Improving Statistical Machine Translation in the Medical Domain using the Unified Medical Language system

Texts from the medical domain are an important task for natural language processing. This paper investigates the usefulness of a large medical database (the Unified Medical Language System) for the translation of dialogues between doctors and patients using a statistical machine translation system. We are able to show that the extraction of a large dictionary and the usage of semantic type information to generalize the training data significantly improves the translation performance

Clinical narrative analytics challenges

Precision medicine or evidence based medicine is based on the extraction of knowledge from medical records to provide individuals with the appropriate treatment in the appropriate moment according to the patient features. Despite the efforts of using clinical narratives for clinical decision support, many challenges have to be faced still today such as multilinguarity, diversity of terms and formats in different services, acronyms, negation, to name but a few. The same problems exist when one wants to analyze narratives in literature whose analysis would provide physicians and researchers with highlights. In this talk we will analyze challenges, solutions and open problems and will analyze several frameworks and tools that are able to perform NLP over free text to extract medical entities by means of Named Entity Recognition process. We will also analyze a framework we have developed to extract and validate medical terms. In particular we present two uses cases: (i) medical entities extraction of a set of infectious diseases description texts provided by MedlinePlus and (ii) scales of stroke identification in clinical narratives written in Spanish

A new clustering method for detecting rare senses of abbreviations in clinical notes

AbstractAbbreviations are widely used in clinical documents and they are often ambiguous. Building a list of possible senses (also called sense inventory) for each ambiguous abbreviation is the first step to automatically identify correct meanings of abbreviations in given contexts. Clustering based methods have been used to detect senses of abbreviations from a clinical corpus [1]. However, rare senses remain challenging and existing algorithms are not good enough to detect them. In this study, we developed a new two-phase clustering algorithm called Tight Clustering for Rare Senses (TCRS) and applied it to sense generation of abbreviations in clinical text. Using manually annotated sense inventories from a set of 13 ambiguous clinical abbreviations, we evaluated and compared TCRS with the existing Expectation Maximization (EM) clustering algorithm for sense generation, at two different levels of annotation cost (10 vs. 20 instances for each abbreviation). Our results showed that the TCRS-based method could detect 85% senses on average; while the EM-based method found only 75% senses, when similar annotation effort (about 20 instances) was used. Further analysis demonstrated that the improvement by the TCRS method was mainly from additionally detected rare senses, thus indicating its usefulness for building more complete sense inventories of clinical abbreviations

Combining Free Text and Structured Electronic Medical Record Entries to Detect Acute Respiratory Infections

The electronic medical record (EMR) contains a rich source of information that could be harnessed for epidemic surveillance. We asked if structured EMR data could be coupled with computerized processing of free-text clinical entries to enhance detection of acute respiratory infections (ARI).A manual review of EMR records related to 15,377 outpatient visits uncovered 280 reference cases of ARI. We used logistic regression with backward elimination to determine which among candidate structured EMR parameters (diagnostic codes, vital signs and orders for tests, imaging and medications) contributed to the detection of those reference cases. We also developed a computerized free-text search to identify clinical notes documenting at least two non-negated ARI symptoms. We then used heuristics to build case-detection algorithms that best combined the retained structured EMR parameters with the results of the text analysis.An adjusted grouping of diagnostic codes identified reference ARI patients with a sensitivity of 79%, a specificity of 96% and a positive predictive value (PPV) of 32%. Of the 21 additional structured clinical parameters considered, two contributed significantly to ARI detection: new prescriptions for cough remedies and elevations in body temperature to at least 38°C. Together with the diagnostic codes, these parameters increased detection sensitivity to 87%, but specificity and PPV declined to 95% and 25%, respectively. Adding text analysis increased sensitivity to 99%, but PPV dropped further to 14%. Algorithms that required satisfying both a query of structured EMR parameters as well as text analysis disclosed PPVs of 52-68% and retained sensitivities of 69-73%.Structured EMR parameters and free-text analyses can be combined into algorithms that can detect ARI cases with new levels of sensitivity or precision. These results highlight potential paths by which repurposed EMR information could facilitate the discovery of epidemics before they cause mass casualties

Human organ re-representation using UML and CMAUT

Clinical data was captured and stored data using natural language (NL) in order to describe the human organs, their attributes and behaviour (Olsen et, 1998). Although this was an accurate form of data representation it created information overload, space complexity, inconsistency and erroneous data. To address the issue of data inconsistency and standardisation, clinical coding such as UMLS was used while for clinical interoperability and data exchange between users, NL7 was introduced. A survey conducted by (de Keizer et, 2000a) revealed that these methods are inadequate for clinical data representation hence the data rerepresentation technique (Haimowitz et, 1988) was introduced and used for modelling CIS with Entity Relationship Diagram (ERD) and (FOL)(de Keizer et, 2000b). However this model does not address the issue of information overload and space complexity. Hence, this paper presents an alternative approach where UML is used to capture human organs, their attributes and relationships. A new framework with built in algorithm converts the multiple attributes modelled in the class diagram into mathematical formalisation using the CMAUT. The logical expression serves as input to the optimisation algorithm to determine the optimal amount of data that must be retrieved for primary healthcare investigation. To evaluate the framework, mathematical operations were performed which revealed that the space complexity when using the CMA rerepresentation technique is θ ( n + 1) compared to θ (2n) for nonCMA. This means less space is needed when the CMA with AND connector is used but for substitutable organs with OR connector the space complexity for both CMA and nonCMA representations have the same exponential expansion of θ (2 n ). A ttest conducted on the amount of data required for investigation before and after optimisation gave a pvalue of 0.000 which means there is a significant different between the two data sets. For epidemiological analysis the output of the framework was benchmarked against the output of a web based heart risk calculator and the single sample ttest conducted gave a pvalue of 0.686 meaning there is no difference between two outputs. Thus this framework with data rerepresentation occupies less space as compared to others and can be used to calculate the risk factor of a heart patient

Structured and unstructured data integration with electronic medical records

In recent years there has been a great population and technological evolution all over the world. At the same time, more areas beyond technology and information technology have also developed, namely medicine, which has led to an increase in average life expectancy which in turn, leads to a greater need for healthcare. In order to provide the best possible treatments and healthcare services, nowadays the hospitals store large amounts of data regarding patients and diseases (in the form of electronic medical records) or the logistics of some departments in their storage systems. Therefore, computer science techniques such as data mining and natural language processing have been used to extract knowledge and value from these information-rich sources in order not only to develop, for example, new models for disease prediction, as well as improving existing processes in healthcare centres and hospitals. This data storage can be done in one of three ways: structured, unstructured or semi-structured. In this paper, the author tested the integration of structured and unstructured data from two different departments of the same Portuguese hospital, in order to extract knowledge and improve hospital processes. Aiming to reduce the value loss of loading data that is not used in the healthcare providers systems.Nos últimos anos tem-se assistido a uma grande evolução populacional e tecnológica por todo o mundo. Paralelamente, mais áreas para além da tecnologia e informática têm-se também desenvolvido, nomeadamente a área da medicina, o que tem permitido um aumento na esperança média de vida que por sua vez leva a uma maior necessidade de cuidados de saúde. Com o intuito de fornecer os melhores serviços de saúde possíveis, nos dias que hoje os hospitais guardam nos seus sistemas informáticos grandes quantidades de dados relativamente aos pacientes e doenças (sobre a forma de registos médicos eletrónicos) ou relativos à logística de alguns departamentos dos hospitais, etc. Por conseguinte, a estes dados têm vindo a ser utilizadas técnicas da área das ciências da computação como o data mining e o processamento da língua natural para extrair conhecimento e valor dessas fontes ricas em informação com o intuito não só de desenvolver, por exemplo, novos modelos de predição de doenças, como também de melhorar processos já existentes em centros de saúde e hospitais. Este armazenamento de dados pode ser feito em uma de três formas: de forma estruturada, não estruturada ou semi-estruturada. Neste trabalho o autor testou a integração de dados estruturados e não estruturados de dois departamentos diferentes do mesmo hospital português, com o intuito de extrair conhecimento e melhorar os processos do hospital. Com o intuito de reduzir a perda do armazenamento de dados que não são utilizados

Doctor of Philosophy

dissertationManual annotation of clinical texts is often used as a method of generating reference standards that provide data for training and evaluation of Natural Language Processing (NLP) systems. Manually annotating clinical texts is time consuming, expensive, and requires considerable cognitive effort on the part of human reviewers. Furthermore, reference standards must be generated in ways that produce consistent and reliable data but must also be valid in order to adequately evaluate the performance of those systems. The amount of labeled data necessary varies depending on the level of analysis, the complexity of the clinical use case, and the methods that will be used to develop automated machine systems for information extraction and classification. Evaluating methods that potentially reduce cost, manual human workload, introduce task efficiencies, and reduce the amount of labeled data necessary to train NLP tools for specific clinical use cases are active areas of research inquiry in the clinical NLP domain. This dissertation integrates a mixed methods approach using methodologies from cognitive science and artificial intelligence with manual annotation of clinical texts. Aim 1 of this dissertation identifies factors that affect manual annotation of clinical texts. These factors are further explored by evaluating approaches that may introduce efficiencies into manual review tasks applied to two different NLP development areas - semantic annotation of clinical concepts and identification of information representing Protected Health Information (PHI) as defined by HIPAA. Both experiments integrate iv different priming mechanisms using noninteractive and machine-assisted methods. The main hypothesis for this research is that integrating pre-annotation or other machineassisted methods within manual annotation workflows will improve efficiency of manual annotation tasks without diminishing the quality of generated reference standards