Rommel makes it particularly complex because of the perception that he was a gentleman general, that he was part of 'The War Without Hate.' And we can't buy into any of those myths … we want to be honest about what he was fighting for and what he represented … there was a very dark side to the war in Africa. Depicting any major military figure of World War II is … a challenge. Set in North Africa and centered on the Deutsch Afrika Corps under the storied Erwin Rommel, Relic is aware that it's a tricky thing to do an Axis point-of-view campaign. He's great at correcting us."Ĭompany of Heroes 3 will be shipping with a second, more linear campaign as well, though. That's not exactly right.' … When we're referring to squads he'll say, 'No, it's guastatori!' and we're all saying 'gustatori' or 'poseglieri' or whatever. He'll say, 'Ah, that's not really how they'd say it. He calls us out on language when we're using curse words and we're trying to include some Italian flavor. And he is so committed to making sure we represent Italy and Italians correctly. "One of our most committed players … Marco Fiore, he joined us recently as a developer. Relic is aware that it's a tricky thing to do an Axis point-of-view campaign. With the Italian campaign being one of the most untold parts of World War II, Relic was fortunate enough to get some feedback from a local. The US commander really wanted to make a push for Rome, and the British commander wanted to play it a little more conservatively … you're going to be right in the middle of this mix and the partisan leader, and they're going to be pushing and pulling for your attention." "So we've taken from the pages of history this rivalry that happened between some real commanders … the US and British Commanders that very much butted heads about what should be done during the Italian Campaign. The commanders of the conventional allied forces will also sometimes require you to make a choice between them. Whether or not to help the partisans isn't your only concern, though. It's only one of the various important narrative decisions you'll be able to make. Valenti and her partisans will not appreciate it if you follow the historical path, which can lock you out of not only the bonuses she can provide, but also certain story beats later in the campaign. In real life, the Allies bombed the Abbey without knowing for sure whether the Axis forces were using it as a defensive position or were merely camped around it, which has remained controversial to this day. Monte Cassino is an abbey in Latium built in 529 A.D.
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In our experiments, the doctors achieve an average diagnostic accuracy of 71.40%, while the associative algorithm achieves a similar accuracy of 72.52%, placing in the top 48% of doctors in our cohort. We compare the accuracy of our counterfactual algorithms to a state-of-the-art associative diagnostic algorithm and a cohort of 44 doctors, using a test set of 1671 clinical vignettes. To resolve this, we present a causal definition of diagnosis that is closer to the decision making of clinicians, and derive counterfactual diagnostic algorithms to validate this approach. We show that failure to disentangle correlation from causation places strong constraints on the accuracy of associative diagnostic algorithms, sometimes resulting in sub-optimal or dangerous diagnoses. Here, we argue that diagnosis is fundamentally a counterfactual inference task. Counterfactual inference sits at the top of this hierarchy, and allows one to ascribe causal explanations to data. As noted by Pearl, associative inference is the simplest in a hierarchy of possible inference schemes 24, 25, 26. This is in contrast to how doctors perform diagnosis, selecting the diseases which offer the best causal explanations for the patients symptoms. This raises the question, why do existing approaches struggle with differential diagnosis? All existing diagnostic algorithms, including Bayesian model-based and Deep Learning approaches, rely on associative inference-they identify diseases based on how correlated they are with a patients symptoms and medical history. Despite significant research efforts and renewed commercial interest, diagnostic algorithms have struggled to achieve the accuracy of doctors in differential diagnosis 17, 18, 19, 20, 21, 22, 23, where there are multiple possible causes of a patients symptoms. In particular, machine learning assisted diagnosis promises to revolutionise healthcare by leveraging abundant patient data to provide precise and personalised diagnoses 8, 9, 10, 11, 12, 13, 14, 15, 16. In recent years, artificial intelligence and machine learning have emerged as powerful tools for solving complex problems in diverse domains 5, 6, 7. These errors are particularly common when diagnosing patients with serious medical conditions, with an estimated 20% of these patients being misdiagnosed at the level of primary care 3 and one in three of these misdiagnoses resulting in serious patient harm 1, 4. In the US alone an estimated 5% of outpatients receive the wrong diagnosis every year 1, 2. Providing accurate and accessible diagnoses is a fundamental challenge for global healthcare systems. Our results show that causal reasoning is a vital missing ingredient for applying machine learning to medical diagnosis. While the associative algorithm achieves an accuracy placing in the top 48% of doctors in our cohort, our counterfactual algorithm places in the top 25% of doctors, achieving expert clinical accuracy. We compare our counterfactual algorithms to the standard associative algorithm and 44 doctors using a test set of clinical vignettes. To overcome this, we reformulate diagnosis as a counterfactual inference task and derive counterfactual diagnostic algorithms. We show that this inability to disentangle correlation from causation can result in sub-optimal or dangerous diagnoses. However, existing machine learning approaches to diagnosis are purely associative, identifying diseases that are strongly correlated with a patients symptoms. In medical diagnosis a doctor aims to explain a patient’s symptoms by determining the diseases causing them. Machine learning promises to revolutionize clinical decision making and diagnosis. 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