Automated processing of electronic medical records is a reliable method of determining aspirin use in populations at risk for cardiovascular events

Background Low-dose aspirin reduces cardiovascular risk; however, monitoring over-the-counter medication use relies on the time-consuming and costly manual review of medical records. Our objective is to validate natural language processing (NLP) of the electronic medical record (EMR) for extracting medication exposure and contraindication information. Methods The text of EMRs for 499 patients with type 2 diabetes was searched using NLP for evidence of aspirin use and its contraindications. The results were compared to a standardised manual records review. Results Of the 499 patients, 351 (70%) were using aspirin and 148 (30%) were not, according to manual review. NLP correctly identified 346 of the 351 aspirin-positive and 134 of the 148 aspirin-negative patients, indicating a sensitivity of 99% (95% CI 97_100) and specificity of 91% (95% CI 88_97). Of the 148 aspirin-negative patients, 66 (45%) had contraindications and 82 (55%) did not, according to manual review.NLP search for contraindications correctly identified 61 of the 66 patients with contraindications and 58 of the 82 patients without, yielding a sensitivity of 92% (95% CI 84_97) and a specificity of 71% (95% CI 60_80). Conclusions NLP of the EMR is accurate in ascertaining documented aspirin use and could potentially be used for epidemiological research as a source of cardiovascular risk factor information

The DDI corpus: An annotated corpus with pharmacological substances and drug-drug interactions

The management of drug-drug interactions (DDIs) is a critical issue resulting from the overwhelming amount of information available on them. Natural Language Processing (NLP) techniques can provide an interesting way to reduce the time spent by healthcare professionals on reviewing biomedical literature. However, NLP techniques rely mostly on the availability of the annotated corpora. While there are several annotated corpora with biological entities and their relationships, there is a lack of corpora annotated with pharmacological substances and DDIs. Moreover, other works in this field have focused in pharmacokinetic (PK) DDIs only, but not in pharmacodynamic (PD) DDIs. To address this problem, we have created a manually annotated corpus consisting of 792 texts selected from the DrugBank database and other 233 Medline abstracts. This fined-grained corpus has been annotated with a total of 18,502 pharmacological substances and 5028 DDIs, including both PK as well as PD interactions. The quality and consistency of the annotation process has been ensured through the creation of annotation guidelines and has been evaluated by the measurement of the inter-annotator agreement between two annotators. The agreement was almost perfect (Kappa up to 0.96 and generally over 0.80), except for the DDIs in the MedLine database (0.55-0.72). The DDI corpus has been used in the SemEvaI 2013 DDIExtraction challenge as a gold standard for the evaluation of information extraction techniques applied to the recognition of pharmacological substances and the detection of DDIs from biomedical texts. DDIExtraction 2013 has attracted wide attention with a total of 14 teams from 7 different countries. For the task of recognition and classification of pharmacological names, the best system achieved an F1 of 71.5%, while, for the detection and classification of DDIs, the best result was F1 of 65.1%.Funding: This work was supported by the EU project TrendMiner [FP7-ICT287863], by the project MULTIMEDICA [TIN2010- 20644-C03-01], and by the Research Network MA2VICMR [S2009/TIC-1542].Publicad

A medication extraction framework for electronic health records

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2012.Cataloged from PDF version of thesis.Includes bibliographical references (p. 71-76).This thesis addresses the problem of concept and relation extraction in medical documents. We present a medical concept and relation extraction system (medNERR) that incorporates hand-built rules and constrained conditional models. We focus on two concept types (i.e., medications and medical conditions) and the pairwise administered-for relation between these two concepts. For medication extraction, we design a rule-based baseline medNERRgreedy med that identifies medications using the UMLS dictionary. We enhance medNERRgreedy med with information from topic models and additional corpus-derived heuristics, and show that the final medication extraction system outperforms the baseline and improves on state-of-the-art systems. For medical conditions extraction we design a Hidden Markov Model with conditional constraints. The conditional constraints frame world knowledge into a probabilistic model and help support model decisions. We approach relation extraction as a sequence labeling task, where we label the context between the medications and the medical concepts that are involved in an administered-for relation. We use a Hidden Markov Model with conditional constraints for labeling the relation context. We show that the relation extraction system outperforms current state of the art systems and that its main advantage comes from the incorporation of domain knowledge through conditional constraints. We compare our sequence labeling approach for relation extraction to a classification approach and show that our approach improves final system performance.by Andreea Bodnari.S.M

Annotation Issues in Pharmacological Texts

This works is at: 5th International Conference on Corpus Linguistics (CILC2013), took place 2013, March 14-16, in Alicante (Spain). Conference web site: http://web.ua.es/en/cilc2013/Natural language processing of pharmacological texts includes recognition of drug names and extraction of relationships between them. To this purpose, pharmacological annotated corpora are required. These corpora are usually semantically annotated by domain experts. However, other linguistic aspects should be considered to ensure the quality and consistency of the annotation. This paper introduces several linguistic phenomena affecting the annotation of both drug named entities and drug-drug interaction relationships that arose during the annotation process of the DDI corpus. The detailed documentation of these issues and the decisions on them will improve the quality of the annotated corpus and its usefulness for other researchers or usersThis work was supported by the Regional Government of Madrid under the Research Network MA2VICMR [S2009/TIC-1542] and by the Spanish Ministry of Economy under the project MULTIMEDICA [TIN2010-20644- C03-01].Publicad

Mining the Medical and Patent Literature to Support Healthcare and Pharmacovigilance

Recent advancements in healthcare practices and the increasing use of information technology in the medical domain has lead to the rapid generation of free-text data in forms of scientific articles, e-health records, patents, and document inventories. This has urged the development of sophisticated information retrieval and information extraction technologies. A fundamental requirement for the automatic processing of biomedical text is the identification of information carrying units such as the concepts or named entities. In this context, this work focuses on the identification of medical disorders (such as diseases and adverse effects) which denote an important category of concepts in the medical text. Two methodologies were investigated in this regard and they are dictionary-based and machine learning-based approaches. Futhermore, the capabilities of the concept recognition techniques were systematically exploited to build a semantic search platform for the retrieval of e-health records and patents. The system facilitates conventional text search as well as semantic and ontological searches. Performance of the adapted retrieval platform for e-health records and patents was evaluated within open assessment challenges (i.e. TRECMED and TRECCHEM respectively) wherein the system was best rated in comparison to several other competing information retrieval platforms. Finally, from the medico-pharma perspective, a strategy for the identification of adverse drug events from medical case reports was developed. Qualitative evaluation as well as an expert validation of the developed system's performance showed robust results. In conclusion, this thesis presents approaches for efficient information retrieval and information extraction from various biomedical literature sources in the support of healthcare and pharmacovigilance. The applied strategies have potential to enhance the literature-searches performed by biomedical, healthcare, and patent professionals. The applied strategies have potential to enhance the literature-searches performed by biomedical, healthcare, and patent professionals. This can promote the literature-based knowledge discovery, improve the safety and effectiveness of medical practices, and drive the research and development in medical and healthcare arena

Overview of Noninterpretive Artificial Intelligence Models for Safety, Quality, Workflow, and Education Applications in Radiology Practice

Artificial intelligence has become a ubiquitous term in radiology over the past several years, and much attention has been given to applications that aid radiologists in the detection of abnormalities and diagnosis of diseases. However, there are many potential applications related to radiologic image quality, safety, and workflow improvements that present equal, if not greater, value propositions to radiology practices, insurance companies, and hospital systems. This review focuses on six major categories for artificial intelligence applications: study selection and protocoling, image acquisition, worklist prioritization, study reporting, business applications, and resident education. All of these categories can substantially affect different aspects of radiology practices and workflows. Each of these categories has different value propositions in terms of whether they could be used to increase efficiency, improve patient safety, increase revenue, or save costs. Each application is covered in depth in the context of both current and future areas of work