Bag-of-Words Algorithms Can Supplement Transformer Sequence Classification & Improve Model Interpretability

AbstractRisk-adjustment models are used to predict the cost of care for patients based on their observable characteristics, and to derive efficient and equitable budgets based on weighted capitation. Markers based on past care contacts can improve model fit, but their coefficients may be affected by provider variations in diagnostic, treatment and reporting quality. This is problematic when distinguishing need and supply influences on costs is required.We examine the extent of this bias in the national formula for mental health care using administrative records for 43.7 million adults registered with 7746 GP practices in England in 2015. We also illustrate a method to control for provider effects.A linear regression containing a rich set of individual, GP practice and area characteristics, and fixed effects for local health organisations, had goodness-of-fit equal to R2 = 0.007 at person level and R2 = 0.720 at GP practice level. The addition of past care markers changed substantially the coefficients on the other variables and increased the goodness-of-fit to R2 = 0.275 at person level and R2 = 0.815 at GP practice level. The further inclusion of provider effects affected the coefficients on GP practice and area variables and on local health organisation fixed effects, increasing goodness-of-fit at GP practice level to R2 = 0.848.With adequate supply controls, it is possible to estimate coefficients on past care markers that are stable and unbiased. Nonetheless, inconsistent reporting may affect need predictions and penalise populations served by underreporting providers.

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Towards Robust Representations of Spatial Networks Using Graph Neural Networks

Applied Sciences ◽

10.3390/app11156918 ◽

2021 ◽

Vol 11 (15) ◽

pp. 6918

Author(s):

Chidubem Iddianozie ◽

Gavin McArdle

Keyword(s):

Machine Learning ◽

Model Performance ◽

Network Models ◽

Data Representation ◽

Spatial Networks ◽

Neural Network Models ◽

Improve Model ◽

Graph Neural Networks ◽

Spatial Entities ◽

Improve Model Performance

The effectiveness of a machine learning model is impacted by the data representation used. Consequently, it is crucial to investigate robust representations for efficient machine learning methods. In this paper, we explore the link between data representations and model performance for inference tasks on spatial networks. We argue that representations which explicitly encode the relations between spatial entities would improve model performance. Specifically, we consider homogeneous and heterogeneous representations of spatial networks. We recognise that the expressive nature of the heterogeneous representation may benefit spatial networks and could improve model performance on certain tasks. Thus, we carry out an empirical study using Graph Neural Network models for two inference tasks on spatial networks. Our results demonstrate that heterogeneous representations improves model performance for down-stream inference tasks on spatial networks.

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Population Pharmacokinetics of Doxycycline in Children

Antimicrobial Agents and Chemotherapy ◽

10.1128/aac.01508-19 ◽

2019 ◽

Vol 63 (12) ◽

Author(s):

Elizabeth J. Thompson ◽

Huali Wu ◽

Chiara Melloni ◽

Stephen Balevic ◽

Janice E. Sullivan ◽

...

Keyword(s):

Model Performance ◽

Standard Of Care ◽

Volume Of Distribution ◽

Clinical Value ◽

Final Model ◽

Childhood Infections ◽

Obesity Status ◽

Improve Model ◽

Using Data ◽

Improve Model Performance

ABSTRACT Doxycycline is a tetracycline-class antimicrobial labeled by the U.S. Food and Drug Administration for children >8 years of age for many common childhood infections. Doxycycline is not labeled for children ≤8 years of age, due to the association between tetracycline-class antibiotics and tooth staining, although doxycycline may be used off-label under severe conditions. Accordingly, there is a paucity of pharmacokinetic (PK) data to guide dosing in children 8 years and younger. We leveraged opportunistically collected plasma samples after intravenous (i.v.) and oral doxycycline doses received per standard of care to characterize the PK of doxycycline in children of different ages and evaluated the effect of obesity and fasting status on PK parameters. We developed a population PK model of doxycycline using data collected from 47 patients 0 to 18 years of age, including 14 participants ≤8 years. We developed a 1-compartment PK model and found doxycycline clearance to be 3.32 liters/h/70 kg of body weight and volume to be 96.8 liters/70 kg for all patients, comparable to values reported in adults. We estimated a bioavailability of 89.6%, also consistent with adult data. Allometrically scaled clearance and volume of distribution did not differ between children 2 to ≤8 years of age and children >8 to ≤18 years of age, suggesting that younger children may be given the same per-kilogram dosing. Obesity status and fasting status were not selected for inclusion in the final model. Additional doxycycline PK samples collected in future studies may be used to improve model performance and maximize its clinical value.

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Bag-of-words-driven, single-camera simultaneous localization and mapping

Journal of Field Robotics ◽

10.1002/rob.20368 ◽

2010 ◽

Vol 28 (2) ◽

pp. 204-226 ◽

Cited By ~ 38

Author(s):

Tom Botterill ◽

Steven Mills ◽

Richard Green

Keyword(s):

Simultaneous Localization And Mapping ◽

Single Camera ◽

Bag Of Words ◽

Localization And Mapping

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MetaBinG: Using GPUs to Accelerate Metagenomic Sequence Classification

PLoS ONE ◽

10.1371/journal.pone.0025353 ◽

2011 ◽

Vol 6 (11) ◽

pp. e25353 ◽

Cited By ~ 6

Author(s):

Peng Jia ◽

Liming Xuan ◽

Lei Liu ◽

Chaochun Wei

Keyword(s):

Sequence Classification ◽

Metagenomic Sequence

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