■在接受全身治疗的癌症患者中使用智能手机应用程序可以促进早期发现症状和治疗副作用，并且可以得到机器学习（ML）的支持，以及时适应治疗并减少不良事件和计划外入院。
■我们旨在创建一个预警系统（EWS），以预测需要采取支持性干预措施以防止计划外访问的情况。为此，动态收集的标准化电子患者报告结果(ePRO)数据根据患者的个人旅程进行分析.关于福祉的信息，重要参数，药物,和自由文本也被考虑用于建立混合ML模型。我们的目标是整合ML在筛选大量数据方面的优势和人类专家的长期经验。鉴于高度不平衡的数据集的局限性（仅存在很少的不良事件）以及人类在监督此类事件的所有可能原因方面的局限性，我们假设应该可以将两者结合起来，以部分克服这些限制。
■通过采用白盒ML算法（即，规则学习者)，从患者数据中学习规则(即，EPRO,重要参数，免费文本)是通过医疗设备智能手机应用程序捕获的。这些规则表明患者经历了计划外就诊的情况，因此，在EWS中捕获为警报触发器。每个规则都是根据成本矩阵进行评估的，其中假阴性(FN)的成本高于假阳性(FP，即，假警报)。然后根据成本对规则进行排名，并优先考虑最便宜的规则。最后,两名肿瘤专家对优先级较高的规则进行了审查,以进行合理性检查,并在附加条件下进行扩展.这种混合方法包括应用敏感的ML算法，产生几种潜在的不可靠的，但是完全由人类解释和修改的规则，然后可以由人类专家进行调整。
■从一组214名患者和超过16,000个可用数据条目中，机器学习的规则集在整个数据集上实现了19%的召回率和5%的准确率.我们将这种表现与人类专家定义的预测不良事件的一组条件进行了比较。这个“人类基线”没有发现我们数据集中记录的任何不良事件，即，它的召回率和准确率为0%。尽管我们的机器学习方法预计会有更多的结果，所涉及的医学专家a)理解并能够理解规则，b)认为有能力建议修改规则，其中一些可能会提高它们的精确度。建议的规则修改包括，例如，增加或收紧某些条件，使它们不那么敏感或改变规则的后果:有时进一步监测情况，应用某些测试（如CRP测试）或应用一些简单的疼痛缓解措施被认为是足够的，没有必要与医生进行昂贵的咨询。因此，我们可以得出结论，将机器学习作为一种鼓舞人心的工具，可以帮助人类专家为EWS制定规则。虽然人类似乎缺乏在没有这种支持的情况下定义这种规则的能力，他们能够修改规则，以提高他们的精确度和普适性。
■从动态ePRO数据集学习规则可用于协助人类专家在门诊环境中为癌症患者建立早期预警系统。
UNASSIGNED: The use of smartphone apps in cancer patients undergoing systemic treatment can promote the early detection of symptoms and therapy side effects and may be supported by machine learning (ML) for timely adaptation of therapies and reduction of adverse events and unplanned admissions.
UNASSIGNED: We aimed to create an Early Warning System (EWS) to predict situations where supportive interventions become necessary to prevent unplanned visits. For this, dynamically collected standardized electronic patient reported outcome (ePRO) data were analyzed in context with the patient\'s individual journey. Information on well-being, vital parameters, medication, and free text were also considered for establishing a hybrid ML model. The goal was to integrate both the strengths of ML in sifting through large amounts of data and the long-standing experience of human experts. Given the limitations of highly imbalanced datasets (where only very few adverse events are present) and the limitations of humans in overseeing all possible cause of such events, we hypothesize that it should be possible to combine both in order to partially overcome these limitations.
UNASSIGNED: The prediction of unplanned visits was achieved by employing a white-box ML algorithm (i.e., rule learner), which learned rules from patient data (i.e.,
ePROs, vital parameters, free text) that were captured via a medical device smartphone app. Those rules indicated situations where patients experienced unplanned visits and, hence, were captured as alert triggers in the EWS. Each rule was evaluated based on a cost matrix, where false negatives (FNs) have higher costs than false positives (FPs, i.e., false alarms). Rules were then ranked according to the costs and priority was given to the least expensive ones. Finally, the rules with higher priority were reviewed by two oncological experts for plausibility check and for extending them with additional conditions. This hybrid approach comprised the application of a sensitive ML algorithm producing several potentially unreliable, but fully human-interpretable and -modifiable rules, which could then be adjusted by human experts.
UNASSIGNED: From a cohort of 214 patients and more than 16\'000 available data entries, the machine-learned rule set achieved a recall of 19% on the entire dataset and a precision of 5%. We compared this performance to a set of conditions that a human expert had defined to predict adverse events. This \"human baseline\" did not discover any of the adverse events recorded in our dataset, i.e., it came with a recall and precision of 0%. Despite more plentiful results were expected by our machine learning approach, the involved medical experts a) had understood and were able to make sense of the rules and b) felt capable to suggest modification to the rules, some of which could potentially increase their precision. Suggested modifications of rules included e.g., adding or tightening certain conditions to make them less sensitive or changing the rule consequences: sometimes further monitoring the situation, applying certain test (such as a CRP test) or applying some simple pain-relieving measures was deemed sufficient, making a costly consultation with the physician unnecessary. We can thus conclude that it is possible to apply machine learning as an inspirational tool that can help human experts to formulate rules for an EWS. While humans seem to lack the ability to define such rules without such support, they are capable of modifying the rules to increase their precision and generalizability.
UNASSIGNED: Learning rules from dynamic ePRO datasets may be used to assist human experts in establishing an early warning system for cancer patients in outpatient settings.
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