TY - JOUR AU - Hoffmann, Katja AU - Cazemier, Katja AU - Baldow, Christoph AU - Schuster, Silvio AU - Kheifetz, Yuri AU - Schirm, Sibylle AU - Horn, Matthias AU - Ernst, Thomas AU - Volgmann, Constanze AU - Thiede, Christian AU - Hochhaus, Andreas AU - Bornhäuser, Martin AU - Suttorp, Meinolf AU - Scholz, Markus AU - Glauche, Ingmar AU - Loeffler, Markus AU - Roeder, Ingo PY - 2020 DA - 2020/02/10 TI - Integration of mathematical model predictions into routine workflows to support clinical decision making in haematology JO - BMC Medical Informatics and Decision Making SP - 28 VL - 20 IS - 1 AB - Individualization and patient-specific optimization of treatment is a major goal of modern health care. One way to achieve this goal is the application of high-resolution diagnostics together with the application of targeted therapies. However, the rising number of different treatment modalities also induces new challenges: Whereas randomized clinical trials focus on proving average treatment effects in specific groups of patients, direct conclusions at the individual patient level are problematic. Thus, the identification of the best patient-specific treatment options remains an open question. Systems medicine, specifically mechanistic mathematical models, can substantially support individual treatment optimization. In addition to providing a better general understanding of disease mechanisms and treatment effects, these models allow for an identification of patient-specific parameterizations and, therefore, provide individualized predictions for the effect of different treatment modalities. SN - 1472-6947 UR - https://doi.org/10.1186/s12911-020-1039-x DO - 10.1186/s12911-020-1039-x ID - Hoffmann2020 ER -