Relation mining from clinical records
Authors: Anita Alicante, Anna Corazza, Francesco Isgrò, Stefano Silvestri (Università di Napoli Federico II, CNR-ICAR, 2016)
Topic: In-domain IR and IE

Abstract
We propose a system to extract entities and relations from a set of clinical records in Italian based on two preceding works AUTHOR and AUTHOR. This approach does not require annotated data and is based on existing domain lexical resources and unsupervised machine learning techniques.

Introduction
The digitization of medical documents in hospitals has produced plenty of information which should be adequately organized. While part of the material, mainly including international scientific publications, is in English, increasingly more material is being created in the language of the country of the medical institution. The main part of the local language material is represented by patient records. They contain important information not only for preparing care plans or solve problems for the particular patient, but also to extract statistics useful for research and also for logistics administration. Automatic processing of such repositories still can not be straightforwardly applied. One of the principal issues to be solved is the automatic extraction of relevant information, usually consisting in entities and relations connecting them AUTHOR. In the cited work, we extensively discuss a domain entity and relation recognition system for Italian. Such step is at the basis of more sophisticated analyses, including semantics-based indexing of documents for improved retrieval, advanced query based information extraction, and the application of ontology-based strategies for privacy protection. General tools, such as TextPro AUTHOR, are not adapted for technical domains such as the medical one, as they are trained on generic documents, rather than domain-specific ones. Furthermore, a lot of tools are available for English and only a few of them have been ported to Italian. Another problem to take into account is the occurrence, in clinical records, of typos and nonstandard abbreviations, in addition to the most usual acronyms. Last but not least, passing from text to knowledge processing raises tricky privacy problems. In fact, especially but not only in small hospitals, obscuring the patient names is not sufficient to hide their identity as the medical information reported in records are often sufficient to reconstruct a precise profiling of the patients. Therefore, ad hoc solutions represent the only way to build effective applications to solve this kind of problems. For example, not only domain entities and relations can help identifying potentially dangerous information, but also ontological information can be exploited to better protect patient privacy AUTHOR. Again, ontologies construction and population are based on entity and relation extraction. Efforts to port systems to languages different from English require, first of all, the development of lexical resources for the considered language. However, they are not sufficient, because of the intrinsic differences between languages. A widely adopted way to tackle such difficulties is represented by machine learning approaches. Although supervised approaches are usually more effective, they require large corpora of annotated data, which are quite expensive to obtain, as they require that domain experts invest time in a long and tedious annotation activity. In the medical domain, staff should invest part of their precious time to annotate data with information about the presence and the type of domain relevant entities and relations in records to be used for the training phase. Things would be much easier if domain experts are only required to check an automatically produced annotation. We therefore propose to integrate a knowledge-based and a text mining approaches to develop an application which requires the expert intervention only to check on medical and pharmaceutical labels associated to groups of relations. More in detail, we propose here to integrate the systems discussed in AUTHOR and in AUTHOR: the former adopts domain dependent lexical resources to extract entities and unsupervised machine learning approaches to decide where relations occur in the text. The latter clusters and labels the extracted relations with an approach based on lexical semantics. The paper is organized with Section~sec:approach detailing the approach implementation and Section~sec:conclusions for conclusions and future works.

Conclusion
The research presented in this paper was partially supported by the national projects CHIS - Cultural Heritage Information System (PON), and BIG4H - Big Data Analytics for E-Health Applications (POR).