Hochschule Trier, Umwelt-Campus Birkenfeld
+49 6782 17-1727
g.dartmann@umwelt-campus.de

Publikationen

Abstract:

Medical guidelines have a significant role in the field of evidence-based medical treatment. The content of a medical guideline is based on a systematic review of clinical evidence with instructions and recommendations that clinicians can refer to. Most of the guidelines are available in an unstructured text format. Hence, clinicians must take a considerable time to search and find relevant recommendations in their semantic context. Using Machine Learning algorithms, automatic information extraction from medical guidelines has recently become possible. We present a novel system for information extraction and a fuzzy rule database developed for clinical guidelines.

The proposed system, dubbed NLP-FUZZY, combines capabilities of Natural Language Processing (NLP) and Fuzzy Logic approaches. First, the NLP-FUZZY performs a semantic extraction of medical guidelines using a bi-directional Long short-term memory (LSTM). Subsequently, using the extracted semantic, it creates fuzzy rules, which are able to recognize new cases in a learning domain while predicting and extract the grade of recommendation. In order to test the NLP-FUZZY system, we compared its performance with state-of-the-art NLP approaches for clinical information extraction.
 
L. B. Fazlic, A. Hallawa, A. Schmeink, A. Peine, L. Martin and G. Dartmann, „A Novel NLP-FUZZY System Prototype for Information Extraction from Medical Guidelines,“ 2019 42nd International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), Opatija, Croatia, 2019, pp. 1025-1030.
 
Published in:
Date of Conference:
20-24 May 2019
Date Added to IEEE Xplore:
11 July 2019
 
ISBN Information:
Electronic ISBN: 978-953-233-098-4
Print on Demand(PoD) ISBN: 978-1-5386-9296-7
Electronic ISSN: 2623-8764
 
INSPEC Accession Number:
18820538
DOI:
 
Publisher:
IEEE
 
Conference Location:
Opatija, Croatia, Croatia
 
Funding Agency:
Bundesministerium für Bildung und Forschung – IMEDALytics project

Abstract:

This letter presents an adaptive learning framework for estimating structural parameters in stochastic state-based models (SSMs). SSMs are a useful modeling tool in systems biology and medicine. While models in these disciplines are traditionally hand-crafted, an automated generation based on experimental data becomes a topic of research interest. In particular, our goal is to classify measured processes using the generated models. An innovative likelihood-based adaptive learning approach capable of learning the structural parameters, i.e., the arc weights of SSMs from data and exploiting the reliability of detected inputs is presented in this letter. Its convergence behavior is analyzed and an expression for the error at steady state is derived. Simulations assess the performance of the proposed and existing algorithms for a gene regulatory network.
 
P. M. Vieting, R. C. de Lamare, L. Martin, G. Dartmann and A. Schmeink, „Likelihood-Based Adaptive Learning in Stochastic State-Based Models,“ in IEEE Signal Processing Letters, vol. 26, no. 7, pp. 1031-1035, July 2019.
 

Published in:
IEEE Signal Processing Letters (Volume: 26, Issue: 7, July 2019)

Page(s):
1031 – 1035

Date of Publication:
17 May 2019

ISSN Information:

Print ISSN: 1070-9908
Electronic ISSN: 1558-2361
 
INSPEC Accession Number:
18715550
 

 

Funding Agency:

10.13039/501100002347-Bundesministerium für Bildung und Forschung; IMEDALytics project;

Find also on: ice.rwth-aachen.de/publications

Abstract:

In this work, a new hybrid algorithm for disease risk classification is proposed. The proposed methodology is based on Dynamic Time Warping (DTW). This methodology can be applied to time series from various domains such as vital sign time series available in medical big data. To validate our methodology, we applied it to risk classification for sepsis, which is one of the most challenging problems within the area of medical data analysis. In the first step the algorithm uses different statistical properties of time series features. Furthermore, using differently labeled training data sets, we created a DTW Barycenter Averaging (DBA) on each feature.

In the second step, validation data sets and DTW are used to validate the precision of classification and the final results are compared. The performance of our methodology is validated with real medical data and on six different criteria definitions for the sepsis diseases. Results show that our algorithm performed, in the best case, with precision and recall of 96,38% and 90,90%, respectively.

Lejla Begic Fazlic, Ahmed Hallawa, Matthias Dziubany, Marlies Morgen, Jens Schneider, Marvin Schacht, Anke Schmeink, Lukas Martin, Arne Peine, Thomas Vollmer, Stefan Winter and Guido Dartmann, 8th International Conference on Cyber – Physical Systems and Internet-of-Things (CPS & IoT‘ 2020), Budva, Montenegro, 2020.

Date of Conference:

8-11 June 2020

Date Added to IEEE Xplore:
07 July 2019
 
ISBN Information:
Electronic ISBN: 978-1-7281-6949-1
Print on Demand(PoD) ISBN: 978-1-7281-6950-7
Electronic ISSN: 2637-9511 
 
DOI:
 
Publisher:
IEEE
 
Conference Location:
Budva, Montenegro
 
Funding Agency:
Bundesministerium für Bildung und Forschung – IMEDALytics project

Abstract:

Intensive Care Unit (ICU) professionals have to make lifesaving therapy decisions promptly under high stress and uncertainty. Clinical Decision Support Systems (CDSS) can improve the quality of healthcare by identifying complex statistical connections between patients‘ parameters and by rapidly presenting the statistically most promising treatment options to physicians. However, HCI aspects are rarely considered when developing CDSSs. This paper describes a field study conducted in three ICUs investigating how physicians and nurses form (volume) therapy decisions and monitor their success. Our findings reveal a continuous decision cycle in which nurses and physicians collaborate synchronously and asynchronously to provide optimal care. Furthermore, the desire to understand how a CDSS generated recommendations varies depending on the user’s goals and other contextual factors such as workload. These findings show that CDSSs for ICUs need to (1) specifically facilitate collaboration and (2) support adaptation of the interface to both context and users.
 
Annika Kaltenhauser, Verena Rheinstädter, Andreas Butz, Dieter P. Wallach, „You Have to Piece the Puzzle Together“ — Implications for Designing Decision Support in Intensive Care, DIS ’20: Proceedings of the 2020 ACM Designing Interactive Systems Conference, Eindhoven, Netherlands, 2020, pp. 1509-1522.
 
Published in:
Date of Conference:
July 2020
 
ISBN Information:
978-1-4503-6974-9
 
DOI:
 
Publisher:
Association for Computing Machinery, New York, NY, United States
 
Conference Location:
Eindhoven, Netherlands
 
Funding Agency:
Bundesministerium für Bildung und Forschung – IMEDALytics project

Abstract:

We present the paradigm of Expert of Oz studies for the formative evaluation of hypothetical AI systems. These studies follow the principle of Wizard of Oz studies but use a human expert for simulating the AI. This allows the experimenter to not only investigate the user’s behavior with and reaction to such a system but also to observe and analyze the performance of the expert in the situation at hand. As a consequence, we can learn both about interaction with the hypothetical AI system and about the required AI functionality at the same time and in realistic interaction scenarios.
 
Andreas Butz, Annika Kaltenhauser, Malin Eiband, The Expert of Oz: A Two-sided Study Paradigm for Intelligent Systems, DIS‘ 20 Companion: Companion Publication of the 2020 ACM Designing Interactive Systems Conference, Eindhoven, Netherlands, 2020, pp. 269-273.
 
Published in:
Date of Conference:
July 2020
 
ISBN Information:
978-1-4503-7987-8
 
DOI:
 
Publisher:
Association for Computing Machinery, New York, NY, United States
 
Conference Location:
Eindhoven, Netherlands
 
Funding Agency:
Bundesministerium für Bildung und Forschung – IMEDALytics project

Abstract:

Integrating Artificial Intelligence (AI) technologies promises to open new possibilities for the development of smart systems and the creation of positive user experiences. While the acronym «AI»has often been used inflationary in recent marketese advertisements, the goal of the paper is to explore the relationship of AI and UX in concrete detail by referring to three case studies from our lab. The first case study is taken from a project targeted at the development of a clinical decision support system, while the second study focuses on the development of an autonomous mobility-on-demand system. The final project explores an innovative, AI-injected prototyping tool. We discuss challenges and the application of available guidelines when designing AI-based systems and provide insights into our learnings from the presented case studies.
 
Dieter P. Wallach, Lukas A. Flohr, Annika Kaltenhauser, Beyond the Buzzwords: On the Perspective of AI in UX and Vice Versa, First International Conference, AI-HCI 2020, Held as Part of the 22nd HCI International Conference, HCII 2020, Copenhagen, Denmark, 2020, pp. 146-166
 
Published in:
Date of Conference:
July 19-24, 2020
 
ISBN Information:
Online ISBN: 978-3-030-50334-5
Print ISBN: 978-3-030-50333-8
Series Online ISSN: 1611-3349
Series Print ISSN: 0302-9743
 
DOI:
 
Publisher:
Springer, Cham
 
Conference Location:
Copenhagen, Denmark
 
Funding Agency:
Bundesministerium für Bildung und Forschung – IMEDALytics project

Abstract

Decision support systems in intensive care units are developed with safety, efficiency, and effectiveness in mind. In contrast, user experience (UX) for decision support systems has received limited attention in practice and research. In this paper, we present an application of the Experience Design approach from Hassenzahl [8] for a clinical decision support system (CDSS) for volume therapy – the administration of intravenous fluids – in intensive care units. In semi-structured interviews with five nurses, we gathered nurses‘ work activities around volume therapy, which are perceived as particularly beneficial for their well-being. Lead by the narratives gathered in the interviews we designed positive experience through the CDSS. The resulting concept of a CDSS for nurses aims to create a positive experience during volume therapy, which is supported by addressing and fulfilling the needs for competence and popularity.

Published in:
MuC’20: Mensch und Computer 2020 Magdeburg, Germany 
 
Date of Conference:
September 2020
 
ISBN Information:
978-1-4503-7540-5
 
DOI:
 
Publisher:
Association for Computing Machinery, New York, NY, United States
 
Conference Location:
Magdeburg, Germany
 
Funding Agency:
Bundesministerium für Bildung und Forschung – IMEDALytics project