Despite the obvious fact that in the field of hematology, diseases are very heterogeneous and a patient's disease course varies substantially from one to another, clinical trial design, drug development and subsequent therapeutic decision has largely relied on the administration of a similar therapeutic regimen to an utterly diverse patient population. This conventional, one-size-fits-all treatment approach predisposes patients with hematologic diseases towards suboptimal response rates.
Further progress in understanding the pathophysiology of complex hematological malignancies has changed our treatment approaches in the last years. Hematologists now require an understanding of a rapidly evolving treatment paradigm that is increasingly nuanced, complex and patient-directed. The underlying heterogeneous disease biology demands differences in personalized therapeutic decisions, making individualized patient treatment a core objective in the hematologic field.
Until now, the therapeutic decision-making process still depends on whether the treating physician has the relevant specialized knowledge and access to the steadily increasing amount of new therapeutic options. To overcome this challenge, the "Artificial Intelligence (AI) in Hematology Group" focuses on the development and implementation of solutions to utilize AI in assisting therapeutic decision and to adapt personalized treatment strategies.
In the "AI in Hematology Group", hematologists and bioinformaticians of Prof. Uwe Platzbecker´s team from the Department of Hematology, Cellular Therapy and Hemostaseology at the University Hospital Leipzig are working closely together with information- and data scientists, engineers and developers of Prof. Thomas Neumuth´s team from the "Innovation Center Computer Assisted Surgery (ICCAS)" Leipzig.
News:
The "AI in Hematology group" recently
published the KAIT (Knowledge-Based and AI-Driven Platform for Therapy
Decision-Support in Hematology) "White paper", for more
information please visit: 
KAIT_White_Paper.pdf
The "AI in Hematology Group" is excited to be part of the ambitious joint consortium of "GenoMed4All - Genomics and Personalised Medicine for all through Artificial Intelligence in Haematological Diseases", selected and approved by the European Commission under the Horizon 2020 Research & Innovation program. For more information visit http://www.genomed4all.eu
Leadership Supervisors | Prof. Uwe Platzbecker (MD)
Prof. Thomas Neumuth (PhD, Biomedical Engineering) |
Group Leader | Alexander Oeser (M.Eng., Engineering Economics) |
Group Members | Nora Grieb (M.Sc., Bioinformatics)
Nicole Schütz (Project coordination) Hyeon Ung Kim (Medical Informatics) |
Selected Publications
Platzbecker U, Fenaux P. Personalized medicine in myelodysplastic syndromes: Wishful thinking or already clinical reality? Haematologica. 2015
Winter S, Shoaie S, Kordasti S, Platzbecker U. Integrating the "Immunome" in the Stratification of Myelodysplastic Syndromes and Future Clinical Trial Design. J. Clin. Oncol. 2020
Gaebel J, Wu H-G, Oeser A, Cypko MA, Stoehr M, Dietz A, Neumuth T, Franke S, Oeltze-Jafra S. Modeling and Processing Up-To-Dateness of Patient Information in Probabilistic Therapy Decision Support. Artificial Intelligence in Medicine. 2020;101842.
Oeser A, Gaebel J, Kuhnt T. Development of an Assistance System for the Intuitive Assessment of Laboratory Findings in Oncology. Current Directions in Biomedical Engineering. 2019;5(1):61–64.
Oeser A, Gaebel J, Dietz A, Wiegand S, Oeltze-Jafra S. Information Architecture for a Patient-Specific Dashboard in Head and Neck Tumor Boards. Int J CARS. 2018;1–8.
Oeser A, Gaebel J, Oeltze-Jafra S, Dietz A. Towards Structural Learning of Bayesian Networks in Head and Neck Oncology. ISBA World Meeting 2018; 2018; Edinburgh.
