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Boodaghian Asl, A., Raghothama, J., Darwich, A. S. & Meijer, S. (2024). A hybrid modeling approach to simulate complex systems and classify behaviors. NETWORK MODELING AND ANALYSIS IN HEALTH INFORMATICS AND BIOINFORMATICS, 13(1), Article ID 9.
Open this publication in new window or tab >>A hybrid modeling approach to simulate complex systems and classify behaviors
2024 (English)In: NETWORK MODELING AND ANALYSIS IN HEALTH INFORMATICS AND BIOINFORMATICS, ISSN 2192-6662, Vol. 13, no 1, article id 9Article in journal (Refereed) Published
Abstract [en]

Many important systems, both natural and artificial, are complex in nature, and models and simulations are one of the main instruments to study them. In this paper, we present an approach where a complex social system is represented at a high level of abstraction as a network, thereby addressing several challenges such as quantification, intervention, adaptation and validation. The network represents the factors that influence the mental health and wellbeing in children and young people. In this article, we present an approach that links a system dynamics simulation to simulate the network and ranking algorithms to measure the vertices' behaviors. The network is enhanced by adding edge strengths in the form of correlations between vertices (established through literature). Such an approach allows us to exploit the network structure to qualify and quantify the vertices of the network, to overlay different processes over the network topology, to add and remove new vertices, and therefore interact dynamically. This in turn allows for the qualification of vertices' importance and network resilience. System dynamics simulation allows for policy analysis, where different scenarios are analyzed by stimulating a set of vertices and the effect over the network is observed. This approach allows for an abstract, flexible, yet comprehensive way of analyzing a complex social network at any scale.

Place, publisher, year, edition, pages
Springer Nature, 2024
Keywords
Simulation and modeling, Ranking algorithm, Complex systems, Mental wellbeing
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:kth:diva-345028 (URN)10.1007/s13721-024-00446-5 (DOI)001186407300001 ()2-s2.0-85188097928 (Scopus ID)
Note

QC 20240408

Available from: 2024-04-08 Created: 2024-04-08 Last updated: 2024-04-08Bibliographically approved
Marzano, L., Darwich, A. S., Raghothama, J., Sven, L., Falk, N., Bodeby, P. & Meijer, S. (2024). Diagnosing an overcrowded emergency department from its Electronic Health Records. Scientific Reports, 14(1), Article ID 9955.
Open this publication in new window or tab >>Diagnosing an overcrowded emergency department from its Electronic Health Records
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2024 (English)In: Scientific Reports, E-ISSN 2045-2322, Vol. 14, no 1, article id 9955Article in journal (Refereed) Published
Abstract [en]

Emergency department overcrowding is a complex problem that persists globally. Data of visits constitute an opportunity to understand its dynamics. However, the gap between the collected information and the real-life clinical processes, and the lack of a whole-system perspective, still constitute a relevant limitation. An analytical pipeline was developed to analyse one-year of production data following the patients that came from the ED (n = 49,938) at Uppsala University Hospital (Uppsala, Sweden) by involving clinical experts in all the steps of the analysis. The key internal issues to the ED were the high volume of generic or non-specific diagnoses from non-urgent visits, and the delayed decision regarding hospital admission caused by several imaging assessments and lack of hospital beds. Furthermore, the external pressure of high frequent re-visits of geriatric, psychiatric, and patients with unspecified diagnoses dramatically contributed to the overcrowding. Our work demonstrates that through analysis of production data of the ED patient flow and participation of clinical experts in the pipeline, it was possible to identify systemic issues and directions for solutions. A critical factor was to take a whole systems perspective, as it opened the scope to the boundary effects of inflow and outflow in the whole healthcare system.

Place, publisher, year, edition, pages
Nature Research, 2024
National Category
Medical and Health Sciences
Identifiers
urn:nbn:se:kth:diva-346372 (URN)10.1038/s41598-024-60888-9 (DOI)38688997 (PubMedID)2-s2.0-85191754138 (Scopus ID)
Note

QC 20240515

Available from: 2024-05-14 Created: 2024-05-14 Last updated: 2024-05-15Bibliographically approved
Marzano, L., Dan, A., Tendler, S., Darwich, A. S., Raghothama, J., De Petris, L., . . . Meijer, S. (2023). A Comparative Analysis between Real-World Data and Clinical Trials to Evaluate Differences in Outcomes for SCLC Patients. Journal of Thoracic Oncology, 18(11), S697-S697
Open this publication in new window or tab >>A Comparative Analysis between Real-World Data and Clinical Trials to Evaluate Differences in Outcomes for SCLC Patients
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2023 (English)In: Journal of Thoracic Oncology, ISSN 1556-0864, E-ISSN 1556-1380, Vol. 18, no 11, p. S697-S697Article in journal, Meeting abstract (Other academic) Published
Place, publisher, year, edition, pages
ELSEVIER SCIENCE INC, 2023
Keywords
small cell lung cancer, real-world evidence, platinum doublet chemotherapy
National Category
Cancer and Oncology
Identifiers
urn:nbn:se:kth:diva-342734 (URN)001098831602137 ()
Note

QC 20240216

Available from: 2024-02-16 Created: 2024-02-16 Last updated: 2024-02-16Bibliographically approved
Marzano, L., Meijer, S., Dan, A., Tendler, S., De Petris, L., Lewensohn, R., . . . Darwich, A. S. (2023). Application of Process Mining for Modelling Small Cell Lung Cancer Prognosis. Paper presented at The 33rd Medical Informatics Europe Conference, MIE2023, Gothenburg, Sweden. May 22-25, 2023.. Studies in Health Technology and Informatics, 302, 18-22
Open this publication in new window or tab >>Application of Process Mining for Modelling Small Cell Lung Cancer Prognosis
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2023 (English)In: Studies in Health Technology and Informatics, ISSN 0926-9630, E-ISSN 1879-8365, Vol. 302, p. 18-22Article in journal (Refereed) Published
Abstract [en]

Process mining is a relatively new method that connects data science and process modelling. In the past years a series of applications with health care production data have been presented in process discovery, conformance check and system enhancement. In this paper we apply process mining on clinical oncological data with the purpose of studying survival outcomes and chemotherapy treatment decision in a real-world cohort of small cell lung cancer patients treated at Karolinska University Hospital (Stockholm, Sweden). The results highlighted the potential role of process mining in oncology to study prognosis and survival outcomes with longitudinal models directly extracted from clinical data derived from healthcare.

Place, publisher, year, edition, pages
IOS Press, 2023
Keywords
oncology, Process mining, Real-world Data, small cell lung cancer, treatment decision
National Category
Cancer and Oncology
Research subject
Applied and Computational Mathematics
Identifiers
urn:nbn:se:kth:diva-329927 (URN)10.3233/SHTI230056 (DOI)001071432900004 ()37203601 (PubMedID)2-s2.0-85159759671 (Scopus ID)
Conference
The 33rd Medical Informatics Europe Conference, MIE2023, Gothenburg, Sweden. May 22-25, 2023.
Note

QC 20230628

Available from: 2023-06-26 Created: 2023-06-26 Last updated: 2023-11-07Bibliographically approved
Darwich, A. S., Bostroem, A.-M., Guidetti, S., Raghothama, J. & Meijer, S. (2023). Investigating the Connections Between Delivery of Care, Reablement, Workload, and Organizational Factors in Home Care Services: Mixed Methods Study. JMIR Human Factors, 10, Article ID e42283.
Open this publication in new window or tab >>Investigating the Connections Between Delivery of Care, Reablement, Workload, and Organizational Factors in Home Care Services: Mixed Methods Study
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2023 (English)In: JMIR Human Factors, E-ISSN 2292-9495, Vol. 10, article id e42283Article in journal (Refereed) Published
Abstract [en]

Background: Home care is facing increasing demand due to an aging population. Several challenges have been identified in the provision of home care, such as the need for support and tailoring support to individual needs. Goal-oriented interventions, such as reablement, may provide a solution to some of these challenges. The reablement approach targets adaptation to disease and relearning of everyday life skills and has been found to improve health-related quality of life while reducing service use.Objective: The objective of this study is to characterize home care system variables (elements) and their relationships (connections) relevant to home care staff workload, home care user needs and satisfaction, and the reablement approach. This is to examine the effects of improvement and interventions, such as the person-centered reablement approach, on the delivery of home care services, workload, work-related stress, home care user experience, and other organizational factors. The main focus was on Swedish home care and tax-funded universal welfare systems.Methods: The study used a mixed methods approach where a causal loop diagram was developed grounded in participatory methods with academic health care science research experts in nursing, occupational therapy, aging, and the reablement approach. The approach was supplemented with theoretical models and the scientific literature. The developed model was verified by the same group of experts and empirical evidence. Finally, the model was analyzed qualitatively and through simulation methods.Results: The final causal loop diagram included elements and connections across the categories: stress, home care staff, home care user, organization, social support network of the home care user, and societal level. The model was able to qualitatively describe observed intervention outcomes from the literature. The analysis suggested elements to target for improvement and the potential impact of relevant studied interventions. For example, the elements "workload" and "distress" were important determinants of home care staff health, provision, and quality of care.Conclusions: The developed model may be of value for informing hypothesis formulation, study design, and discourse within the context of improvement in home care. Further work will include a broader group of stakeholders to reduce the risk of bias. Translation into a quantitative model will be explored.

Place, publisher, year, edition, pages
JMIR Publications Inc., 2023
National Category
Nursing
Identifiers
urn:nbn:se:kth:diva-333252 (URN)10.2196/42283 (DOI)001025960000001 ()37389904 (PubMedID)
Note

QC 20230731

Available from: 2023-07-31 Created: 2023-07-31 Last updated: 2023-07-31Bibliographically approved
El-Khateeb, E., Chinnadurai, R., Al Qassabi, J., Scotcher, D., Darwich, A. S., Kalra, P. A. & Rostami-Hodjegan, A. (2023). Using Prior Knowledge on Systems Through PBPK to Gain Further Insight into Routine Clinical Data on Trough Concentrations: The Case of Tacrolimus in Chronic Kidney Disease. Therapeutic Drug Monitoring, 45(6), 743-753
Open this publication in new window or tab >>Using Prior Knowledge on Systems Through PBPK to Gain Further Insight into Routine Clinical Data on Trough Concentrations: The Case of Tacrolimus in Chronic Kidney Disease
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2023 (English)In: Therapeutic Drug Monitoring, ISSN 0163-4356, E-ISSN 1536-3694, Vol. 45, no 6, p. 743-753Article in journal (Refereed) Published
Abstract [en]

Background: Routine therapeutic drug monitoring (TDM) relies heavily on measuring trough drug concentrations. Trough concentrations are affected not only by drug bioavailability and clearance, but also by various patient and disease factors and the volume of distribution. This often makes interpreting differences in drug exposure from trough data challenging. This study aimed to combine the advantages of top-down analysis of therapeutic drug monitoring data with bottom-up physiologically-based pharmacokinetic (PBPK) modeling to investigate the effect of declining renal function in chronic kidney disease (CKD) on the nonrenal intrinsic metabolic clearance (CLint) of tacrolimus as a case example.

Methods: Data on biochemistry, demographics, and kidney function, along with 1167 tacrolimus trough concentrations for 40 renal transplant patients, were collected from the Salford Royal Hospital's database. A reduced PBPK model was developed to estimate CLint for each patient. Personalized unbound fractions, blood-to-plasma ratios, and drug affinities for various tissues were used as priors to estimate the apparent volume of distribution. Kidney function based on the estimated glomerular filtration rate (eGFR) was assessed as a covariate for CLint using the stochastic approximation of expectation and maximization method.

Results: At baseline, the median (interquartile range) eGFR was 45 (34.5-55.5) mL/min/1.73 m2. A significant but weak correlation was observed between tacrolimus CLint and eGFR (r = 0.2, P < 0.001). The CLint declined gradually (up to 36%) with CKD progression. Tacrolimus CLint did not differ significantly between stable and failing transplant patients.

Conclusions: Kidney function deterioration in CKD can affect nonrenal CLint for drugs that undergo extensive hepatic metabolism, such as tacrolimus, with critical implications in clinical practice. This study demonstrates the advantages of combining prior system information (via PBPK) to investigate covariate effects in sparse real-world datasets.

Place, publisher, year, edition, pages
Ovid Technologies (Wolters Kluwer Health), 2023
Keywords
tacrolimus; pharmacokinetic modeling; chronic kidney disease; intrinsic clearance; renal transplantation
National Category
Other Clinical Medicine
Research subject
Applied and Computational Mathematics, Mathematical Statistics
Identifiers
urn:nbn:se:kth:diva-329362 (URN)10.1097/ftd.0000000000001108 (DOI)001135565400013 ()37315152 (PubMedID)2-s2.0-85178332058 (Scopus ID)
Note

QC 20230620

Available from: 2023-06-20 Created: 2023-06-20 Last updated: 2024-02-06Bibliographically approved
Abourraja, M. N., Marzano, L., Raghothama, J., Boodaghian Asl, A., Darwich, A. S., Meijer, S., . . . Falk, N. (2022). A Data-Driven Discrete Event Simulation Model to Improve Emergency Department Logistics. In: Proceedings of the 2022 Winter Simulation Conference: . Paper presented at Winter Simulation Conference, WSC 2022, Singapore, December 11-14, 2022. Institute of Electrical and Electronics Engineers (IEEE)
Open this publication in new window or tab >>A Data-Driven Discrete Event Simulation Model to Improve Emergency Department Logistics
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2022 (English)In: Proceedings of the 2022 Winter Simulation Conference, Institute of Electrical and Electronics Engineers (IEEE) , 2022Conference paper, Published paper (Refereed)
Abstract [en]

Demands for health care are becoming overwhelming for healthcare systems around the world regarding theavailability of resources, particularly, in emergency departments (EDs) that are continuously open and mustserve immediately any patient who comes in. Efficient management of EDs and their resources is requiredmore than ever. This could be achieved either by optimizing resource utilization or by the improvement ofhospital layout. This paper investigates, through data-driven simulation alternative designs of workflowsand layouts to operate the ED of the Uppsala University Hospital in Sweden. Results are analyzed tounderstand the requirements across the hospital for reduced waiting times in the ED. The main observationrevealed that introducing a new ward dedicated to patients having complex diagnoses with a capacity ofless than 20 beds leads to lower waiting times. Furthermore, the use of data-mining was of great help inreducing the efforts of building the simulation model.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2022
National Category
Health Care Service and Management, Health Policy and Services and Health Economy
Identifiers
urn:nbn:se:kth:diva-330232 (URN)10.1109/wsc57314.2022.10015465 (DOI)000991872900062 ()2-s2.0-85147456598 (Scopus ID)
Conference
Winter Simulation Conference, WSC 2022, Singapore, December 11-14, 2022
Note

QC 20230628

Available from: 2023-06-28 Created: 2023-06-28 Last updated: 2023-09-01Bibliographically approved
Marzano, L., Darwich, A. S., Tendler, S., Dan, A., Lewensohn, R., De Petris, L., . . . Meijer, S. (2022). A novel analytical framework for risk stratification of real‐world data using machine learning: A small cell lung cancer study. Clinical and Translational Science, 15(10), 2437-2447
Open this publication in new window or tab >>A novel analytical framework for risk stratification of real‐world data using machine learning: A small cell lung cancer study
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2022 (English)In: Clinical and Translational Science, ISSN 1752-8054, E-ISSN 1752-8062, Vol. 15, no 10, p. 2437-2447Article in journal (Refereed) Published
Abstract [en]

In recent studies, small cell lung cancer (SCLC) treatment guidelines based on Veterans’ Administration Lung Study Group limited/extensive disease staging and resulted in broad and inseparable prognostic subgroups. Evidence suggests that the eight versions of tumor, node, and metastasis (TNM) staging can play an important role to address this issue. The aim of the present study was to improve the detection of prognostic subgroups from a real-word data (RWD) cohort of patients and analyze their patterns using a development pipeline with thoracic oncologists and machine learning methods. The method detected subgroups of patients informing unsupervised learning (partition around medoids) including the impact of covariates on prognosis (Cox regression and random survival forest). An analysis was carried out using patients with SCLC (n = 636) with stage IIIA–IVB according to TNM classification. The analysis yielded k = 7 compacted and well-separated clusters of patients. Performance status (Eastern Cooperative Oncology Group-Performance Status), lactate dehydrogenase, spreading of metastasis, cancer stage, and CRP were the baselines that characterized the subgroups. The selected clustering method outperformed standard clustering techniques, which were not capable of detecting meaningful subgroups. From the analysis of cluster treatment decisions, we showed the potential of future RWD applications to understand disease, develop individualized therapies, and improve healthcare decision making.

Place, publisher, year, edition, pages
Wiley, 2022
Keywords
C reactive protein; carboplatin; cisplatin; etoposide; irinotecan; lactate dehydrogenase; platinum complex
National Category
Medical and Health Sciences Cancer and Oncology
Identifiers
urn:nbn:se:kth:diva-321440 (URN)10.1111/cts.13371 (DOI)000832654100001 ()35856401 (PubMedID)2-s2.0-85135121261 (Scopus ID)
Funder
Swedish Cancer Society, CAN2021/1469 Pj01Swedish Cancer Society, CAN 2018/597KTH Royal Institute of Technology
Note

QC 20221115

Available from: 2022-11-14 Created: 2022-11-14 Last updated: 2022-11-16Bibliographically approved
McAllister, M., Flanagan, T., Cole, S., Abend, A., Kotzagiorgis, E., Limberg, J., . . . Mackie, C. (2022). Developing Clinically Relevant Dissolution Specifications (CRDSs) for Oral Drug Products: Virtual Webinar Series. Pharmaceutics, 14(5), 1010-1010
Open this publication in new window or tab >>Developing Clinically Relevant Dissolution Specifications (CRDSs) for Oral Drug Products: Virtual Webinar Series
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2022 (English)In: Pharmaceutics, ISSN 1999-4923, E-ISSN 1999-4923, Vol. 14, no 5, p. 1010-1010Article in journal (Refereed) Published
Abstract [en]

A webinar series that was organised by the Academy of Pharmaceutical Sciences Biopharmaceutics focus group in 2021 focused on the challenges of developing clinically relevant dissolution specifications (CRDSs) for oral drug products. Industrial scientists, together with regulatory and academic scientists, came together through a series of six webinars, to discuss progress in the field, emerging trends, and areas for continued collaboration and harmonisation. Each webinar also hosted a Q&A session where participants could discuss the shared topic and information. Although it was clear from the presentations and Q&A sessions that we continue to make progress in the field of CRDSs and the utility/success of PBBM, there is also a need to continue the momentum and dialogue between the industry and regulators. Five key areas were identified which require further discussion and harmonisation.

Place, publisher, year, edition, pages
MDPI AG, 2022
Keywords
oral drug productsclinically relevant dissolution specificationsPBBMproduct performancebiorelevant dissolution
National Category
Pharmaceutical Sciences
Identifiers
urn:nbn:se:kth:diva-312019 (URN)10.3390/pharmaceutics14051010 (DOI)000801919600001 ()35631595 (PubMedID)2-s2.0-85130127779 (Scopus ID)
Note

QC 20220613

Available from: 2022-05-09 Created: 2022-05-09 Last updated: 2023-05-25Bibliographically approved
Wilson, C. G., Aarons, L., Augustijns, P., Brouwers, J., Darwich, A. S., De Waal, T., . . . García-Horsman, J. A. (2022). Integration of advanced methods and models to study drug absorption and related processes: An UNGAP perspective.. European Journal of Pharmaceutical Sciences, 172, Article ID 106100.
Open this publication in new window or tab >>Integration of advanced methods and models to study drug absorption and related processes: An UNGAP perspective.
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2022 (English)In: European Journal of Pharmaceutical Sciences, ISSN 0928-0987, E-ISSN 1879-0720, Vol. 172, article id 106100Article in journal (Refereed) Published
Abstract [en]

This collection of contributions from the European Network on Understanding Gastrointestinal Absorption-related Processes (UNGAP) community assembly aims to provide information on some of the current and newer methods employed to study the behaviour of medicines. It is the product of interactions in the immediate pre-Covid period when UNGAP members were able to meet and set up workshops and to discuss progress across the disciplines. UNGAP activities are divided into work packages that cover special treatment populations, absorption processes in different regions of the gut, the development of advanced formulations and the integration of food and pharmaceutical scientists in the food-drug interface. This involves both new and established technical approaches in which we have attempted to define best practice and highlight areas where further research is needed. Over the last months we have been able to reflect on some of the key innovative approaches which we were tasked with mapping, including theoretical, in silico, in vitro, in vivo and ex vivo, preclinical and clinical approaches. This is the product of some of us in a snapshot of where UNGAP has travelled and what aspects of innovative technologies are important. It is not a comprehensive review of all methods used in research to study drug dissolution and absorption, but provides an ample panorama of current and advanced methods generally and potentially useful in this area.

This collection starts from a consideration of advances in a priori approaches: an understanding of the molecular properties of the compound to predict biological characteristics relevant to absorption. The next four sections discuss a major activity in the UNGAP initiative, the pursuit of more representative conditions to study lumenal dissolution of drug formulations developed independently by academic teams. They are important because they illustrate examples of in vitro simulation systems that have begun to provide a useful understanding of formulation behaviour in the upper GI tract for industry. The Leuven team highlights the importance of the physiology of the digestive tract, as they describe the relevance of gastric and intestinal fluids on the behaviour of drugs along the tract. This provides the introduction to microdosing as an early tool to study drug disposition. Microdosing in oncology is starting to use gamma-emitting tracers, which provides a link through SPECT to the next section on nuclear medicine. The last two papers link the modelling approaches used by the pharmaceutical industry, in silico to Pop-PK linking to Darwich and Aarons, who provide discussion on pharmacometric modelling, completing the loop of molecule to man.

Place, publisher, year, edition, pages
Elsevier BV, 2022
National Category
Pharmaceutical Sciences
Research subject
Chemistry
Identifiers
urn:nbn:se:kth:diva-308949 (URN)10.1016/j.ejps.2021.106100 (DOI)34936937 (PubMedID)2-s2.0-85125491441 (Scopus ID)
Note

QC 20220222

Available from: 2022-02-16 Created: 2022-02-16 Last updated: 2023-10-09Bibliographically approved
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0001-8218-4306

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