In recent years the use of electronic medical records has accelerated resulting in large volumes of medical data when a patient visits a healthcare facility. As a first step towards reimbursement healthcare institutions need to associate ICD-10 billing codes to these documents. This is done by trained clinical coders who may use a computer assisted solution for shortlisting of codes. In this work, we present our work to build a machine learning based scalable system for predicting ICD-10 codes from electronic medical records. We address data imbalance issues by implementing two system architectures using convolutional neural networks and logistic regression models. We illustrate the pros and cons of those system designs and show that the best performance can be achieved by leveraging the advantages of both using a system combination approach.

We describe an effort to improve standard reference-based metrics for Machine Translation (MT) evaluation by enriching them with Confidence Estimation (CE) features and using a learning mechanism trained on human annotations. Reference-based MT evaluation metrics compare the system output against reference translations looking for overlaps at different levels (lexical, syntactic, and semantic). These metrics aim at comparing MT systems or analyzing the progress of a given system and are known to have reasonably good correlation with human judgments at the corpus level, but not at the segment level. CE metrics, on the other hand, target the system in use, providing a quality score to the end-user for each translated segment. They cannot rely on reference translations, and use instead information extracted from the input text, system output and possibly external corpora to train machine learning algorithms. These metrics correlate better with human judgments at the segment level. However, they are usually highly biased by difficulty level of the input segment, and therefore are less appropriate for comparing multiple systems translating the same input segments. We show that these two classes of metrics are complementary and can be combined to provide MT evaluation metrics that achieve higher correlation with human judgments at the segment level.

This work studies the viability of performing heterogeneous automatic MT error analyses. Error analysis is, undoubtly, one of the most crucial stages in the development cycle of an MT system. However, often not enough attention is paid to this process. The reason is that performing an accurate error analysis requires intensive human labor. In order to speed up the error analysis process, we suggest partially automatizing it by having automatic evaluation metrics play a more active role. For that purpose, we have compiled a large and heterogeneous set of features at different linguistic levels and at different levels of granularity. Through a practical case study, we show how these features provide an effective means of ellaborating interpretable and detailed automatic reports of translation quality.

We present the IQMT Framework for Machine Translation Evaluation Inside QARLA. IQMT offers a common workbench in which existing evaluation metrics can be utilized and combined. It provides i) a measure to evaluate the quality of any set of similarity metrics (KING), ii) a measure to evaluate the quality of a translation using a set of similarity metrics (QUEEN), and iii) a measure to evaluate the reliability of a test set (JACK). The first release of the IQMT package is freely available for public use. Current version includes a set of 26 metrics from 7 different well-known metric families, and allows the user to supply its own metrics. For future releases, we are working on the design of new metrics that are able to capture linguistic aspects of translation beyond lexical ones.