Ghassemi Mohammad M, Mark Roger G, Nemati Shamim
Department of Electrical Engineering and Computer Science at the Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, MA 02139, USA.
Department of Biomedical Informatics at Emory University, Atlanta, GA 30322, USA.
Comput Cardiol (2010). 2015 Sep;2015:629-632. doi: 10.1109/CIC.2015.7410989. Epub 2016 Feb 18.
Our objective in this paper was to visualize the evolution of clinical language and sentiment with respect to several common population-level categories including: time in the hospital, age, mortality, gender and race. Our analysis utilized seven years of unstructured free text notes from the Multiparameter Intelligent Monitoring in Intensive Care (MIMIC) database. The text data was partitioned by category and used to generate several high dimensional vector space representations. We generated visualizations of the vector spaces using Distributed Stochastic Neighbor Embedding (tSNE) and Principal Component Analysis (PCA). We also investigated representative words from clusters in the vector space. Lastly, we inferred the general sentiment of the clinical notes toward each parameter by gauging the average distance between positive and negative keywords and all other terms in the space. We found intriguing differences in the sentiment of clinical notes over time, outcome, and demographic features. We noted a decrease in the homogeneity and complexity of clusters over time for patients with poor outcomes. We also found greater positive sentiment for females, unmarried patients, and patients of African ethnicity.
我们在本文中的目标是可视化临床语言和情感相对于几个常见人群层面类别（包括：住院时间、年龄、死亡率、性别和种族）的演变。我们的分析利用了重症监护多参数智能监测（MIMIC）数据库中七年的非结构化自由文本记录。文本数据按类别进行划分，并用于生成几个高维向量空间表示。我们使用分布式随机邻域嵌入（tSNE）和主成分分析（PCA）生成向量空间的可视化。我们还研究了向量空间中聚类的代表性词汇。最后，我们通过测量积极和消极关键词与空间中所有其他术语之间的平均距离，推断临床记录对每个参数的总体情感。我们发现临床记录在情感方面随时间、结果和人口统计学特征存在有趣的差异。我们注意到，预后不良患者的聚类同质性和复杂性随时间下降。我们还发现女性、未婚患者和非洲裔患者的积极情感更强。
