Today, there are approximately 350 million people living with 7,000 rare diseases worldwide. In the United States alone, 25 to 30 million people are affected – more than half of whom are children – while treatments are available for just 5% of them. As these diseases are exceedingly rare, initial misdiagnosis is common and underdiagnosis is extensive. On average, it can take more than seven years for patients with a rare disease to receive an accurate diagnosis – which creates a significant delay to critical treatment options for these patients.
We must find a more reliable means of detecting rare diseases to ensure patients get the treatments they need. With advances in machine learning and deep learning technologies, we can now begin to pursue answers to questions about these diseases regardless of the complexity in faster, more compelling ways.
使用机器学习检测罕见疾病时，存在两个主要的挑战。首先，这些疾病的低患病率限制了训练数据中阳性受试者的数量（即有疾病确定诊断的患者）。因此，疾病模式很难提取。
This is made more difficult by the fact that many rare diseases have not been assigned an ICD-10 code. ICD-10 codes are used by physicians to identify specific diseases or symptoms. In the case of many rare diseases, each physician must use their judgment to select an ICD-10 code that best accounts for a patient’s symptoms. In practice, this means a set of patients who in fact share the same disease could each be assigned a different ICD-10 code.
其次，由于需要正确诊断的罕见疾病所需的长时间，有许多患者有不确定的诊断。尽管我们不知道这些患者是谁，但他们的存在可能会有所帮助。近年来，大规模电子健康记录（EHR）数据的可用性进一步使深度学习模型的培训能够进行准确的预测健康。lol买外围用什么软件
克服误报障碍
尽管人工智能在检测未诊断的患者方面具有巨大的希望，但由于难以区分罕见疾病的患病率较低，因此很难区分患者的障碍，因此障碍较高。为了应对这些挑战，我们需要继续发展如何应用机器学习。一种方法是使用图案增强来更好地保存和丰富目标疾病的关键模式。
与伊利诺伊大学Urbana-Champaign大学的Jimeng Sun合作，我在IQVIA的团队发表了一篇论文AAAI 2020outlining a Complementary Pattern Augmentation (CONAN) framework for rare and low prevalence disease detection.
柯南使用对抗性学习的想法。首先，发电机学会创建合理但假的耐心样品。然后，疾病检测器旨在区分阴性和阳性患者样本。这确立了所谓的最小游戏和疾病探测器之间的最小游戏。训练后，疾病检测器可用于检测阳性患者。对现实世界数据集的实验表明性能很强。Read the full paper here。
Improving the ability to detect rare diseases is a critical step in finding answers to the perplexing questions surrounding rare diseases, and ultimately ensuring patients can be properly diagnosed and treated. To build on this success, deep expertise and creativity are required to challenge the way things have been done previously and to make connections, with accuracy, even when it seems impossible.
在IQVIA，我们认为对更健康的世界的追求始于解决曾经看似无法解决的问题，并lol买外围用什么软件为曾经认为无法治疗的疾病提供疗法。在这种追求中，lpl哪里可以下外围领先。这是一种革命性的方法，可以在医疗保健中解决问题，利用技术，数据科学和人类创造力的进步来改善人类健康。lol买外围用什么软件
有关罕见疾病和孤儿药的更多信息iqviaInstitute report.