Entanglements and vessel strikes impact large whales worldwide. Post-event health status is often unknown because whales are seen once or over short spans that conceal long-term health declines. Well-studied populations with high site fidelity verified by photo-ID offer opportunity to confirm deaths, health declines and recoveries. We used known outcome entanglements and vessel strikes of right whales (Eubalaena glacialis) and humpback whales (Megaptera novaeangliae) to model probabilities of deaths, health declines and recoveries with Random Forest (RF) classification trees. Variables included presence or absence of phrases from case narratives (‘deep laceration’, ‘cyamid’, ‘healing’, ‘superficial’) and a categorical variable for vessel size. Health status post-entanglement was correctly classified in 95.7% of right whale and 93.6% of humpback whale cases (expected by chance=50%). Health status post-vessel strike was correctly classified in 91.4% of right whale and 88.6% of humpback whale cases. Important variables included cyamid presence, emaciation, discolored skin, constricting entanglements, gear-free resightings, superficial or healing lacerations, and vessel size. Cross-validated RF models were applied to unknown outcome cases to estimate the probability of deaths, health declines and recoveries. Total serious injuries (probability of death or health decline > 0.50) assigned by RF were nearly equal to current injury assessment methods applied by biologists for known outcomes. However, RF consistently predicted higher serious injury totals for unknown outcomes, suggesting that current assessment methods may underestimate risk for cases lacking details or long-term observations. Advantages of the RF method include: 1) risk models are based on known outcomes; 2) unknown outcomes are assigned post-event health status probabilities; and 3) identification of important predictor variables improves data collection standards.

纠缠和船只撞击影响着全世界的大型鲸鱼。事件后的健康状况通常是未知的，因为鲸鱼被看到一次或在短时间内掩盖了长期健康下降的情况。通过照片 ID 验证的具有高现场保真度的经过充分研究的人群提供了确认死亡、健康状况下降和康复的机会。我们使用已知的结果纠缠和露脊鲸的船只撞击（冰川桉) 和座头鲸 (巨翅目) 使用随机森林 (RF) 分类树对死亡、健康下降和恢复的概率进行建模。变量包括病例叙述中是否存在短语（“深裂伤”、“Cyamid”、“愈合”、“表面”）和血管大小的分类变量。在 95.7% 的露脊鲸和 93.6% 的座头鲸病例中，纠缠后的健康状况被正确分类（预期概率 = 50%）。在 91.4% 的露脊鲸和 88.6% 的座头鲸病例中，船舶撞击后的健康状况被正确分类。重要的变量包括纤维质的存在、消瘦、皮肤变色、收缩缠结、无齿轮重新定位、浅表或愈合裂伤和血管大小。将交叉验证的 RF 模型应用于未知结果病例，以估计死亡、健康状况下降和康复的概率。RF 分配的严重伤害总数（死亡或健康下降的概率 > 0.50）几乎等于生物学家针对已知结果应用的当前伤害评估方法。然而，RF 始终预测未知结果的严重伤害总数更高，这表明当前的评估方法可能低估了缺乏细节或长期观察的病例的风险。RF 方法的优点包括： 1) 风险模型基于已知结果；2) 未知结果被分配事件后健康状态概率；3) 重要预测变量的识别提高了数据收集标准。RF 一直预测未知结果的严重伤害总数较高，这表明当前的评估方法可能低估了缺乏细节或长期观察的病例的风险。RF 方法的优点包括： 1) 风险模型基于已知结果；2) 未知结果被分配事件后健康状态概率；3) 重要预测变量的识别提高了数据收集标准。RF 一直预测未知结果的严重伤害总数较高，这表明当前的评估方法可能低估了缺乏细节或长期观察的病例的风险。RF 方法的优点包括： 1) 风险模型基于已知结果；2) 未知结果被分配事件后健康状态概率；3) 重要预测变量的识别提高了数据收集标准。