DISA

When? Friday September 20th 14-16
Where? Onsite: D0073 at Linnaeus University in Växjö and online
Registration: Please sign up for the PhD-seminar via this link https://forms.gle/h8oQ9VVYJUFu89i77 by September 18th (especially important if you plan on attending onsite so we have fika for everyone)

Agenda

14.00-14.10 Welcome and practical information from Welf Löwe
14.10-14.55 Presentation and discussion: Improving medication safety though the collaboration between researchers from medicine and computer science – Tora Hammar
14.55 – 15.05 Coffee break
15.05 – 15.50 Presentation and discussion – A machine learning approach to improving drug risk assessment – Daniel Nilsson
15.50 -16.00 Sum up and plan for the October seminar

Abstracts

Improving medication safety though the collaboration between researchers from medicine and computer science – Tora Hammar
In my presentation I will talk about our research project where we use health data to see if we can predict medication related problems and improve predictions compared with the current clinical decision support systems (CDSS) used in health care. The project is an interdisciplinary collaboration between researchers from medicine, pharmacy and computer science.

Medication usage and the simultaneous use of many medications is increasing world-wide. Problems with side-effects (adverse drug events) are common and cause suffering and even death, as well as substantial costs for society. One method to prevent harmful combinations of medications is by using CDSS in health care or at pharmacies that can detect potential ADEs. Todays CDSS are often based on rules written by humans (so called knowledge databases). Although we have high quality knowledge databases in Sweden these systems have known weaknesses such as having to many non-relevant alerts causing alert fatigue among users. One reason is that the rules are often very simple, another reason is that they have not been validated in large populations. In our research project we use data from health care in the region of Kalmar County for over ten years of time, including data on all medications being used during that time. We also have the rules and algorithms from the Swedish knowledge databases called Janusmed which is used in health care to give warnings about potentially harmful combinations of medications. In the project we aim to:

• Increase knowledge of effects when combining many different medications
• Study how well current CDSS can predict adverse drug events (Spoiler alert! Not very well.)
• Examine if we can improve predictions compared with the current CDSS by using machine learning.
• Develop methods to identify adverse drug events in clinical notes (unstructured data) using large language models.

Tora is an associate professor in health informatics at the eHealth Institute at Linnaeus University. She is a pharmacist, with a master and PhD in Biomedical Science. In her research she is using different methods to improve information systems and decision support in the medication management process. Much of her research is done in collaboration with computer science as a part of LnuC DISA, and she is the research leader for the DISA eHealth group.

Daniel Nilsson who is presenting after Tora is working in the research project Tora is presenting and will dive deeper into some of the questions.

A machine learning approach to improving drug risk assessment – Daniel Nilsson

Many medications are associated with adverse side effects. An understanding of what factors influence the risk of adverse drug events is important for managing this risk. In this presentation I will describe the results of a project to investigate ways to improve risk assessments for two categories of adverse drug events (bleedings and QT-prolongation) compared to the currently used knowledge database Janusmed. Using data on adverse drug events from the healthcare information system from Kalmar region (comprising ten years of event data, and ~280 000 patients), we seek to use machine learning methods to answer questions such as:

• Do the Janusmed risk values provide predictive information?
• Can we combine the Janusmed risk values (which only contain information of current medication) with demographic information and additional health data to improve predictions?
• Are the risk values assigned to different medications in Janusmed in alignment with the risks observed in the data?

Daniel is working in the DISA eHealth group as part of the AI Sweden program Eye for AI. He has a PhD in computational biology from Lund University, where he studied computational methods for protein simulation.

When? Friday September 6th 14-16
Where? Onsite: D2272 at Linnaeus University in Växjö and online
Registration: Please sign up for the PhD-seminar via this link https://forms.gle/xtG9s5Qhs4SFd98E7 by September 4th (especially important if you plan on attending onsite so we have fika for everyone)

Agenda

14.00-14.10 Welcome and practical information from Welf Löwe
14.10-14.55 Presentation and discussion: Reuse of health data, combing the best of two worlds – Machine learning driven knowledge discovery from real world health data with collaboration of domain expert – Olle Björneld, Industrial PhD-student Region Kalmar
14.55 – 15.05 Coffee break
15.05 – 15.50 Presentation and discussion: Remaining useful life prediction of batteries based on historical loading-unloading cycle logs – Zijie Feng, Industrial PhD-student Micropower
15.50 -16.00 Sum up and plan for our next seminar on October 4th and other ongoing activities.

Abstracts

Reuse of health data, combing the best of two worlds – Machine learning driven knowledge discovery from real world health data with collaboration of domain expert – Olle Björneld, Industrial PhD-student Region Kalmar
The main objective of the PhD project is “How can data driven knowledge discovery in databases (KDD), performed in the medical research domain supported with domain knowledge, be more effective and efficient?” To answer this question the following work has been performed:

Knowledge discovery from real-world data in health care can be demanding due to unstructured data and low registration quality in electronic health records (EHRs). Close collaboration between domain experts and data scientists is essential. New variables, referred to as features, are generated from domain experts and computer scientists in collaboration with medical researchers. This process is named knowledge-driven feature engineering (KDFE). (Study A, published)

A case study comprising two projects (P1 and P2) was performed to evaluate the effectiveness of manual KDFE (mKDFE), the effectiveness was represented of classification performance, more precisely the area under the receiver operating characteristic curve (AUROC). The study gave salient results that it is valuable for medical researchers to involve a data scientist when medical research based on real world medical data is performed. When mKDFE was compared to baseline, the average classification performance measured by AUROC for the engineered features rose for P1 from 0.62 to 0.82 and for P2 from 0.61 to 0.89 (p-values << 0.001). (Study B, published)

To perform KDD more effectively and efficiently, a framework for automatic Knowledge Driven Feature Engineering (aKDFE) was developed. Central to aKDFE is an automated feature engineering (FE), i.e., an automated construction of new, highly informative features, from those directly observed and recorded, e.g., in EHRs. The framework learns and aggregates domain knowledge to generate features that are more informative compared to those recorded in EHRs or manually engineered (manual FE) as done in many medical research projects today.

aKDFE is (i) more efficient than manual FE since it automates the manual knowledge discovery and FE processes. It is (ii) more effective due to its higher predictive power compared to manual KDFE. Finally, aKDFE (iii) applies and describes data pivoting and feature generation as explicit and transparent operation sequences on EHR features. (Study C, published)

Domain expert knowledge can be found in knowledge databases or expert knowledge decision support systems, in which derived and distilled knowledge has been manually entered and can be represented as risk scores or indexes. To leverage the effect of expert knowledge in aKDFE we will dissect the following questions: “How does decision support scores impact the effectiveness of aKDFE?”. (Study D/E, under construction)

aKDFE saves time and resources from medical researchers and produces more informative features, however future enhancements still exists, (i) evaluation of more sophisticated time series oriented models, (ii) use of LLMs to collect and structure domain knowledge, and (iii) evaluate multi-agent knowledge discovery.

Remaining useful life prediction of batteries based on historical loading-unloading cycle logs – Zijie Feng, Industrial PhD-student Micropower

As technology advances, battery usage has become increasingly prevalent in daily life. Many traditional fuel-powered mechanical devices, such as forklifts and automated guided vehicles, are now powered by battery. Concurrently, concerns about safety and efficiency have heightened the focus on monitoring the condition of batteries in these large devices.

During usage, the battery’s actual capacity diminishes gradually. When the capacity falls to a certain threshold, the battery becomes unusable. In general, we can measure the remaining useful life of a battery (i.e., RUL) in two ways: directly by measuring the physical and chemical characteristics of the battery, and indirectly by using data-driven models. Since direct measurement of batteries is very inconvenient, RUL prediction based on data models is a promising research direction. RUL is typically estimated, considering the battery’s condition and the customer’s usage. However, both factors are influenced by numerous variables, introducing uncertainty into the estimated RUL, and consequently significant fluctuations in the RUL curve.

In this presentation, we will share the progress we’ve made at Micropower in developing a workflow that predicts battery RUL with confidence intervals using machine learning algorithms on historical battery cycle logs. These results will help battery owners and suppliers plan maintenance and replacements in advance. Additionally, we will also introduce our ongoing project on anomaly detection within battery logs.

When? Wednesday September 4th 11.30-13.00
Where? D1140, Campus Växjö.
Registration: No registration needed – just come by

We have a guest visit from Sebastian Zeng from Universität Salzburg (Austria) who will give a guest lecture with the title Latent SDEs on Homogeneous Spaces

Abstract: We consider the problem of variational Bayesian inference in a latent variable model where a (possibly complex) observed stochastic process is governed by the solution of a latent stochastic differential equation (SDE). Motivated by the challenges that arise when trying to learn an (almost arbitrary) latent neural SDE from data, such as efficient gradient computation, we take a step back and study a specific subclass instead. In our case, the SDE evolves on a homogeneous latent space and is induced by stochastic dynamics of the corresponding (matrix) Lie group. In learning problems, SDEs on the unit n-sphere are arguably the most relevant incarnation of this setup. Notably, for variational inference, the sphere not only facilitates using a truly uninformative prior, but we also obtain a particularly simple and intuitive expression for the Kullback-Leibler divergence between the approximate posterior and prior process in the evidence lower bound. Experiments demonstrate that a latent SDE of the proposed type can be learned efficiently by means of an existing one-step geometric Euler-Maruyama scheme. Despite restricting ourselves to a less rich class of SDEs, we achieve competitive or even state-of-the-art results on various time series interpolation/classification problems.

For more information or questions about the seminar, please contact:
– Wolfgang Bock wolfgang.bock@lnu.se or Jonas Nordqvist jonas.nordqvist@lnu.se

When? Friday August 16th 14-16
Where? Onsite: B1009 at Linnaeus University in Växjö and online
Registration: Please sign up for the PhD-seminar via this link https://forms.gle/aYqRMod68hVLv8EW9 by August 14th (especially important if you plan on attending onsite so we have fika for everyone)

Agenda

14.00-14.10 Welcome and practical information from Welf Löwe
14.10-14.55 Presentation and discussion: Improving Non-Indigenous Species Introduction Risk Considering Seasonality and Gravity-informed Deep Learning Models – Amilcar Soares
14.55 – 15.05 Coffee break
15.05 – 15.50 Presentation and discussion – State-of-the-art and ongoing research on the Visualization of Temporal and Multivariate Networks – Claudio Linhares
15.50 -16.00 Sum up and plan for the September seminar

Abstracts

Improving Non-Indigenous Species Introduction Risk Considering Seasonality and Gravity-informed Deep Learning Models – Amilcar Soares

The introduction and spread of aquatic non-indigenous species (NIS) pose significant threats to global biodiversity, disrupt ecosystems, and cause substantial economic damage in agriculture, forestry, and fisheries. The growing complexity of international trade and transportation networks has exacerbated the risk of NIS introduction and spread. In this presentation, I will discuss the common problem of NIS management and the importance of robust risk assessment models to mitigate these threats. First, I will present a study investigating the influence of temporal variability in sea surface temperature and salinity on ballast water risk assessment (BWRA) models. By comparing global ports’ monthly and annual environmental data, the study highlights how seasonal variations can impact the environmental similarity scores between source and recipient locations, which are crucial for predicting NIS survival and establishment. The findings suggest that incorporating monthly data in BWRA models provides a more sensitive and accurate risk assessment than traditional annual average models. Next, I will introduce a novel physics-informed model designed to forecast maritime shipping traffic and assess the risk of NIS spread through global transportation networks. This model, inspired by the gravity model for international trade, integrates factors such as shipping flux density, port distance, trade flow, and centrality measures. By incorporating transformers, the model effectively captures both short- and long-term dependencies, achieving significant improvements in predicting vessel trajectories and traffic flows. The enhanced accuracy of this model aids policymakers and stakeholders in identifying high-risk invasion pathways and prioritizing management actions. Together, these studies advance our understanding of NIS risk assessment and underscore the need for dynamic, data-driven approaches to effectively manage and mitigate NIS’s impacts in a rapidly changing global landscape.

State-of-the-art and ongoing research on the Visualization of Temporal and Multivariate Networks – Claudio Linhares
This presentation will cover an overview of current research on visualizing temporal and multivariate networks, emphasizing the challenges and advancements in representing evolving interactions and diverse attributes. Also, it will discuss ongoing research into temporal network visualization techniques, including static and dynamic approaches, and the incorporation of multivariate attributes, such as node features, edge weights, and temporal dynamics. Furthermore, it will explore the challenges of scalability, interpretability, and interactive exploration.

When? Friday June 14th 14-16
Where? Onsite: D1172 at Linnaeus University in Växjö and online
Registration: Please sign up for the PhD-seminar via this link https://forms.gle/H3oZ9QqRpjVn575u7 by June 12th (especially important if you plan on attending onsite so we have fika for everyone)

Agenda
14.00-14.10 Welcome and practical information from Welf Löwe
14.10-14.55 Presentation and discussion: Mitigating Health Inequalities – A Transdisciplinary System Thinking Approach, Nadeem Abbas
14.55 – 15.05 Coffee break
15.05 – 15.50 Presentation and discussion – Antifragility and resilience in ICT systems – Diego Perez
15.50 -16.00 Sum up and plan for the fall seminars

Abstracts

Mitigating Health Inequalities – A Transdisciplinary System Thinking Approach – Nadeem Abbas
Health inequalities persist and have increased in some Swedish regions. Considering the multifaceted nature of this problem the new national public health policy emphasizes shared responsibility and coordinated efforts at all levels. To this end, Nadeem will present preliminary results of a project that aims to identify and investigate factors that are leading to the problem of health inequalities in the Kronoberg region, and how digital technologies can help to mitigate such inequalities. The project follows a transdisciplinary approach involving researchers from various disciplines and practitioners in the field, to identify and analyze factors, barriers and unmet needs leading to the health inequalities. The project focusses on Araby area of Kronoberg. The data is collected through meetings and semi-structured interviews with relevant stakeholders working in the area. Preliminary analysis indicates following major factors causing health inequalities in the region: 1) language and cultural barriers, 2) lack of knowledge about the health care system, and 3) complexity of the existing system. Furthermore, the analysis shows that there is need for improving health literacy, particularly, in vulnerable groups, such as migrants. The project work is still in progress and Nadeem will discuss future plans and possibilities to collaborate with interested researchers.

Antifragility and resilience in ICT systems – Diego Perez
Antifragility is a term that has recently emerged to refer to ICT systems that remain trustworthy despite their dynamic and evolving operating context. This talk will present a characterization of antifragility for ICT systems, aiming to clarify the implications of its adoption, its relationships with other approaches sharing a similar objective, and a possible guide to engineer antifragile computing systems. To provide a background for reasoning on antifragility, the talk will first introduce a framework for dependability and resilience properties and then it will relate them to antifragility.

When: Monday, June 3rd 1000-1200
Where: Onsite D1172 (Linnaeus University)

You are welcome to attend Farid’s seminar and the follow-up discussion with Prof. Welf Löwe (Examiner).

Title: Realizing Smarter Organizations through Digital Twin of the Organization Approach

Abstract:
To survive and remain competitive in today’s dynamic, uncertain, and constantly changing environment, organizations must alter their traditional business software solution and be smarter. A smarter organization needs smarter machinery systems. These systems can be categorized into four levels of smartness based on their ability to adapt at runtime. Self-adaptive systems (SAS), enabling run-time adaptation, play a crucial role in achieving different levels of system smartness where they autonomously adjust their behavior in response to changes in the environment, system conditions, or system goals. Although various waves of research on engineering SAS pave the way toward smarter systems, several issues like changing adaptation goals at run time, keeping run time models up-to-date, complex nature of uncertainty, etc. have remained open, yet. To overcome these issues, our solution is adding a digital twin as an additional specialized component to modify the managing system of SAS over time.
Despite the contribution of smarter systems to the efficiency and effectiveness of an organization, it is not the ultimate goal of smarter organizations. Becoming a smarter organization requires a holistic approach considering smart machinery systems, processes, people, culture, and strategy. Therefore, developing methodologies to facilitate managing, controlling, and evolving the organization as well as dealing with its complexity is crucial. Digital Twin of the Organization (DTO) provides a suitable basis for continuous assessment, optimization, and prediction by representing all the organizational system elements and connections in virtual models and through perpetual simulation and analysis. However, creating a flexible and evolvable DTO that covers and supports the organization’s business strategies is a complex and time-consuming task that requires engineering best practices. As our proposal, EA Blueprint Pattern serves as an architectural reference for the development and evolution of a DTO by allowing for mapping well-known Enterprise Architecture concepts into software components defining the DTO software architecture.

When? Friday May 31 14-16
Where? Onsite: B1006 at Linnaeus University in Växjö and online
Registration: Please sign up for the PhD-seminar via this link by May 29st https://forms.gle/DVCYhx4aeiU87xmS8 (especially important if you plan on attending onsite so we have fika for everyone)

14.00-14.10 Welcome and practical information from Welf Löwe
14.10-14.55 Presentation and discussion: AI and data literacy: integration of related knowledge in K-12 education? – Johanna Velander, PhD-student in Computer Science and Media Technology
14.55 – 15.05 Coffee break
15.05 – 15.50 Presentation and discussion: Performance and Condition Monitoring of Drilling Machinery. – Rammohan Kodakadath Premachandran, PhD-student in Mecanical Engineering
15.50 -16.00 Sum up and plan for our next seminar on September 6th

Abstracts

When? Friday May 3 14-16
Where? Onsite: B1006 at Linnaeus University in Växjö and online
Registration: Please sign up for the PhD-seminar via this link https://forms.gle/9zwTEPoH9Uw26Cbi7 by May 1st (especially important if you plan on attending onsite so we have fika for everyone)

14.00-14.10 Welcome and practical information from Welf Löwe
14.10-14.55 Presentation and discussion: ChatGPT as a combined OCR and key-value extractor – Nemi Pelgrom, Industry PhD student at Fortnox
14.55 – 15.05 Coffee break
15.05 – 15.50 Presentation and discussion: Data-Driven Approach Towards Objective Evaluation of the Gravel Road Condition – Keegan Mbiyana, PhD-student in Mechanical Engineering
15.50 -16.00 Sum up and plan for our next seminar on May 31st

Abstracts

ChatGPT as a combined OCR and key-value extractor – Nemi Pelgrom, Industry PhD student at Fortnox

This study presents the results of an experiment of using the AI chatbot chatGPT as a combined OCR and key-value extractor. The dataset used consisted of 1000 real invoices and 500 images of real receipts from a wide variety of Swedish market sectors. This tool demonstrated an accuracy rate of 99.8% in extracting key financial data from invoices, and 100% accuracy from images of receipts, showcasing its potential as a one-stop solution for financial document processing.

Data-Driven Approach Towards Objective Evaluation of the Gravel Road Condition – Keegan Mbiyana, PhD-student in Mechanical Engineering

Visual windshield surveys are the primary method for assessing the condition of gravel roads, but they can be prone to human judgment errors and can be time-consuming. There is a need to shift to an objective assessment of the condition of gravel roads by leveraging advances in sensor technology and data processing techniques for efficiency and effectiveness. This seminar will present work conducted in the Department of Mechanical Engineering in objectively assessing the gravel road condition to improve maintenance planning and decision-making. The results from the field experiments on three gravel roads with different condition classes and the vehicle vibration response collected using Integrated Electronics Piezo-Electric (IEPE) and smartphone accelerometers to verify a proposed rule-based approach will be presented. The challenge of regular monitoring is discussed, considering the remote location of gravel roads. Regular condition monitoring and data collection would require collecting and storing huge amounts of data. Therefore, as part of future work, the seminar will briefly introduce an adaptive sampling technique to reduce redundant or unnecessary data before storage.

When? Friday May 17th 14-16
Where? Onsite: B1006 at Linnaeus University in Växjö and online
Registration: Please sign up for the PhD-seminar via this link https://forms.gle/yidY5w9CWFqbXgz38 by May 15th (especially important if you plan on attending onsite so we have fika for everyone)

Agenda
14.00-14.10 Welcome and practical information from Welf Löwe
14.10-14.55 Presentation and discussion: Using AI in eHealth Research, Alisa Lincke
14.55 – 15.05 Coffee break
15.05 – 15.50 Presentation and discussion – Emerging Technologies for Preventive Healthcare, Hemant Ghayvat
15.50 -16.00 Sum up and plan for our next seminar on June 14th

Abstracts

Using AI in eHealth Research, Alisa Lincke
In this seminar, Dr. Alisa Lincke, Senior Lecturer at Linnaeus University, will share her research experience in eHealth over the past three years, conducted in collaboration with Kalmar eHealth Institute, Stockholm University, Uppsala University, Karolinska, and others. Three seed research projects will describe the process starting from writing seed application, ethical approval, data extraction, data analysis, and research outcome. You will get insights about the research process and challenges in eHealth research.

Emerging Technologies for Preventive Healthcare, Hemant Ghayvat
In the rapidly evolving world of healthcare, the integration of Artificial Intelligence (AI) and the Internet of Things (IoT) stands at the forefront of innovation, offering unprecedented opportunities to enhance patient care and outcomes. Our seminar delves into the transformative potential of these technologies in addressing some of the most pressing healthcare challenges, particularly in elderly care, mental health, and cardiac care. Through expert-led discussions, we will explore how AI’s predictive analytics can revolutionize elderly care by monitoring and managing chronic conditions, ensuring timely interventions, and promoting independent living. Similarly, in mental health, AI’s data-driven insights can personalize treatment plans and provide support, while IoT devices offer real-time monitoring and management solutions. For cardiac care, the combination of AI and IoT promises to improve diagnostics, treatment efficacy, and continuous patient monitoring, significantly reducing the risk of adverse events. Join us as we uncover the synergistic power of AI and IoT in crafting the future of healthcare, where technology empowers care providers to offer more precise, effective, and compassionate care across these critical areas.

Our friends at BTH invites our PhD-student to join their new course offered by the innovation office in Innovation and entrepreneurship during 2024. The course aims to support researchers in the process of taking research results to Innovation, impact and commercialization.

Research impact is no longer solely evaluated on academic publications and teaching programmes. The research funding landscape today expects a clear strategy regarding impact on society included in project proposals. Non-academic research dissemination, either through a start-up company or collaborations with established companies is therefore increasingly important.

The course is divided into three optional modules, i.e., you can register to one or several modules according to your interest (one credit per module). See attached course description for further information.

• The course starts at 2/4 with an introductory lecture
• The course is provided by Innovation office at BTH in cooperation with external expert representatives who will give lectures and seminars.
• Course registration is done by sending email to marie.netz@bth.se at the latest 28th of March.
• Please state your full name, department and research area AND which module(s) you want to take.
• Senior researchers are also welcome to attend and follow the course.

For more information about the course see: https://lnu.box.com/s/vkvu3k3incqwqh9higjvsi0q0lgnzzvf