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Svenson, K., McRobbie, S. & Alam, M. (2019). Detecting road pavement deterioration with finite mixture models. The international journal of pavement engineering, 20(4), 458-465
Open this publication in new window or tab >>Detecting road pavement deterioration with finite mixture models
2019 (English)In: The international journal of pavement engineering, ISSN 1029-8436, E-ISSN 1477-268X, Vol. 20, no 4, p. 458-465Article in journal (Refereed) Published
Abstract [en]

Budget restrictions often limit the number of possible maintenance activities in a road network each year. To effectively allocate resources, the rate of road pavement deterioration is of great importance. If two maintenance candidates have an equivalent condition, it is reasonable to maintain the segment with the highest deterioration rate first. To identify such segments, finite mixture models were applied to road condition data from a part of the M4 highway in England. Assuming that data originates from two different normal distributions – defined as a ‘change’ distribution and an ‘unchanged’ distribution – all road segments were classified into one of the groups. Comparisons with known measurement errors and maintenance records showed that segments in the unchanged group had a stationary road condition. Segments classified into the change group showed either a rapid deterioration, improvement in condition because of previous maintenance or unusual measurement errors. Together with additional information from maintenance records, finite mixture models can identify segments with the most rapid deterioration rate, and contribute to more efficient maintenance decisions.

Keywords
Finite mixture models, pavement deterioration, road maintenance
National Category
Probability Theory and Statistics
Research subject
Complex Systems – Microdata Analysis
Identifiers
urn:nbn:se:du-24767 (URN)10.1080/10298436.2017.1309193 (DOI)000457029200009 ()2-s2.0-85017252284 (Scopus ID)
Available from: 2017-04-24 Created: 2017-04-24 Last updated: 2019-02-14Bibliographically approved
Saqlain, M., Alam, M., Rönnegård, L. & Westin, J. (2019). Investigating Stochastic Differential Equations Modelling for Levodopa Infusion in Patients with Parkinson's Disease. European journal of drug metabolism and pharmacokinetics
Open this publication in new window or tab >>Investigating Stochastic Differential Equations Modelling for Levodopa Infusion in Patients with Parkinson's Disease
2019 (English)In: European journal of drug metabolism and pharmacokinetics, ISSN 0378-7966, E-ISSN 2107-0180Article in journal (Refereed) Epub ahead of print
Abstract [en]

BACKGROUND AND OBJECTIVES: Levodopa concentration in patients with Parkinson's disease is frequently modelled with ordinary differential equations (ODEs). Here, we investigate a pharmacokinetic model of plasma levodopa concentration in patients with Parkinson's disease by introducing stochasticity to separate the intra-individual variability into measurement and system noise, and to account for auto-correlated errors. We also investigate whether the induced stochasticity provides a better fit than the ODE approach.

METHODS: In this study, a system noise variable is added to the pharmacokinetic model for duodenal levodopa/carbidopa gel (LCIG) infusion described by three ODEs through a standard Wiener process, leading to a stochastic differential equations (SDE) model. The R package population stochastic modelling (PSM) was used for model fitting with data from previous studies for modelling plasma levodopa concentration and parameter estimation. First, the diffusion scale parameter (σw), measurement noise variance, and bioavailability are estimated with the SDE model. Second, σw is fixed to certain values from 0 to 1 and bioavailability is estimated. Cross-validation was performed to compare the average root mean square errors (RMSE) of predicted plasma levodopa concentration.

RESULTS: Both the ODE and the SDE models estimated bioavailability to be approximately 75%. The SDE model converged at different values of σw that were significantly different from zero. The average RMSE for the ODE model was 0.313, and the lowest average RMSE for the SDE model was 0.297 when σw was fixed to 0.9, and these two values are significantly different.

CONCLUSIONS: The SDE model provided a better fit for LCIG plasma levodopa concentration by approximately 5.5% in terms of mean percentage change of RMSE.

National Category
Probability Theory and Statistics
Research subject
Complex Systems – Microdata Analysis
Identifiers
urn:nbn:se:du-30962 (URN)10.1007/s13318-019-00580-w (DOI)31595429 (PubMedID)
Available from: 2019-10-16 Created: 2019-10-16 Last updated: 2019-10-16
Thomas, I., Alam, M., Bergquist, F., Johansson, D., Memedi, M., Nyholm, D. & Westin, J. (2019). Sensor-based algorithmic dosing suggestions for oral administration of levodopa/carbidopa microtablets for Parkinson's disease: a first experience. Journal of Neurology, 266(3), 651-658
Open this publication in new window or tab >>Sensor-based algorithmic dosing suggestions for oral administration of levodopa/carbidopa microtablets for Parkinson's disease: a first experience
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2019 (English)In: Journal of Neurology, ISSN 0340-5354, E-ISSN 1432-1459, Vol. 266, no 3, p. 651-658Article in journal (Refereed) Published
Abstract [en]

OBJECTIVE: Dosing schedules for oral levodopa in advanced stages of Parkinson's disease (PD) require careful tailoring to fit the needs of each patient. This study proposes a dosing algorithm for oral administration of levodopa and evaluates its integration into a sensor-based dosing system (SBDS).

MATERIALS AND METHODS: In collaboration with two movement disorder experts a knowledge-driven, simulation based algorithm was designed and integrated into a SBDS. The SBDS uses data from wearable sensors to fit individual patient models, which are then used as input to the dosing algorithm. To access the feasibility of using the SBDS in clinical practice its performance was evaluated during a clinical experiment where dosing optimization of oral levodopa was explored. The supervising neurologist made dosing adjustments based on data from the Parkinson's KinetiGraph™ (PKG) that the patients wore for a week in a free living setting. The dosing suggestions of the SBDS were compared with the PKG-guided adjustments.

RESULTS: The SBDS maintenance and morning dosing suggestions had a Pearson's correlation of 0.80 and 0.95 (with mean relative errors of 21% and 12.5%), to the PKG-guided dosing adjustments. Paired t test indicated no statistical differences between the algorithmic suggestions and the clinician's adjustments.

CONCLUSION: This study shows that it is possible to use algorithmic sensor-based dosing adjustments to optimize treatment with oral medication for PD patients.

Keywords
Algorithmic suggestions, Levodopa, Oral medication, Parkinson’s disease, Sensor data
National Category
Probability Theory and Statistics Other Medical Sciences
Research subject
Complex Systems – Microdata Analysis; Complex Systems – Microdata Analysis
Identifiers
urn:nbn:se:du-29314 (URN)10.1007/s00415-019-09183-6 (DOI)000459203400013 ()30659356 (PubMedID)2-s2.0-85060256040 (Scopus ID)
Available from: 2019-01-21 Created: 2019-01-21 Last updated: 2019-03-14Bibliographically approved
Thomas, I., Westin, J., Alam, M., Bergquist, F., Nyholm, D., Senek, M. & Memedi, M. (2018). A treatment–response index from wearable sensors for quantifying Parkinson's disease motor states. IEEE journal of biomedical and health informatics, 22(5), 1341-1349
Open this publication in new window or tab >>A treatment–response index from wearable sensors for quantifying Parkinson's disease motor states
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2018 (English)In: IEEE journal of biomedical and health informatics, ISSN 2168-2194, E-ISSN 2168-2208, Vol. 22, no 5, p. 1341-1349Article in journal (Refereed) Published
Abstract [en]

The goal of this study was to develop an algorithm that automatically quantifies motor states (off,on,dyskinesia) in Parkinson's disease (PD), based on accelerometry during a hand pronation-supination test. Clinician's ratings using the Treatment Response Scale (TRS), ranging from -3 (very Off) to 0 (On) to +3 (very dyskinetic), was used as target. For that purpose, 19 participants with advanced PD and 22 healthy persons were recruited in a single center open label clinical trial in Uppsala, Sweden. The trial consisted of single levodopa dose experiments for the people with PD (PwP), where participants were asked to perform standardized wrist rotation tests, using each hand, before and at pre-specified time points after the dose. The participants used wrist sensors containing a 3D accelerometer and gyroscope. Features to quantify the level, variation and asymmetry of the sensor signals, three-level Discrete Wavelet Transform features and approximate entropy measures were extracted from the sensors data. At the time of the tests, the PwP were video recorded. Three movement disorder specialists rated the participants’ state on the TRS scale. A Treatment Response Index from Sensors (TRIS) was constructed to quantify the motor states based on the wrist rotation tests. Different machine learning algorithms were evaluated to map the features derived from the sensor data to the ratings provided by the three specialists. Results from cross validation, both in 10-fold and a leave-one-individual out setting, showed good predictive power of a support vector machine model and high correlation to the TRS scale. Values at the end tails of the TRS scale were under and over predicted due to the lack of observations at those values but the model managed to accurately capture the dose - effect profiles of the patients. In addition, the TRIS had good test-retest reliability on the baseline levels of the PD participants (Intraclass correlation coefficient of 0.83) and reasonable sensitivity to levodopa treatment (0.33 for the TRIS). For a series of test occasions the proposed algorithms provided dose - effect time profiles for participants with PD, which could be useful during therapy individualization of people suffering from advanced PD

Keywords
Accelerometers, Accelerometry, Diseases, Feature extraction, Levodopa response, Machine learning, Parkinson's disease, Pattern recognition, Sensor phenomena and characterization, Signal processing, Wearable sensors, Wrist
National Category
Computer and Information Sciences
Research subject
Complex Systems – Microdata Analysis
Identifiers
urn:nbn:se:du-26745 (URN)10.1109/JBHI.2017.2777926 (DOI)000441795800002 ()29989996 (PubMedID)2-s2.0-85035809095 (Scopus ID)
Available from: 2017-12-11 Created: 2017-12-11 Last updated: 2019-02-06Bibliographically approved
Thomas, I., Alam, M., Nyholm, D., Senek, M. & Westin, J. (2018). Individual dose-response models for levodopa infusion dose optimization. International Journal of Medical Informatics, 112, 137-142
Open this publication in new window or tab >>Individual dose-response models for levodopa infusion dose optimization
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2018 (English)In: International Journal of Medical Informatics, ISSN 1386-5056, E-ISSN 1872-8243, Vol. 112, p. 137-142Article in journal (Refereed) Published
Abstract [en]

Background and Objective

To achieve optimal effect with continuous infusion treatment in Parkinson’s disease (PD), the individual doses (morning dose and continuous infusion rate) are titrated by trained medical personnel. This study describes an algorithmic method to derive optimized dosing suggestions for infusion treatment of PD, by fitting individual dose-response models. The feasibility of the proposed method was investigated using patient chart data.

Methods

Patient records were collected at Uppsala University hospital which provided dosing information and dose-response evaluations. Mathematical optimization was used to fit individual patient models using the records’ information, by minimizing an objective function. The individual models were passed to a dose optimization algorithm, which derived an optimized dosing suggestion for each patient model.

Results

Using data from a single day’s admission the algorithm showed great ability to fit appropriate individual patient models and derive optimized doses. The infusion rate dosing suggestions had 0.88 correlation and 10% absolute mean relative error compared to the optimal doses as determined by the hospital’s treating team. The morning dose suggestions were consistency lower that the optimal morning doses, which could be attributed to different dosing strategies and/or lack of on-off evaluations in the morning.

Conclusion

The proposed method showed promise and could be applied in clinical practice, to provide the hospital personnel with additional information when making dose adjustment decisions.

Place, publisher, year, edition, pages
Elsevier, 2018
Keywords
Levodopa infusion; Algorithmic dosing suggestions; Patient-specific models; Parkinson’s disease
National Category
Computer and Information Sciences
Research subject
Complex Systems – Microdata Analysis
Identifiers
urn:nbn:se:du-27065 (URN)10.1016/j.ijmedinf.2018.01.018 (DOI)000426130900018 ()29500011 (PubMedID)
Available from: 2018-02-02 Created: 2018-02-02 Last updated: 2019-02-06Bibliographically approved
Skarin, A., Sandström, P. & Alam, M. (2018). Out of sight of wind turbines — Reindeer response to wind farms in operation. Ecology and Evolution, 8, 9906-9919
Open this publication in new window or tab >>Out of sight of wind turbines — Reindeer response to wind farms in operation
2018 (English)In: Ecology and Evolution, ISSN 2045-7758, E-ISSN 2045-7758, Vol. 8, p. 9906-9919Article in journal (Refereed) Published
Abstract [en]

To meet the expanding land use required for wind energy development, a better understanding of the effects on terrestrial animals’ responses to such development is required. Using GPS-data from 50 freely ranging female reindeer (Rangifer tarandus) in the Malå reindeer herding community, Sweden, we determined reindeer calving sites and estimated reindeer habitat selection using resource selection functions (RSF). RSFs were estimated at both second- (selection of home range) and third-order (selection within home range) scale in relation to environmental variables, wind farm (WF) development phase (before construction, construction, and operation), distance to the WFs and at the second-order scale whether the wind turbines were in or out of sight of the reindeer. We found that the distance between reindeer calving site and WFs increased during the operation phase, compared to before construction. At both scales of selection, we found a significant decrease in habitat selection of areas in proximity of the WFs, in the same comparison. The results also revealed a shift in home range selection away from habitats where wind turbines became visible toward habitats where the wind turbines were obscured by topography (increase in use by 79% at 5 km). We interpret the reindeer shift in home range selection as an effect of the wind turbines per se. Using topography and land cover information together with the positions of wind turbines could therefore help identify sensitive habitats for reindeer and improve the planning and placement of WFs. In addition, we found that operation phase of these WFs had a stronger adverse impact on reindeer habitat selection than the construction phase. Thus, the continuous running of the wind turbines making a sound both day and night seemed to have disturbed the reindeer more than the sudden sounds and increased human activity during construction work.

Keywords
anthropogenic disturbance, calving season, cumulative impact, habitat selection, large herbivore, Rangifer tarandus, renewable energy, semi-domesticated reindeer
National Category
Biological Sciences Energy Engineering
Research subject
Complex Systems – Microdata Analysis
Identifiers
urn:nbn:se:du-28489 (URN)10.1002/ece3.4476 (DOI)000448803000029 ()30386585 (PubMedID)2-s2.0-85052859300 (Scopus ID)
Available from: 2018-09-18 Created: 2018-09-18 Last updated: 2018-11-15Bibliographically approved
Saqlain, M., Alam, M., Brandt, D., Rönnegård, L. & Westin, J. (2018). Stochastic differential equations modelling of levodopa concentration in patients with Parkinson's disease. In: : . Paper presented at The 40th Conference on Stochastic Processes and their Applications – SPA 2018, June 11-15 2018, Gothenburg.
Open this publication in new window or tab >>Stochastic differential equations modelling of levodopa concentration in patients with Parkinson's disease
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2018 (English)Conference paper, Poster (with or without abstract) (Other academic)
Abstract [en]

The purpose of this study is to investigate a pharmacokinetic model of levodopa concentration in patients with Parkinson's disease by introducing stochasticity so that inter-individual variability may be separated into measurement and system noise. It also aims to investigate whether the stochastic differential equations (SDE) model provide better fits than its ordinary differential equations (ODE) counterpart, by using a real data set. Westin et al. developed a pharmacokinetic-pharmacodynamic model for duodenal levodopa infusion described by four ODEs, the first three of which define the pharmacokinetic model. In this study, system noise variables are added to the aforementioned first three equations through a standard Wiener process, also known as Brownian motion. The R package PSM for mixed-effects models is used on data from previous studies for modelling levodopa concentration and parameter estimation. First, the diffusion scale parameter, σ, and bioavailability are estimated with the SDE model. Second, σ is fixed to integer values between 1 and 5, and bioavailability is estimated. Cross-validation is performed to determine whether the SDE based model explains the observed data better or not by comparingthe average root mean squared errors (RMSE) of predicted levodopa concentration. Both ODE and SDE models estimated bioavailability to be about 88%. The SDE model converged at different values of σ that were signicantly different from zero while estimating bioavailability to be about 88%. The average RMSE for the ODE model wasfound to be 0.2980, and the lowest average RMSE for the SDE model was 0.2748 when σ was xed to 4. Both models estimated similar values for bioavailability, and the non-zero σ estimate implies that the inter-individual variability may be separated. However, the improvement in the predictive performance of the SDE model turned out to be rather small, compared to the ODE model.

Keywords
levodopa, parkinson's disease, pharmacokinetic model, stochastic modelling, PSM.
National Category
Probability Theory and Statistics
Research subject
Complex Systems – Microdata Analysis, General Microdata Analysis - methods
Identifiers
urn:nbn:se:du-28268 (URN)
Conference
The 40th Conference on Stochastic Processes and their Applications – SPA 2018, June 11-15 2018, Gothenburg
Available from: 2018-08-08 Created: 2018-08-08 Last updated: 2018-12-17Bibliographically approved
Skarin, A. & Alam, M. (2017). Reindeer habitat use in relation to two small wind farms, during preconstruction, construction, and operation. Ecology and Evolution, 7(11), 3870-3882
Open this publication in new window or tab >>Reindeer habitat use in relation to two small wind farms, during preconstruction, construction, and operation
2017 (English)In: Ecology and Evolution, ISSN 2045-7758, E-ISSN 2045-7758, Vol. 7, no 11, p. 3870-3882Article in journal (Refereed) Published
Abstract [en]

Worldwide there is a rush toward wind power development and its associated infrastructure. In Fennoscandia, large-scale wind farms comprising several hundred windmills are currently built in important grazing ranges used for Sámi reindeer husbandry. In this study, reindeer habitat use was assessed using reindeer fecal pellet group counts in relation to two relatively small wind farms, with 8 and 10 turbines, respectively. In 2009, 1,315 15-m2 plots were established and pellet groups were counted and cleaned from the plots. This was repeated once a year in May, during preconstruction, construction, and operation of the wind farms, covering 6 years (2009-2014) of reindeer habitat use in the area. We modeled the presence/absence of any pellets in a plot at both the local (wind farm site) and regional (reindeer calving to autumn range) scale with a hierarchical logistic regression, where spatial correlation was accounted for via random effects, using vegetation type, and the interaction between distance to wind turbine and time period as predictor variables. Our results revealed an absolute reduction in pellet groups by 66% and 86% around each wind farm, respectively, at local scale and by 61% at regional scale during the operation phase compared to the preconstruction phase. At the regional, scale habitat use declined close to the turbines in the same comparison. However, at the local scale, we observed increased habitat use close to the wind turbines at one of the wind farms during the operation phase. This may be explained by continued use of an important migration route close to the wind farm. The reduced use at the regional scale nevertheless suggests that there may be an overall avoidance of both wind farms during operation, but further studies of reindeer movement and behavior are needed to gain a better understanding of the mechanisms behind this suggested avoidance.

Keywords
Rangifer, Anthropogenic disturbance, Before after design, Pellet group count, Renewable energy development, Spatial correlation, Sámi reindeer husbandry
National Category
Other Agricultural Sciences
Research subject
Complex Systems – Microdata Analysis
Identifiers
urn:nbn:se:du-24825 (URN)10.1002/ece3.2941 (DOI)000403273000024 ()2-s2.0-85017692325 (Scopus ID)
Available from: 2017-05-08 Created: 2017-05-08 Last updated: 2018-01-13Bibliographically approved
Thomas, I., Alam, M., Bergquist, F., Senek, M., Nyholm, D. & Westin, J. (2016). Individual levodopa dosing suggestions based on a single dose test. In: : . Paper presented at 4th World Parkinson Congress, Portland, Oregon, September 20-23, 2016.
Open this publication in new window or tab >>Individual levodopa dosing suggestions based on a single dose test
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2016 (English)Conference paper, Poster (with or without abstract) (Other academic)
National Category
Other Computer and Information Science
Research subject
Complex Systems – Microdata Analysis
Identifiers
urn:nbn:se:du-23618 (URN)
Conference
4th World Parkinson Congress, Portland, Oregon, September 20-23, 2016
Projects
FLOAT
Available from: 2016-12-19 Created: 2016-12-19 Last updated: 2018-01-13Bibliographically approved
Thomas, I., Alam, M., Senek, M., Dag, N. & Westin, J. (2016). Minimizing levodopa titration period for Parkinson’s disease. In: : . Paper presented at 20th International Congress of Parkinson's Disease and Movement Disorders, June 19-23 2016, Berlin (pp. S633-S633). , 31(suppl. 2)
Open this publication in new window or tab >>Minimizing levodopa titration period for Parkinson’s disease
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2016 (English)Conference paper, Poster (with or without abstract) (Other academic)
National Category
Software Engineering
Research subject
Complex Systems – Microdata Analysis
Identifiers
urn:nbn:se:du-22627 (URN)10.1002/mds.26688 (DOI)
Conference
20th International Congress of Parkinson's Disease and Movement Disorders, June 19-23 2016, Berlin
Available from: 2016-07-18 Created: 2016-07-18 Last updated: 2018-01-10
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0002-3183-3756

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