Explainable machine learning model based on EEG, ECG, and clinical features for predicting neurological outcomes in cardiac arrest patientCardiac arrestMachine learningQuantitative EEGHeart rate variabilityMulti
we assemble and analyse a continent-wide database of field experiments spanning 10 years and hundreds of thousands of machine-phenotyped populations of ten major crop species. Training an ensemble of machine learning models, using thousands of variables capturing weather, ground sensor, soil, chemi...
Goal of Explainable ML 对于解释性方面有些人的想法是:一定要完全知道ML model如何工作的,但是对于我们人脑来说,我们并不完全知道人脑是如何工作的,但是我们往往会相信人脑做出的决策。所以对于可解释的定义,我们认为模型的解释性被人所接受(你的客户或者老板等),就可以说明模型解释性很强。 Explainable ML 解释性有...
To develop an explainable model for predicting mechanical ventilation (MV) duration in patients with ARDS using the machine learning (ML) approach. Method The number of 1,148, 1,697, and 29 ARDS patients admitted to intensive care units (ICU) in the MIMIC-IV, eICU-CRD, and AmsterdamUMCdb...
Clustering and dimensionality reduction are used in machine learning to uncover hidden patterns, reduce noise, and gain valuable insights from complex datasets.
Explainable Machine Learning P1-Why Does the Model Make This Prediction 到目前為止,我们已经训练了很多很多的模型,你可能我们训练过影像辨识的模型,给它一张图片,它会给你答案,但我们并不满足於此,接下来我们要机器给我们,它得到答案的理由,这个就是 Explainable 的 Machine Learning Why we need Explainable ML...
有没有model是Interpretable,也是powerful的呢 ? 决策树可以interpretable,也是比较powerful的 2 Local Explanation 方法一: 对于输入的 , 我将其分成components , 每个component由一个像素,或者一小块组 成 目标是知道每个component对making the decision的重要性有多少,那么可以通过remove或者 modify其中一个component的值...
Model Explaination 1. 为什么要解释算法 在某些场景,我们在使用一些机器学习模型处理数据以得到结果时,往往也会寻求一个解释,也就是数据的结果是从何而来的。 举个例子,医生在使用医疗诊断的模型获得诊断结果时。医生或是病人不可能仅仅依靠得到的结果就决定病情。重要的是,通过结果得到的过程,也就是结果的解释最终确...
3、GLOBAL EXPLANATION:EXPLAIN THE WHOLE MODEL Question: What do you think a “cat” looks like? 4、Using a model to explain another 1、Explainable ML 1)是什么? EXPLAINABLEMACHINE LEARNING :让机器在判断input的同时,需要知道为什么这样判断。
Objective This study aimed to enhance the performance of risk assessment models in this patient population by developing a predictive model for adverse outcomes using various machine learning (ML) techniques. Methods This multicenter cohort study included 326 patients diagnosed with severe AS and HFpEF ...