Natural language processing in healthcare data

点击翻译按钮获取中文摘要

Word embeddings (i.e., 'word vectors' or 'distributed representations') are dense numeric representations of words, which serve as input to various statistical machine learning methods. Typically, by optimizing contextual statistics, these embeddings induce latent dimensions that encode aspects of morphology, syntax, and even semantics. The results therefore can capture meaningful relationships among concepts in the data not afforded by traditional methods.******The Vector Institute is partnering with the Institute for Clinical Evaluative Sciences (ICES) around the collaborative use of the EMRALD corpus, which consists of text from a variety of primary care sources (e.g., consult notes, referrals, risk factors, past medical history) sourced from hundreds of doctors in Ontario. EMRALD is an order of magnitude larger, in vocabulary and overall size, than Google's news corpus which is one of the de facto corpora used for training embeddings. Currently, the extremely large vocabulary size appears to produce two main consequences: i) a preponderance of technical terms and their many variants, and b) spelling mistakes. These consequences lead to very sparse contextual matrices.******We have three primary goals in this program of research:******1) To enrich word embeddings with ontological information. Our team has developed a method of 'enriching' embeddings using a multi-task learning approach and normative lexical data, from crowd-sourced statistics. For example, enriching the embedding process with norms of sentiment increases the accuracy not only of sentiment analysis, but out--of--domain tasks as well, e.g., machine translation. Here, we intend to apply a similar approach but with structured ontological information from medical texts and resources. ******2) To produce explainable and private models. It is increasingly important to audit decisions made by classifiers, and to ensure the privacy of personal information in their respective models. For instance, it was recently shown that it is possible to re-identify patients in an anonymized data set using another set of minimally linked data. In order to increase the explainability of our models, we will apply methods such as LIME , text--based 'anchoring', and differential privacy. We will explore whether generative adversarial networks can also synthesize distributions with similar properties. ******3) To perform longitudinal classification. An initial goal will be to use the structured data in EMRALD to perform supervised classification of diagnostic codes given clinical notes. Given the longitudinal nature of the data, this will include recurrent neural networks and convolutional neural networks with attention. We will similarly explore semi-supervised learning either by removing some structured data or adding noise to the labels. The long-term aim is to combine these approaches in order to predict various long-term trends and human trajectories.**