Risk adjustment has become an increasingly important tool in healthcare. It has been extensively applied to payment adjustment for health plans to reflect the expected cost of providing coverage for members. Risk adjustment models are typically estimated using linear regression, which does not fully exploit the information in claims data. Moreover, the development of such linear regression models requires substantial domain expert knowledge and computational effort for data preprocessing. In this paper, we propose a novel approach for risk adjustment that uses semantic embeddings to represent patient medical histories. Embeddings efficiently represent medical concepts learned from diagnostic, procedure, and prescription codes in patients' medical histories. This approach substantially reduces the need for feature engineering. Our results show that models using embeddings had better performance than a commercial risk adjustment model on the task of prospective risk score prediction.
Le and Mikolov @cite_3 extended the models to groups of words, including sentences, paragraphs, and entire documents. In their Distributed Memory Model of Paragraph Vectors (PV-DM), which is analogous to the CBOW model, a paragraph (or other chosen word group) vector is added as a predictor to the context words' vectors. The paragraph vector "remembers" information about the paragraph beyond the selected context words and thus helps to predict the target word. The Distributed Bag of Words version of Paragraph Vectors (PV-DBOW) is analogous to the skip-gram model and uses only the paragraph vector to predict context words from the same paragraph. PV-DM provides the advantage of accounting for the sequence of the words in the paragraph, while PV-DBOW is less computationally intensive. Both, which are collectively known as , allow efficient learning of paragraph vectors, even though different paragraphs may vary in length.
@cite_3
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Many machine learning algorithms require the input to be represented as a fixed-length feature vector. When it comes to texts, one of the most common fixed-length features is bag-of-words. Despite their popularity, bag-of-words features have two major weaknesses: they lose the ordering of the words and they also ignore semantics of the words. For example, "powerful," "strong" and "Paris" are equally distant. In this paper, we propose Paragraph Vector, an unsupervised algorithm that learns fixed-length feature representations from variable-length pieces of texts, such as sentences, paragraphs, and documents. Our algorithm represents each document by a dense vector which is trained to predict words in the document. Its construction gives our algorithm the potential to overcome the weaknesses of bag-of-words models. Empirical results show that Paragraph Vectors outperform bag-of-words models as well as other techniques for text representations. Finally, we achieve new state-of-the-art results on several text classification and sentiment analysis tasks.