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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., Memedi, M., Westin, J. & Nyholm, D. (2019). The effect of continuous levodopa treatment during the afternoon hours. Acta Neurologica Scandinavica, 139(1), 70-75
Open this publication in new window or tab >>The effect of continuous levodopa treatment during the afternoon hours
2019 (English)In: Acta Neurologica Scandinavica, ISSN 0001-6314, E-ISSN 1600-0404, Vol. 139, no 1, p. 70-75Article in journal (Refereed) Published
Abstract [en]

OBJECTIVE: The aim of this retrospective study was to investigate if patients with PD, who are treated with levodopa-carbidopa intestinal gel (LCIG), clinically worsen during the afternoon hours and if so, to evaluate whether this occurs in all LCIG-treated patients or in a sub-group of patients.

METHODS: Three published studies were identified and included in the analysis. All studies provided individual response data assessed on the treatment response scale (TRS) and patients were treated with continuous LCIG. Ninety-eight patients from the three studies fulfilled the criteria. T-tests were performed to find differences on the TRS values between the morning and the afternoon hours, linear mixed effect models were fitted on the afternoon hours' evaluations to find trends of wearing-off, and patients were classified into three TRS categories (meaningful increase in TRS, meaningful decrease in TRS, non -meaningful increase or decrease).

RESULTS: In all three studies significant statistical differences were found between the morning TRS values and the afternoon TRS values (p-value <= 0.001 in all studies). The linear mixed effect models had significant negative coefficients for time in two studies, and 48 out of 98 patients (49%) showed a meaningful decrease of TRS during the afternoon hours.

CONCLUSION: The results from all studies were consistent, both in the proportion of patients in the three groups and the value of TRS decrease in the afternoon hours. Based on these findings there seems to be a group of patients with predictable "off" behavior in the later parts of the day. This article is protected by copyright. All rights reserved.

Keywords
diurnal motor fluctuation; infusion pumps; levodopa; Parkinson disease
National Category
Medical Engineering Probability Theory and Statistics
Research subject
Complex Systems – Microdata Analysis
Identifiers
urn:nbn:se:du-28478 (URN)10.1111/ane.13020 (DOI)000452067700007 ()30180267 (PubMedID)2-s2.0-85053714059 (Scopus ID)
Available from: 2018-09-11 Created: 2018-09-11 Last updated: 2019-02-06Bibliographically 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
Johansson, D., Ericsson, A., Johansson, A., Medvedev, A., Nyholm, D., Ohlsson, F., . . . Westin, J. (2018). Individualization of levodopa treatment using a microtablet dispenser and ambulatory accelerometry. CNS Neuroscience & Therapeutics, 24(5), 439-447
Open this publication in new window or tab >>Individualization of levodopa treatment using a microtablet dispenser and ambulatory accelerometry
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2018 (English)In: CNS Neuroscience & Therapeutics, ISSN 1755-5930, E-ISSN 1755-5949, Vol. 24, no 5, p. 439-447Article in journal (Refereed) Published
Abstract [en]

Aim

This 4‐week open‐label observational study describes the effect of introducing a microtablet dose dispenser and adjusting doses based on objective free‐living motor symptom monitoring in individuals with Parkinson's disease (PD).

Methods

Twenty‐eight outpatients with PD on stable levodopa treatment with dose intervals of ≤4 hour had their daytime doses of levodopa replaced with levodopa/carbidopa microtablets, 5/1.25 mg (LC‐5) delivered from a dose dispenser device with programmable reminders. After 2 weeks, doses were adjusted based on ambulatory accelerometry and clinical monitoring.

Results

Twenty‐four participants completed the study per protocol. The daily levodopa dose was increased by 15% (112 mg, < 0.001) from period 1 to 2, and the dose interval was reduced by 12% (22 minutes, P = 0.003). The treatment adherence to LC‐5 was high in both periods. The MDS‐UPDRS parts II and III, disease‐specific quality of life (PDQ‐8), wearing‐off symptoms (WOQ‐19), and nonmotor symptoms (NMS Quest) improved after dose titration, but the generic quality‐of‐life measure EQ‐5D‐5L did not. Blinded expert evaluation of accelerometry results demonstrated improvement in 60% of subjects and worsening in 25%.

Conclusions

The introduction of a levodopa microtablet dispenser and accelerometry aided dose adjustments improve PD symptoms and quality of life in the short term.

Keywords
Parkinson's disease; accelerometry; dose titration; microtablets; observational study
National Category
Medical Engineering
Research subject
Complex Systems – Microdata Analysis
Identifiers
urn:nbn:se:du-27206 (URN)10.1111/cns.12807 (DOI)000430058800008 ()29652438 (PubMedID)2-s2.0-85041030284 (Scopus ID)
Available from: 2018-02-14 Created: 2018-02-14 Last updated: 2018-05-03Bibliographically approved
Schiavella, M., Pelagatti, M., Westin, J., Lepore, G. & Cherubini, P. (2018). Profiling online poker players: Are executive functions correlated with poker ability and problem gambling?. Journal of Gambling Studies, 34(3), 823-851
Open this publication in new window or tab >>Profiling online poker players: Are executive functions correlated with poker ability and problem gambling?
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2018 (English)In: Journal of Gambling Studies, ISSN 1050-5350, E-ISSN 1573-3602, Vol. 34, no 3, p. 823-851Article in journal (Refereed) Published
Abstract [en]

Poker playing and responsible gambling both entail the use of the executive functions (EF), which are higher-level cognitive abilities. This study investigated if online poker players of different ability showed different performances in their EF and if so, which functions were the most discriminating for their playing ability. Furthermore, it assessed if the EF performance was correlated to the quality of gambling, according to self-reported questionnaires (PGSI, SOGS, GRCS). Three poker experts evaluated anonymized poker hand history files and, then, a trained professional administered an extensive neuropsychological test battery. Data analysis determined which variables of the tests correlated with poker ability and gambling quality scores. The highest correlations between EF test results and poker ability and between EF test results and gambling quality assessment showed that mostly different clusters of executive functions characterize the profile of the strong(er) poker player and those ones of the problem gamblers (PGSI and SOGS) and the one of the cognitions related to gambling (GRCS). Taking into consideration only the variables overlapping between PGSI and SOGS, we found some key predictive factors for a more risky and harmful online poker playing: a lower performance in the emotional intelligence competences (Emotional Quotient inventory Short) and, in particular, those grouped in the Intrapersonal scale (emotional self-awareness, assertiveness, self-regard, independence and self-actualization).

Keywords
Executive functions, GRCS, Online poker, PGSI, Problem gambling, SOGS
National Category
Other Computer and Information Science
Research subject
Complex Systems – Microdata Analysis
Identifiers
urn:nbn:se:du-26938 (URN)10.1007/s10899-017-9741-z (DOI)000441533700013 ()29330827 (PubMedID)2-s2.0-85054893588 (Scopus ID)
Available from: 2018-01-15 Created: 2018-01-15 Last updated: 2018-10-29Bibliographically 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
Senek, M., Aquilonius, S.-M., Askmark, H., Bergquist, F., Constantinescu, R., Ericsson, A., . . . Nyholm, D. (2017). Levodopa/carbidopa microtablets in Parkinson's disease: a study of pharmacokinetics and blinded motor assessment. European Journal of Clinical Pharmacology, 73(5), 563-571
Open this publication in new window or tab >>Levodopa/carbidopa microtablets in Parkinson's disease: a study of pharmacokinetics and blinded motor assessment
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2017 (English)In: European Journal of Clinical Pharmacology, ISSN 0031-6970, E-ISSN 1432-1041, Vol. 73, no 5, p. 563-571Article in journal (Refereed) Published
Abstract [en]

BACKGROUND: Motor function assessments with rating scales in relation to the pharmacokinetics of levodopa may increase the understanding of how to individualize and fine-tune treatments.

OBJECTIVES: This study aimed to investigate the pharmacokinetic profiles of levodopa-carbidopa and the motor function following a single-dose microtablet administration in Parkinson's disease.

METHODS: This was a single-center, open-label, single-dose study in 19 patients experiencing motor fluctuations. Patients received 150% of their individual levodopa equivalent morning dose in levodopa-carbidopa microtablets. Blood samples were collected at pre-specified time points. Patients were video recorded and motor function was assessed with six UPDRS part III motor items, dyskinesia score, and the treatment response scale (TRS), rated by three blinded movement disorder specialists.

RESULTS: AUC0-4/dose and C max/dose for levodopa was found to be higher in Parkinson's disease patients compared with healthy subjects from a previous study, (p = 0.0008 and p = 0.026, respectively). The mean time to maximum improvement in sum of six UPDRS items score was 78 min (±59) (n = 16), and the mean time to TRS score maximum effect was 54 min (±51) (n = 15). Mean time to onset of dyskinesia was 41 min (±38) (n = 13).

CONCLUSIONS: In the PD population, following levodopa/carbidopa microtablet administration in fasting state, the Cmax and AUC0-4/dose were found to be higher compared with results from a previous study in young, healthy subjects. A large between subject variability in response and duration of effect was observed, highlighting the importance of a continuous and individual assessment of motor function in order to optimize treatment effect.

Keywords
Levodopa; Parkinson’s disease; Pharmacodynamics; Pharmacokinetics
National Category
Clinical Medicine
Research subject
Complex Systems – Microdata Analysis
Identifiers
urn:nbn:se:du-23985 (URN)10.1007/s00228-017-2196-4 (DOI)000399175100006 ()28101657 (PubMedID)
Funder
VINNOVA
Available from: 2017-01-26 Created: 2017-01-26 Last updated: 2017-07-25Bibliographically approved
Aghanavesi, S., Memedi, M., Dougherty, M., Nyholm, D. & Westin, J. (2017). Verification of a method for measuring Parkinson’s disease related temporal irregularity in spiral drawings. Sensors, 17(10), Article ID 2341.
Open this publication in new window or tab >>Verification of a method for measuring Parkinson’s disease related temporal irregularity in spiral drawings
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2017 (English)In: Sensors, ISSN 1424-8220, E-ISSN 1424-8220, Vol. 17, no 10, article id 2341Article in journal (Refereed) Published
Abstract [en]

Parkinson's disease (PD) is a progressive movement disorder caused by the death of dopamine-producing cells in the midbrain. There is a need for frequent symptom assessment, since the treatment needs to be individualized as the disease progresses. The aim of this paper was to verify and further investigate the clinimetric properties of an entropy-based method for measuring PD-related upper limb temporal irregularities during spiral drawing tasks. More specifically, properties of a temporal irregularity score (TIS) for patients at different stages of PD, and medication time points were investigated. Nineteen PD patients and 22 healthy controls performed repeated spiral drawing tasks on a smartphone. Patients performed the tests before a single levodopa dose and at specific time intervals after the dose was given. Three movement disorder specialists rated videos of the patients based on the unified PD rating scale (UPDRS) and the Dyskinesia scale. Differences in mean TIS between the groups of patients and healthy subjects were assessed. Test-retest reliability of the TIS was measured. The ability of TIS to detect changes from baseline (before medication) to later time points was investigated. Correlations between TIS and clinical rating scores were assessed. The mean TIS was significantly different between healthy subjects and patients in advanced groups (p-value = 0.02). Test-retest reliability of TIS was good with Intra-class Correlation Coefficient of 0.81. When assessing changes in relation to treatment, TIS contained some information to capture changes from Off to On and wearing off effects. However, the correlations between TIS and clinical scores (UPDRS and Dyskinesia) were weak. TIS was able to differentiate spiral drawings drawn by patients in an advanced stage from those drawn by healthy subjects, and TIS had good test-retest reliability. TIS was somewhat responsive to single-dose levodopa treatment. Since TIS is an upper limb high-frequency-based measure, it cannot be detected during clinical assessment.

Keywords
Parkinson's disease; smartphone; spiral tests; temporal irregularity; timing variability; motor assessment; approximate entropy; complexity
National Category
Other Medical Engineering Probability Theory and Statistics
Research subject
Complex Systems – Microdata Analysis
Identifiers
urn:nbn:se:du-29318 (URN)10.3390/s17102341 (DOI)29027941 (PubMedID)2-s2.0-85032855199 (Scopus ID)
Available from: 2019-01-21 Created: 2019-01-21 Last updated: 2019-02-27Bibliographically approved
Memedi, M., Aghanavesi, S. & Westin, J. (2016). A method for measuring Parkinson's disease related temporal irregularity in spiral drawings. In: 2016 IEEE International Conference on Biomedical and Health Informatics: . Paper presented at Biomedical and Health Informatics 2016, Las Vegas, 24-27 February (pp. 410-413).
Open this publication in new window or tab >>A method for measuring Parkinson's disease related temporal irregularity in spiral drawings
2016 (English)In: 2016 IEEE International Conference on Biomedical and Health Informatics, 2016, p. 410-413Conference paper, Published paper (Refereed)
Abstract [en]

The objective of this paper was to develop and evaluate clinimetric properties of a method for measuring Parkinson's disease (PD)-related temporal irregularities using digital spiral analysis. In total, 108 (98 patients in different stages of PD and 10 healthy elderly subjects) performed repeated spiral drawing tasks in their home environments using a touch screen device. A score was developed for representing the amount of temporal irregularity during spiral drawing tasks, using Approximate Entropy (ApEn) technique. In addition, two previously published spiral scoring methods were adapted and their scores were analyzed. The mean temporal irregularity score differed significantly between healthy elderly subjects and advanced PD patients (P<0.005). The ApEn-based method had a better responsiveness and test-retest reliability when compared to the other two methods. In contrast to the other methods, the mean scores of the ApEn-based method improved significantly during a 3 year clinical study, indicating a possible impact of pathological basal ganglia oscillations in temporal control during spiral drawing tasks. In conclusion, the ApEn-based method could be used for differentiating between patients in different stages of PD and healthy subjects. The responsiveness and test-retest reliability were good for the ApEn-based method indicating that this method is useful for measuring upper limb temporal irregularity at a micro-level.

National Category
Computer Systems Signal Processing
Research subject
Complex Systems – Microdata Analysis, FLOAT - Flexible Levodopa Optimizing Assistive Technology
Identifiers
urn:nbn:se:du-21183 (URN)10.1109/BHI.2016.7455921 (DOI)000381398000102 ()978-1-5090-2455-1 (ISBN)
Conference
Biomedical and Health Informatics 2016, Las Vegas, 24-27 February
Available from: 2016-03-01 Created: 2016-03-01 Last updated: 2019-02-27Bibliographically approved
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0003-0403-338X

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