Which medication works for which patient? This is one of the central questions in oncology \u2013 and one that remains difficult to answer. Machine learning opens up new possibilities here, but for clinical use, predictive performance alone is not enough: doctors need to be able to trust a model\u2019s output.<\/p>\n\n\n\n
Genau hier setzt die neue Arbeit von Forschenden der Universit\u00e4t des Saarlandes<\/a>, der University Medical Center G\u00f6ttingen<\/a>, ETH Zurich<\/a> and CAIMed<\/a> an. Das von ihnen entwickelte Verfahren MORGOTH<\/em><\/strong> verbessert nicht nur die Genauigkeit von Vorhersagen zur Medikamentenwirksamkeit, sondern macht auch transparent, wie sicher das Modell bei einer gegebenen Vorhersage ist. F\u00fcr Behandlungsentscheidungen ist diese Nachvollziehbarkeit mindestens so wichtig wie die Vorhersage selbst.<\/p>\n\n\n\n The study also surfaces a methodologically relevant finding: previous comparisons between different model types contained a systematic bias \u2013 when evaluated on equal footing, results differ from what had previously been assumed.<\/p>\n\n\n\n MORGOTH<\/em><\/strong> is freely available to the research community as an open-source package. The paper was published open access in the journal Digital Discovery<\/em>\u00a0by the Royal Society of Chemistry<\/a>.<\/p>\n\n\n\n