Home // FSTM // News // Machine Learning in the Clinical Decision Support System for Laboratory Medicine AMPEL

Machine Learning in the Clinical Decision Support System for Laboratory Medicine AMPEL

twitter linkedin facebook google+ email this page
Add to calendar
Speaker: Dr. Mark Wernsdorfer, Research Associate, Laboratory University Hospital Leipzig, Germany
Event date: Tuesday, 17 March 2020 02:30 pm - 04:00 pm
Place: Université du Luxembourg, Belval campus
Learning Center, room 2.02
7, Ënnert den Héichiewen, 4362 Esch-sur-Alzette

This conference is part of the AI4Health Lecture Series organised by the Department of Computer Science and the Department of Life Sciences and Medicine of the University of Luxembourg. 

Abstract

Laboratory medicine is essential for the diagnosis, therapy, and management of patients. The timely and appropriate consideration and interpretation of laboratory results is critical to, for example, review the choice of treatment or respond quickly to sudden changes in the patient's condition. Laboratory diagnostics, in general, provide relevant and high-quality information about the condition of the patient. Clinical Decision Support Systems (CDSS) assist in the digital collection of large amounts of such information, its automated delivery to the appropriate medical staff, and the development of treatment methods. This helps to avoid medical errors due to mistakes or misinterpretations. The aim of the AMPEL project at the University Hospital Leipzig is to implement and evaluate a CDSS for laboratory diagnostics.
At the core of the system is the conversion of laboratory results into more condensed information, enabling better and faster treatment. From the data available to this system, machine learning methods can deduce critical patient states, biomarkers that are difficult or expensive to collect, or medical diagnoses from similar cases in the past. Medical staff can then be automatically alerted to critical constellations of biomarkers that require medical intervention, costly and time-consuming analyses can be performed in a more targeted manner, and diagnoses of rare diseases can be proposed that could otherwise have been overlooked.
A transparent machine learning system has been developed that can predict diagnoses with high precision and recall. The generated models are validated by specialists and checked for medical plausibility. This ensures that the tests for models are representative and practice-oriented. A connection to the productive patient database of the University Hospital Leipzig enables the reactive adaptation of models to specific changes of the local patient population.
The current status of the AMPEL project is presented. The methods used and the problems resulting from their application to certain data sets are described by means of an exemplary case. Methods of result analysis, as well as means to improve the system and to extend its applicability in the future, are described.

Speaker

Dr Mark Wernsdorfer studied philosophy and computer science at the University of Bamberg. After his studies, he did his doctorate at the professorship Cognitive Systems on the question of consciousness in artificial systems. He then provided technical support to the Centre for Heritage Conservation Studies and Technologies. He has been a research associate at the AMPEL project of the Laboratory Medicine of the University Hospital Leipzig since October 2019. The project supports treating physicians by automatically recording laboratory values of patients, recognizing them as critical and, if necessary, reporting them to medical staff. His research interests include the philosophy of mind and artificial intelligence. In the intersection of both, he is particularly concerned with the structural prerequisites that a system must have in order to be considered intelligent, as well as the associated external prerequisites that it must have in order to be perceived as conscious by others.

Link: AI4Health Lecture Series
Mark