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Galozy, A. & Nowaczyk, S. (2023). Information-gathering in latent bandits. Knowledge-Based Systems, 260, Article ID 110099.
Open this publication in new window or tab >>Information-gathering in latent bandits
2023 (English)In: Knowledge-Based Systems, ISSN 0950-7051, E-ISSN 1872-7409, Vol. 260, article id 110099Article in journal (Refereed) Published
Abstract [en]

In the latent bandit problem, the learner has access to reward distributions and – for the non-stationary variant – transition models of the environment. The reward distributions are conditioned on the arm and unknown latent states. The goal is to use the reward history to identify the latent state, allowing for the optimal choice of arms in the future. The latent bandit setting lends itself to many practical applications, such as recommender and decision support systems, where rich data allows the offline estimation of environment models with online learning remaining a critical component. Previous solutions in this setting always choose the highest reward arm according to the agent’s beliefs about the state, not explicitly considering the value of information-gathering arms. Such information-gathering arms do not necessarily provide the highest reward, thus may never be chosen by an agent that chooses the highest reward arms at all times.

In this paper, we present a method for information-gathering in latent bandits. Given particular reward structures and transition matrices, we show that choosing the best arm given the agent’s beliefs about the states incurs higher regret. Furthermore, we show that by choosing arms carefully, we obtain an improved estimation of the state distribution, and thus lower the cumulative regret through better arm choices in the future. Through theoretical analysis we show that the proposed method retains the sub-linear regret rate of previous methods while having much better problem dependent constants. We evaluate our method on both synthetic and real-world data sets, showing significant improvement in regret over state-of-the-art methods. © 2022 The Author(s). 

Place, publisher, year, edition, pages
Amsterdam: Elsevier, 2023
Keywords
Latent bandits, Information gathering, Non-stationary, Information directed sampling
National Category
Computer Sciences
Identifiers
urn:nbn:se:hh:diva-49833 (URN)10.1016/j.knosys.2022.110099 (DOI)2-s2.0-85143522327 (Scopus ID)
Funder
Vinnova, 2017-04617
Available from: 2023-01-16 Created: 2023-01-16 Last updated: 2023-11-29Bibliographically approved
Galozy, A. (2023). Mobile Health Interventions through Reinforcement Learning. (Doctoral dissertation). Halmstad: Halmstad University Press
Open this publication in new window or tab >>Mobile Health Interventions through Reinforcement Learning
2023 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

This thesis presents work conducted in the domain of sequential decision-making in general and Bandit problems in particular, tackling challenges from a practical and theoretical perspective, framed in the contexts of mobile Health. The early stages of this work have been conducted in the context of the project ``improving Medication Adherence through Person-Centred Care and Adaptive Interventions'' (iMedA) which aims to provide personalized adaptive interventions to hypertensive patients, supporting them in managing their medication regimen. The focus lies on inadequate medication adherence (MA), a pervasive issue where patients do not take their medication as instructed by their physician. The selection of individuals for intervention through secondary database analysis on Electronic Health Records (EHRs) was a key challenge and is addressed through in-depth analysis of common adherence measures, development of prediction models for MA, and discussions on limitations of such approaches for analyzing MA. Providing personalized adaptive interventions is framed in several bandit settings and addresses the challenge of delivering relevant interventions in environments where contextual information is unreliable and full of noise. Furthermore, the need for good initial policies is explored and improved in the latent-bandits setting, utilizing prior collected data to optimal selection the best intervention at every decision point. As the final concluding work, this thesis elaborates on the need for privacy and explores different privatization techniques in the form of noise-additive strategies using a realistic recommendation scenario.         

The contributions of the thesis can be summarised as follows: (1) Highlighting the issues encountered in measuring MA through secondary database analysis and providing recommendations to address these issues, (2) Investigating machine learning models developed using EHRs for MA prediction and extraction of common refilling patterns through EHRs, (3) formal problem definition for a novel contextual bandit setting with context uncertainty commonly encountered in Mobile Health and development of an algorithm designed for such environments. (4) Algorithmic improvements, equipping the agent with information-gathering capabilities for active action selection in the latent bandit setting, and (5) exploring important privacy aspects using a realistic recommender scenario.   

Place, publisher, year, edition, pages
Halmstad: Halmstad University Press, 2023. p. 56
Series
Halmstad University Dissertations ; 102
National Category
Computer Sciences
Research subject
Health Innovation, Information driven care
Identifiers
urn:nbn:se:hh:diva-52139 (URN)978-91-89587-17-5 (ISBN)978-91-89587-16-8 (ISBN)
Public defence
2023-12-15, S1002, Kristian IV:s väg 3, Halmstad, 13:00 (English)
Opponent
Supervisors
Available from: 2023-11-29 Created: 2023-11-29 Last updated: 2024-01-03Bibliographically approved
Jendle, J., Agvall, B., Galozy, A. & Adolfsson, P. (2022). Better Glycemic Control and Higher Use of Advanced Diabetes Technology in Age Group 0-17 Yrs Compared to 18-25 Yrs with Type 1 Diabetes. Paper presented at 15th International Conference on Advanced Technologies & Treatments for Diabetes (ATTD), Barcelona & Online, 27-30 April, 2022. Diabetes Technology & Therapeutics, 24(S1), A127-A127
Open this publication in new window or tab >>Better Glycemic Control and Higher Use of Advanced Diabetes Technology in Age Group 0-17 Yrs Compared to 18-25 Yrs with Type 1 Diabetes
2022 (English)In: Diabetes Technology & Therapeutics, ISSN 1520-9156, E-ISSN 1557-8593, Vol. 24, no S1, p. A127-A127Article in journal, Meeting abstract (Refereed) Published
Place, publisher, year, edition, pages
New Rochelle, NY: Mary Ann Liebert, 2022
National Category
Endocrinology and Diabetes
Identifiers
urn:nbn:se:hh:diva-52300 (URN)10.1089/dia.2022.2525.abstracts (DOI)000791212200303 ()
Conference
15th International Conference on Advanced Technologies & Treatments for Diabetes (ATTD), Barcelona & Online, 27-30 April, 2022
Available from: 2023-12-22 Created: 2023-12-22 Last updated: 2023-12-22Bibliographically approved
Jendle, J., Agvall, B., Galozy, A. & Adolfsson, P. (2022). Patterns and Predictors Associated With Long-Term Glycemic Control in Pediatric and Young Adult Patients with Type 1 Diabetes. Journal of Diabetes Science and Technology, 17(5), 1243-1251
Open this publication in new window or tab >>Patterns and Predictors Associated With Long-Term Glycemic Control in Pediatric and Young Adult Patients with Type 1 Diabetes
2022 (English)In: Journal of Diabetes Science and Technology, E-ISSN 1932-2968, Vol. 17, no 5, p. 1243-1251Article in journal (Refereed) Published
Abstract [en]

Background: The development of diabetes technology is rapid and requires education and resources to be successfully implemented in diabetes care management.

Method: In an observational study, we evaluated the use of advanced diabetes technology, resource utilization, and glycemic control. The study population was 725 individuals with type 1 diabetes (T1D) living in Region Halland, Sweden. The study cohort was followed for 7 years between 2013 and 2019.

Results: Children aged 0 to 17 years were associated with significantly better glucose control than young adults aged 18 to 25 years. The mean HbA1c in children and young adults was 53 mmol/mol (7.0%) compared to 61 mmol/mol (7.7%) (P <.0001), respectively. Comorbidities such as attention deficit hyperactivity disorder (ADHD), autism, and coelic disease were associated with higher HbA1c. All groups, regardless of age and comorbidity, showed a positive effect on glucose control after visiting a dietitian or psychologist. Differences were found between the age groups in terms of more use of advanced diabetes technology and more frequent visits to a physician in children compared to young adults.

Conclusions: More frequent visits to physicians, and a visit to dietitians, and psychologists were associated with improved glucose control in individuals with T1D 0 to 25 years. Increased resources, including access to more advanced technologies, may be required in young adults with T1D. © 2022 Diabetes Technology Society.

Place, publisher, year, edition, pages
Thousand Oaks, CA: Sage Publications, 2022
Keywords
CGM, diabetes management, diabetes technology, type 1 diabetes
National Category
Pediatrics
Identifiers
urn:nbn:se:hh:diva-48931 (URN)10.1177/19322968221096423 (DOI)35549729 (PubMedID)2-s2.0-85130466937 (Scopus ID)
Available from: 2022-12-16 Created: 2022-12-16 Last updated: 2023-11-28Bibliographically approved
Galozy, A. (2021). Data-driven personalized healthcare: Towards personalized interventions via reinforcement learning for Mobile Health. (Licentiate dissertation). Halmstad: Halmstad University Press
Open this publication in new window or tab >>Data-driven personalized healthcare: Towards personalized interventions via reinforcement learning for Mobile Health
2021 (English)Licentiate thesis, comprehensive summary (Other academic)
Abstract [en]

Medical and technological advancement in the last century has led to the unprecedented increase of the populace's quality of life and lifespan. As a result, an ever-increasing number of people live with chronic health conditions that require long-term treatment, resulting in increased healthcare costs and managerial burden to the healthcare provider. This increase in complexity can lead to ineffective decision-making and reduce care quality for the individual while increasing costs. One promising direction to tackle these issues is the active involvement of the patient in managing their care. Particularly for chronic diseases, where ongoing support is often required, patients must understand their illness and be empowered to manage their care. With the advent of smart devices such as smartphones, it is easier than ever to provide personalised digital interventions to patients, help them manage their treatment in their daily lives, and raise awareness about their illness. If such new approaches are to succeed, scalability is necessary, and solutions are needed that can act autonomously without costly human intervention. Furthermore, solutions should exhibit adaptability to the changing circumstances of an individual patient's health, needs and goals. Through the ongoing digitisation of healthcare, we are presented with the unique opportunity to develop cost-effective and scalable solutions through Artificial Intelligence (AI).

This thesis presents work that we conducted as part of the project improving Medication Adherence through Person-Centered Care and Adaptive Interventions (iMedA) that aims to provide personalised adaptive interventions to hypertensive patients, supporting them in managing their medication regiment. The focus lies on inadequate medication adherence (MA), a pervasive issue where patients do not take their medication as instructed by their physician. The selection of individuals for intervention through secondary database analysis on Electronic Health Records (EHRs) was a key challenge and is addressed through in-depth analysis of common adherence measures, development of prediction models for MA and discussions on limitations of such approaches for analysing MA. Furthermore, providing personalised adaptive interventions is framed in the contextual bandit setting and addresses the challenge of delivering relevant interventions in environments where contextual information is significantly corrupted.       

The contributions of the thesis can be summarised as follows: (1) Highlighting the issues encountered in measuring MA through secondary database analysis and providing recommendations to address these issues, (2) Investigating machine learning models developed using EHRs for MA prediction and extraction of common refilling patterns through EHRs and (3) formal problem definition for a novel contextual bandit setting with context uncertainty commonly encountered in Mobile Health and development of an algorithm designed for such environments.  

Place, publisher, year, edition, pages
Halmstad: Halmstad University Press, 2021. p. 55
Series
Halmstad University Dissertations ; 79
Keywords
Information Driven Care, Electronic Health Records, Machine Learning, Reinforcement Learning
National Category
Health Care Service and Management, Health Policy and Services and Health Economy Signal Processing
Identifiers
urn:nbn:se:hh:diva-44091 (URN)9789188749666 (ISBN)9789188749673 (ISBN)
Presentation
2021-04-19, Wigforss, Visionen, Kristian IV:s väg 3, Halmstad, 14:00 (English)
Opponent
Supervisors
Available from: 2021-04-08 Created: 2021-04-01 Last updated: 2022-03-11Bibliographically approved
Etminani, K., Göransson, C., Galozy, A., Norell Pejner, M. & Nowaczyk, S. (2021). Improving Medication Adherence Through Adaptive Digital Interventions (iMedA) in Patients With Hypertension: Protocol for an Interrupted Time Series Study. JMIR Research Protocols, 10(5), Article ID e24494.
Open this publication in new window or tab >>Improving Medication Adherence Through Adaptive Digital Interventions (iMedA) in Patients With Hypertension: Protocol for an Interrupted Time Series Study
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2021 (English)In: JMIR Research Protocols, E-ISSN 1929-0748, Vol. 10, no 5, article id e24494Article in journal (Refereed) Published
Abstract [en]

Background: There is a strong need to improve medication adherence (MA) for individuals with hypertension in order to reduce long-term hospitalization costs. We believe this can be achieved through an artificial intelligence agent that helps the patient in understanding key individual adherence risk factors and designing an appropriate intervention plan. The incidence of hypertension in Sweden is estimated at approximately 27%. Although blood pressure control has increased in Sweden, barely half of the treated patients achieved adequate blood pressure levels. It is a major risk factor for coronary heart disease and stroke as well as heart failure. MA is a key factor for good clinical outcomes in persons with hypertension.

Objective: The overall aim of this study is to design, develop, test, and evaluate an adaptive digital intervention called iMedA, delivered via a mobile app to improve MA, self-care management, and blood pressure control for persons with hypertension.

Methods: The study design is an interrupted time series. We will collect data on a daily basis, 14 days before, during 6 months of delivering digital interventions through the mobile app, and 14 days after. The effect will be analyzed using segmented regression analysis. The participants will be recruited in Region Halland, Sweden. The design of the digital interventions follows the just-in-time adaptive intervention framework. The primary (distal) outcome is MA, and the secondary outcome is blood pressure. The design of the digital intervention is developed based on a needs assessment process including a systematic review, focus group interviews, and a pilot study, before conducting the longitudinal interrupted time series study.

Results: The focus groups of persons with hypertension have been conducted to perform the needs assessment in a Swedish context. The design and development of digital interventions are in progress, and the interventions are planned to be ready in November 2020. Then, the 2-week pilot study for usability evaluation will start, and the interrupted time series study, which we plan to start in February 2021, will follow it.

Conclusions: We hypothesize that iMedA will improve medication adherence and self-care management. This study could illustrate how self-care management tools can be an additional (digital) treatment support to a clinical one without increasing burden on health care staff. © Kobra Etminani, Carina Göransson, Alexander Galozy, Margaretha Norell Pejner, Sławomir Nowaczyk.

Place, publisher, year, edition, pages
Toronto: JMIR, 2021
Keywords
medication adherence, hypertension, digital intervention, mHealth, artificial intelligence
National Category
Health Sciences Nursing
Identifiers
urn:nbn:se:hh:diva-44275 (URN)10.2196/24494 (DOI)000658257400006 ()33978593 (PubMedID)2-s2.0-85106034833 (Scopus ID)
Funder
Vinnova, 2017-04617
Available from: 2021-05-14 Created: 2021-05-14 Last updated: 2024-01-17Bibliographically approved
Valle, F., Galozy, A., Ashfaq, A., Etminani, K., Vinel, A. & Cooney, M. (2021). Lonely road: speculative challenges for a social media robot aimed to reduce driver loneliness. In: Workshop Proceedings of the 15th International AAAI Conference on Web and Social Media: . Paper presented at MAISoN 2021. 6th International Workshop on Mining Actionable Insights from Social Networks – Special Edition on Healthcare Social Analytics & The 15th International AAAI Conference on Web and Social Media (ICWSM 2021), Virtual, June 7, 2021.
Open this publication in new window or tab >>Lonely road: speculative challenges for a social media robot aimed to reduce driver loneliness
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2021 (English)In: Workshop Proceedings of the 15th International AAAI Conference on Web and Social Media, 2021Conference paper, Published paper (Refereed)
Abstract [en]

Driver monitoring is expected to contribute greatly to safety in nascent smart cities, also in complex, mixed-traffic scenarios with autonomous vehicles (AVs), vulnerable road users (VRUs), and manually driven vehicles. Until now, one focus has been on detecting bio signals during the relatively short time when a person is inside a vehicle; but, life outside of the vehicle can also affect driving. For example, loneliness, depression, and sleep-deprivation, which might be difficult to detect in time, can increase the risk of accidents–raising possibilities for new and alternative intervention strategies. Thus, the current conceptual paper explores one idea for how continuous care could be provided to improve drivers’ mental states; in particular, the idea of a “robot” that could positively affect a driver’s health through interactions supported by social media mining on Facebook. A speculative design approach is used to present some potential challenges and solutions in regard to a robot’s interaction strategy, user modeling, and ethics. For example, to address how to generate appropriate robot activities and mitigate the risk of damage to the driver, a hybrid neuro-symbolic recognition strategy leveraging stereotypical and self-disclosed information is described. Thereby, the aim of this conceptual paper is to navigate through some “memories” of one possible future, toward stimulating ideation and discussion within the increasingly vital area of safety in smart cities Copyright © 2021

National Category
Mechanical Engineering
Identifiers
urn:nbn:se:hh:diva-45236 (URN)10.36190/2021.72 (DOI)
Conference
MAISoN 2021. 6th International Workshop on Mining Actionable Insights from Social Networks – Special Edition on Healthcare Social Analytics & The 15th International AAAI Conference on Web and Social Media (ICWSM 2021), Virtual, June 7, 2021
Note

"Welcome to the 6th International workshop on Mining Actionable Insights from Social Networks (MAISoN 2021) - Special Edition on Healthcare Social Analytics. The workshop takes place on Jun 7 and is co-located with The 15th The International AAAI Conference on Web and Social Media (ICWSM 2021), which will be a fully virtual conference"

Available from: 2021-07-02 Created: 2021-07-02 Last updated: 2021-09-09Bibliographically approved
Galozy, A., Nowaczyk, S., Pinheiro Sant'Anna, A., Ohlsson, M. & Lingman, M. (2020). Pitfalls of medication adherence approximation through EHR and pharmacy records: Definitions, data and computation. International Journal of Medical Informatics, 136, Article ID 104092.
Open this publication in new window or tab >>Pitfalls of medication adherence approximation through EHR and pharmacy records: Definitions, data and computation
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2020 (English)In: International Journal of Medical Informatics, ISSN 1386-5056, E-ISSN 1872-8243, Vol. 136, article id 104092Article in journal (Refereed) Published
Abstract [en]

Background and purpose: Patients’ adherence to medication is a complex, multidimensional phenomenon. Dispensation data and electronic health records are used to approximate medication-taking through refill adherence. In-depth discussions on the adverse effects of data quality and computational differences are rare. The purpose of this article is to evaluate the impact of common pitfalls when computing medication adherence using electronic health records.

Procedures: We point out common pitfalls associated with the data and operationalization of adherence measures. We provide operational definitions of refill adherence and conduct experiments to determine the effect of the pitfalls on adherence estimations. We performed statistical significance testing on the impact of common pitfalls using a baseline scenario as reference.

Findings: Slight changes in definition can significantly skew refill adherence estimates. Pickup patterns cause significant disagreement between measures and the commonly used proportion of days covered. Common data related issues had a small but statistically significant (p < 0.05) impact on population-level and significant effect on individual cases.

Conclusion: Data-related issues encountered in real-world administrative databases, which affect various operational definitions of refill adherence differently, can significantly skew refill adherence values, leading to false conclusions about adherence, particularly when estimating adherence for individuals. © 2020 The Authors. Published by Elsevier B.V. 

Place, publisher, year, edition, pages
Shannon: Elsevier, 2020
Keywords
Medication refill adherence, Electronic health records, Data quality, Pitfalls
National Category
Other Medical Engineering
Identifiers
urn:nbn:se:hh:diva-41712 (URN)10.1016/j.ijmedinf.2020.104092 (DOI)32062562 (PubMedID)2-s2.0-85079281579 (Scopus ID)
Funder
Vinnova, 2017-04617
Note

Other funding: Health Technology Center and CAISR at Halmstad University and Halland's Hospital

Available from: 2020-02-25 Created: 2020-02-25 Last updated: 2023-11-29Bibliographically approved
Galozy, A. & Nowaczyk, S. (2020). Prediction and pattern analysis of medication refill adherence through electronic health records and dispensation data. Journal of Biomedical Informatics: X, 6-7, Article ID 100075.
Open this publication in new window or tab >>Prediction and pattern analysis of medication refill adherence through electronic health records and dispensation data
2020 (English)In: Journal of Biomedical Informatics: X, E-ISSN 2590-177X, Vol. 6-7, article id 100075Article in journal (Refereed) Published
Abstract [en]

Background and purpose

Low adherence to medication in chronic disease patients leads to increased morbidity, mortality, and healthcare costs. The widespread adoption of electronic prescription and dispensation records allows a more comprehensive overview of medication utilization. In combination with electronic health records (EHR), such data provides new opportunities for identifying patients at risk of nonadherence and provide more targeted and effective interventions. The purpose of this article is to study the predictability of medication adherence for a cohort of hypertensive patients, focusing on healthcare utilization factors under various predictive scenarios. Furthermore, we discover common proportion of days covered patterns (PDC-patterns) for patients with index prescriptions and simulate medication-taking behaviours that might explain observed patterns.

Procedures

We predict refill adherence focusing on factors of healthcare utilization, such as visits, prescription information and demographics of patient and prescriber. We train models with machine learning algorithms, using four different data splits: stratified random, patient, temporal forward prediction with and without index patients. We extract frequent, two-year long PDC-patterns using K-means clustering and investigate five simple models of medication-taking that can generate such PDC-patterns.

Findings

Model performance varies between data splits (AUC test set: 0.77–0.89). Including historical information increases the performance slightly in most cases (approx. 1–2% absolute AUC uplift). Models show low predictive performance (AUC test set: 0.56–0.66) on index-prescriptions and patients with sudden drops in PDC (Recall: 0.58–0.63). We find 21 distinct two-year PDC-patterns, ranging from good adherence to intermittent gaps and early discontinuation in the first or second year. Simulations show that observed PDC-patterns can only be explained by specific medication consumption behaviours.

Conclusions

Prediction models developed using EHR exhibit bias towards patients with high healthcare utilization. Even though actual medication-taking is not observable, consumption patterns may not be as arbitrary, provided that medication refilling and consumption is linked.  © 2020 The Authors. Published by Elsevier Inc.

Place, publisher, year, edition, pages
New York, NY: Elsevier, 2020
Keywords
Medication refill adherence, Electronic health records, Simulation, Prediction, Refill patterns
National Category
Signal Processing Pharmacology and Toxicology Computer Sciences
Identifiers
urn:nbn:se:hh:diva-43529 (URN)10.1016/j.yjbinx.2020.100075 (DOI)2-s2.0-85087509892 (Scopus ID)
Funder
Vinnova
Note

Funding: Vinnova, Health Technology Center and CAISR at Halmstad University and Hallands Hospital for financing the research work under the project iMedA [Grant No.: 2017-04617]. 

Available from: 2020-11-26 Created: 2020-11-26 Last updated: 2023-11-29Bibliographically approved
Galozy, A., Nowaczyk, S. & Pinheiro Sant'Anna, A. (2019). Towards Understanding ICU Treatments Using Patient Health Trajectories. In: Artificial Intelligence in Medicine: Knowledge Representation and Transparent and Explainable Systems: Revised Selected Papers. Paper presented at AIME 2019 International Workshops, KR4HC/ProHealth and TEAAM, Poznan, Poland, June 26–29, 2019 (pp. 67-81). Heidelberg: Springer
Open this publication in new window or tab >>Towards Understanding ICU Treatments Using Patient Health Trajectories
2019 (English)In: Artificial Intelligence in Medicine: Knowledge Representation and Transparent and Explainable Systems: Revised Selected Papers, Heidelberg: Springer, 2019, p. 67-81Conference paper, Published paper (Refereed)
Abstract [en]

Overtreatment or mistreatment of patients is a phenomenon commonly encountered in health care and especially in the Intensive Care Unit (ICU) resulting in increased morbidity and mortality. We explore the MIMIC-III intensive care unit database and conduct experiments on an interpretable feature space based on the fusion of severity subscores, commonly used to predict mortality in an ICU setting. Clustering of medication and procedure context vectors based on a semantic representation has been performed to find common and individual treatment patterns. Two-day patient health state trajectories of a cohort of congestive heart failure patients are clustered and correlated with the treatment and evaluated based on an increase or reduction of probability of mortality on the second day of stay. Experimental results show differences in treatments and outcomes and the potential for using patient health state trajectories as a starting point for further evaluation of medical treatments and interventions. © Springer Nature Switzerland AG 2019.

Place, publisher, year, edition, pages
Heidelberg: Springer, 2019
Series
Lecture Notes in Computer Science, ISSN 1611-3349
Keywords
Clustering, Electronic Health Records, Health trajectory, Intensive care treatments, Knowledge representation, Medical computing, Medical information systems, Patient treatment, Semantics, Trajectories, Congestive heart failures, Context vector, Electronic health record, Intensive care, Medical treatment, Patient health, Semantic representation, Intensive care units
National Category
Health Care Service and Management, Health Policy and Services and Health Economy Computer Sciences
Identifiers
urn:nbn:se:hh:diva-41538 (URN)10.1007/978-3-030-37446-4_6 (DOI)000654170100006 ()2-s2.0-85078449305 (Scopus ID)978-3-030-37445-7 (ISBN)978-3-030-37446-4 (ISBN)
Conference
AIME 2019 International Workshops, KR4HC/ProHealth and TEAAM, Poznan, Poland, June 26–29, 2019
Available from: 2020-02-04 Created: 2020-02-04 Last updated: 2023-08-21Bibliographically approved
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0002-7453-9186

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