In intensive care units, most patients are usually in critical conditions which require physicians to make immediate diagnosis and treatments. However, not every patient could get the best treatment because it highly related to the physician’s expertise. With the development of the machine learning, many studies have started trying to develop models that can learn the representations in Electronic Health Records (EHR) and make accurate predictions on clinical tasks. On code assignment tasks, models based on convolutional neural networks (CNN) or Recurrent Neural Networks (RNN) have shown promising results but their performances are still insufficient to be applied on real-world applications due to (1) the large number of codes and (2) the length of the document. Here, we propose a Convolutional Neural Network with Multi-label attention mechanism (Multi-Label AT- CNN) model that predict ICD-9 code assignments by learning the base representations of the clinical notes from EHRs.