In the upcoming years, artificial intelligence (AI) is going to transform the practice of medicine in most of its specialties. Deep learning can help achieve better and earlier problem detection, while reducing errors on diagnosis. By feeding a deep neural network (DNN) with the data from a low-cost and low-accuracy sensor array, we demonstrate that it becomes possible to significantly improve the measurements' precision and accuracy. The data collection is done with an array composed of 32 temperature sensors, including 16 analog and 16 digital sensors. All sensors have accuracies between 0.5-2.0$^\circ$C. 800 vectors are extracted, covering a range from to 30 to 45$^\circ$C. In order to improve the temperature readings, we use machine learning to perform a linear regression analysis through a DNN. In an attempt to minimize the model's complexity in order to eventually run inferences locally, the network with the best results involves only three layers using the hyperbolic tangent activation function and the Adam Stochastic Gradient Descent (SGD) optimizer. The model is trained with a randomly-selected dataset using 640 vectors (80% of the data) and tested with 160 vectors (20%). Using the mean squared error as a loss function between the data and the model's prediction, we achieve a loss of only 1.47x10$^{-4}$ on the training set and 1.22x10$^{-4}$ on the test set. As such, we believe this appealing approach offers a new pathway towards significantly better datasets using readily-available ultra low-cost sensors.
翻译:在未来几年, 人工智能( AI) 将在未来几年里改变其大部分专业的医学实践。 深层次学习有助于更好和更早地发现问题, 并减少诊断错误。 通过向深神经网络( DNN) 提供低成本和低精度传感器阵列的数据, 我们证明可以大幅提高测量的精确度和准确性。 数据收集是用一个由32个温度传感器组成的阵列完成的, 包括16个模拟传感器和16个数字传感器。 所有传感器都有0. 5-2. 0 ⁇ circ$ C. 800 矢量被提取, 范围从30美元到45美元不等的问题检测, 同时减少诊断错误。 为了改进温度读数, 我们用机器学习通过 DNNN 进行线性回归分析。 为了尽量降低模型的复杂性, 以便最终在本地运行10, 最优秀的网络只涉及三层, 使用超偏调调调调色调激活功能, 和亚当· 托切斯特利特· 源普( SGD) 优化。 800 矢量的矢量矢量介被提取,, 从30到45美元不等的矢量, 。 为了改进模型, 我们用一个随机的测试到160级的矢量级的矢量测试数据, 测试, 使用一个模型, 将使用一个40级的矢量测算为1, 我们测测算为1, 测试测为1, 以160x, 以100美元, 我们测为1, 测试一个40 。