased τ[HHb] suggests that this effect was mediated, at least in part, by improved oxygen availability

LIMITATIONS TO EXERCISE TOLERANCE IN TYPE 1 DIABETES: THE ROLE OF PULMONARY OXYGEN UPTAKE KINETICS AND PRIMING EXERCISE

We compared the time constant (τ_V ̇ O2) of the fundamental phase of pulmonary oxygen uptake (V ̇O2) kinetics between young adult males with type 1 diabetes and healthy controls. We also assessed the impact of priming exercise on τ_V ̇ O2, critical power, and muscle deoxygenation in a subset of participants with type 1 diabetes. 17 males with type 1 diabetes and 17 healthy male controls performed moderate-intensity exercise to determine τ_V ̇ O2. A subset of 7 participants with type 1 diabetes performed an additional eight visits, whereby critical power, τ_V ̇ O2 and muscle deoxyhaemoglobin + myoglobin ([HHb+Mb]; via near-infrared spectroscopy) kinetics (described by a time constant, τ[HHb+Mb]) were determined with (PRI) and without (CON) a prior 6-minute bout of heavy exercise. τ_V ̇ O2 was greater in participants with type 1 diabetes compared to controls (type 1 diabetes: 50±13 vs. control: 32±12 s; P<0.001). Critical power was greater in PRI compared to CON (PRI: 161±25 W vs. CON: 149±22 W; P<0.001), whereas τ_V ̇ O2 (PRI: 36±15 vs. CON: 50±21 s; P=0.006) and τ[HHb+Mb] (PRI: 10±5 vs. CON: 17±11 s; P=0.037) were reduced in PRI compared to CON. Type 1 diabetes patients showed slower pulmonary V ̇O2 kinetics when compared to controls; priming exercise speeded V ̇O2 and [HHb + Mb] kinetics, and increased critical power in a subgroup with type 1 diabetes. These data therefore represent the first characterisation of the power-duration relationship in type 1 diabetes, and the first experimental evidence that τ_V ̇ O2 is an independent determinant of critical power in this population

Quality Improvement Toward Decreasing High-Risk Medications for Older Veteran Outpatients

To examine the effectiveness of a quality improvement program to decrease prescribing of high-risk medications. DESIGN : Single cohort, pre- and postintervention. SETTING : Regional network of Department of Veterans Affairs medical facilities. PARTICIPANTS : Outpatient veterans aged 65 and older who received one or more high-risk medications and the prescribing clinicians. INTERVENTION : A two-stage intervention was implemented. First, a real-time warning message to prescribers appeared whenever one of the high-risk drugs was ordered; second, a personally addressed letter from the Chief Medical Officer asking prescribers to consider discontinuing the high-risk medication along with a copy of the Beers criteria article, a list of suggested alternatives to high-risk medications, and a list of older patients receiving the high-risk medications who had upcoming appointments with these prescribers. MEASUREMENTS : The primary outcome was the absence of prescribed high-risk medications for all patients in the cohort during the postintervention period. For a subgroup of the cohort whose prescribers received the second-stage intervention, an additional outcome was the absence of prescribed high-risk medications within the subgroup. RESULTS : Two thousand seven hundred fifty-three unique patients were identified in the cohort; 1,396 (50.7%) had high-risk medications discontinued, resulting in a significant decrease in the number of patients prescribed high-risk medications from the preintervention period to the postintervention period ( P <.001). Of the 801 patients in the subgroup, 72.0% (n=577) had high-risk medications discontinued ( P <.001). CONCLUSION : This multimethod intervention significantly decreased prescribing of high-risk medications to older patients. Further studies are needed to confirm the findings.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/65236/1/j.1532-5415.2008.01772.x.pd