摘要
机器人与机器学习的新闻编辑每日新闻-机器学习的新研究是一篇报道的主题。根据NewsR X记者从德国乌尔姆发回的新闻报道，研究表明：“数字表型可以成为一种创新的、不引人注目的方法来提高失眠的检出率。本研究探讨了智能手机使用特征(SUF)与失眠症状之间的关系以及对失眠症状检测的预测价值。”我们的新闻记者从乌尔姆大学的研究中获得了一句话，“在一项德国便利样本的观察性研究中，获得了DEX(ISI)中的失眠严重程度和前7天智能手机使用数据（例如屏幕处于活动状态的时间、夜间屏幕处于非活动状态的最长时间）。对于机器学习模型(ML)的规格,80%的数据分配给t raining,20%的数据分配给测试,5次交叉验证,6种算法(支持向量机,XGBoost,随机森林,K-nearest-Neigh Bor,Naive Bayes,K-Nearest-Neigh Bor,Naive Bayes,SUF)。对15.752名受试者（51.1%女性，平均ISI＝10.23，平均年龄＝41.92）进行了分析，发现部分SU F与失眠症状之间的相关性很小，在ML模型中，敏感性较低，在检验子样本中介于0.05～0.27之间，随机森林和朴素贝叶斯是表现最好的算法。他们在测试子样本中的AUC（分别为0.57，0.58）表明识别能力低。鉴于ML模型的相关性和识别能力低，本研究测量的SU Fs似乎不足以检测INSO MNIA症状。
Abstract
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New research on Machine Learning is th e subject of a report. According to news originating from Ulm, Germany, by NewsR x correspondents, research stated, “Digital phenotyping can be an innovative and unobtrusive way to improve the detection of insomnia. This study explores the c orrelations between smartphone usage features (SUF) and insomnia symptoms and th eir predictive value for detecting insomnia symptoms.” Our news journalists obtained a quote from the research from Ulm University, “In an observational study of a German convenience sample, the Insomnia Severity In dex (ISI) and smartphone usage data (e.g., time the screen was active, longest t ime the screen was inactive in the night) for the previous 7 days were obtained. SUF (e.g., min, mean) were calculated from the smartphone usage data. Correlati on analyses between the ISI and SUF were conducted. For the specification of the machine learning models (ML), 80 % of the data was allocated to t raining, 20 % to testing, and five-fold cross-validation was used. Six algorithms (support vector machine, XGBoost, Random Forest, k-Nearest-Neigh bor, Naive Bayes, and Logistic Regressions) were specified to predict ISI scores 15. 752 participants (51.1 % female, mean ISI = 10.23, mean age = 41.92) were included in the analyses. Small correlations between some of the SU F and insomnia symptoms were found. In the ML models, sensitivity was low, rangi ng from 0.05 to 0.27 in the testing subsample. Random Forest and Naive Bayes wer e the best-performing algorithms. Yet, their AUCs (0.57, 0.58 respectively) in t he testing subsample indicated a low discrimination capacity. Given the small ma gnitude of the correlations and low discrimination capacity of the ML models, SU Fs, as measured in this study, do not appear to be sufficient for detecting inso mnia symptoms.”
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