Medication Adherence Dataset

该数据集包含了27名参与者的数据，这些参与者每天服用药物以管理他们的心血管疾病。该数据集被用于训练长短期记忆网络（LSTM）和卷积神经网络（CNN）模型，目的是预测药物依从性。研究结果表明，将未来知识纳入模型对于提高预测准确性具有重要意义。该数据集的规模涉及27名参与者，任务是对药物依从性进行预测。

This dataset contains data from 27 participants who took daily medications to manage their cardiovascular diseases. It was used to train Long Short-Term Memory (LSTM) and Convolutional Neural Network (CNN) models for predicting medication adherence. Study results demonstrate that incorporating future knowledge into the models is critical for enhancing prediction accuracy. The sample size of this dataset includes 27 participants, and the core predictive task targets medication adherence.

搜集汇总

背景与挑战

背景概述

该数据集包含27名心血管疾病患者的数据，用于训练LSTM和CNN模型以预测药物依从性。研究发现，纳入未来知识能显著提升预测准确性。