Civil registration and vital statistics systems capture birth and death events to compile vital statistics and to provide legal rights to citizens. Vital statistics are a key factor in promoting public health policies and the health of the population. Medical certification of cause of death is the preferred source of cause of death information. However, two thirds of all deaths worldwide are not captured in routine mortality information systems and their cause of death is unknown. Verbal autopsy is an interim solution for estimating the cause of death distribution at the population level in the absence of medical certification. A Verbal Autopsy (VA) consists of an interview with the relative or the caregiver of the deceased. The VA includes both Closed Questions (CQs) with structured answer options, and an Open Response (OR) consisting of a free narrative of the events expressed in natural language and without any pre-determined structure. There are a number of automated systems to analyze the CQs to obtain cause specific mortality fractions with limited performance. We hypothesize that the incorporation of the text provided by the OR might convey relevant information to discern the CoD. The experimental layout compares existing Computer Coding Verbal Autopsy methods such as Tariff 2.0 with other approaches well suited to the processing of structured inputs as is the case of the CQs. Next, alternative approaches based on language models are employed to analyze the OR. Finally, we propose a new method with a bi-modal input that combines the CQs and the OR. Empirical results corroborated that the CoD prediction capability of the Tariff 2.0 algorithm is outperformed by our method taking into account the valuable information conveyed by the OR. As an added value, with this work we made available the software to enable the reproducibility of the results attained with a version implemented in R to make the comparison with Tariff 2.0 evident.

Electronic Health Records (EHRs) convey valuable information. Experts in clinical documentation read the report, understand the prior work, procedures, tests carried out, and encode the EHRs according to the International Classification of Diseases (ICD). Assigning these codes to the EHRs helps to share information, and extract statistics. In this paper, we explore computer-aided multi-label classification approaches. While Natural Language Understanding has evolved for clinical text mining, there is still a gap for languages other than English. Language-modeling aware Transformers has demonstrated state of the art approaches through exploiting contextual dependencies. Here we focus on EHRs written in Spanish, and try to benefit from the Language Model itself, with unannotated corpus with less data but in-house, in-domain and closely-related EHRs to that of the downstream task. The International Classification of Diseases coding scheme is hierarchical, but its synergies among hierarchical levels are rarely exploited. In this work, we implement and release a hierarchical head for multi-label classification, which benefits from the hierarchy of the ICD via multi-task classification.

The international standard to ascertain the cause of death is medical certification. However, in many low and middle-income countries, the majority of deaths occur outside of health facilities. In these cases, Verbal Autopsy (VA), the narrative provided by a family member or friend together with a questionnaire is designed by the World Health Organization as the main information source. Until now technology allowed us to automatically analyze the responses of the VA questionnaire with the narrative captured by the interviewer excluded. Our work addresses this gap by developing a set of models for automatic Cause of Death (CoD) ascertainment in VAs with a focus on the textual information. Empirical results show that the open response conveys valuable information towards the ascertainment of the Cause of Death, and the combination of the closed-ended questions and the open response lead to the best results. Model interpretation capabilities position the Deep Learning models as the most encouraging choice.

This work focuses on the detection of adverse drug reactions (ADRs) in electronic health records (EHRs) written in Spanish. The World Health Organization underlines the importance of reporting ADRs for patients' safety. The fact is that ADRs tend to be under-reported in daily hospital praxis. In this context, automatic solutions based on text mining can help to alleviate the workload of experts. Nevertheless, these solutions pose two challenges: 1) EHRs show high lexical variability, the characterization of the events must be able to deal with unseen words or contexts and 2) ADRs are rare events, hence, the system should be robust against skewed class distribution. To tackle these challenges, deep neural networks seem appropriate because they allow a high-level representation. Specifically, we opted for a joint AB-LSTM network, a sub-class of the bidirectional long short-term memory network. Besides, in an attempt to reinforce lexical variability, we proposed the use of embeddings created using lemmas. We compared this approach with supervised event extraction approaches based on either symbolic or dense representations. Experimental results showed that the joint AB-LSTM approach outperformed previous approaches, achieving an f-measure of 73.3.

This work focuses on data mining applied to the clinical documentation domain. Diagnostic terms (DTs) are used as keywords to retrieve valuable information from electronic health records. Indeed, they are encoded manually by experts following the International Classification of Diseases (ICD). The goal of this work is to explore the aid of text mining on DT encoding. From the machine learning (ML) perspective, this is a high-dimensional classification task, as it comprises thousands of codes. This work delves into a robust representation of the instances to improve ML results. The proposed system is able to find the right ICD code among more than 1500 possible ICD codes with 92% precision for the main disease (primary class) and 88% for the main disease together with the nonessential modifiers (fully specified class). The methodology employed is simple and portable. According to the experts from public hospitals, the system is very useful in particular for documentation and pharmacosurveillance services. In fact, they reported an accuracy of 91.2% on a small randomly extracted test. Hence, together with this paper, we made the software publicly available in order to help the clinical and research community.

This work focuses on the extraction of allergic drug reactions in electronic health records. The goal is to annotate a sub-class of cause-effect events, those in which drugs are causing allergies. Little work has carried out in this field, seldom for Spanish clinical text mining, which is, indeed, the aim of this work. We present two approaches: a rule-based method and another one based on machine learning. Both approaches incorporate semantic knowledge derived from FreeLing-Med, a software explicitly developed to parse texts in the medical domain. Having recognised the medical entities for a given record, the challenge stands on triggering the underlying allergies. To this end, the knowledge is expressed as a set of semantic, syntactic and structural features. Our best system, based on machine learning, obtained a precision of 0.90 with a recall of 0.87, outperforming a rule-based approach.

This work presents an on-line system developed for medical information extraction. The goal is to provide a web-based service addressed to the medical community for the efficient processing of electronic health records in Spanish and support the clinical decision making process. This tool assists in the identification of medical entities as well as cause-effect reactions in order to help professionals both to prevent and to document adverse drug events. So far, the prototype is in its early stage of testing and validation by experts from the Galdakao-Usansolo and Basurto hospitals from the Basque Sanitary System (Osakidetza).

This paper presents an approach for Multilingual Document Clustering in comparable corpora. The algorithm is of heuristic nature and it uses as unique evidence for clustering the identification of cognate named entities between both sides of the comparable corpora. One of the main advantages of this approach is that it does not depend on bilingual or multilingual resources. However, it depends on the possibility of identifying cognate named entities between the languages used in the corpus. An additional advantage of the approach is that it does not need any information about the right number of clusters; the algorithm calculates it. We have tested this approach with a comparable corpus of news written in English and Spanish. In addition, we have compared the results with a system which translates selected document features. The obtained results are encouraging.

Extensively annotated bilingual parallel corpora can be exploited to feed editing tools that integrate the processes of document composition and translation. Here we discuss the architecture of an interactive editing tool that, on top of techniques common to most Translation Memory-based systems, applies the potential of SGML's DTDs to guide the process of bilingual document generation. Rather than employing just simple task-oriented mark-up, we selected a set of TEI's highly complex and versatile collection of tags to help disclose the underlying logical structure of documents in the test-corpus. DTDs were automatically induced and later integrated in the editing tool to provide the basic scheme for new documents.