Ontology-Based Clinical Information Extraction Using SNOMED CT

Extracting and encoding clinical information captured in unstructured clinical documents with standard medical terminologies is vital to enable secondary use of clinical data from practice. SNOMED CT is the most comprehensive medical ontology with broad types of concepts and detailed relationships and it has been widely used for many clinical applications. However, few studies have investigated the use of SNOMED CT in clinical information extraction. In this dissertation research, we developed a fine-grained information model based on the SNOMED CT and built novel information extraction systems to recognize clinical entities and identify their relations, as well as to encode them to SNOMED CT concepts. Our evaluation shows that such ontology-based information extraction systems using SNOMED CT could achieve state-of-the-art performance, indicating its potential in clinical natural language processing

GNTeam at 2018 n2c2:Feature-augmented BiLSTM-CRF for drug-related entity recognition in hospital discharge summaries

Monitoring the administration of drugs and adverse drug reactions are key parts of pharmacovigilance. In this paper, we explore the extraction of drug mentions and drug-related information (reason for taking a drug, route, frequency, dosage, strength, form, duration, and adverse events) from hospital discharge summaries through deep learning that relies on various representations for clinical named entity recognition. This work was officially part of the 2018 n2c2 shared task, and we use the data supplied as part of the task. We developed two deep learning architecture based on recurrent neural networks and pre-trained language models. We also explore the effect of augmenting word representations with semantic features for clinical named entity recognition. Our feature-augmented BiLSTM-CRF model performed with F1-score of 92.67% and ranked 4th for entity extraction sub-task among submitted systems to n2c2 challenge. The recurrent neural networks that use the pre-trained domain-specific word embeddings and a CRF layer for label optimization perform drug, adverse event and related entities extraction with micro-averaged F1-score of over 91%. The augmentation of word vectors with semantic features extracted using available clinical NLP toolkits can further improve the performance. Word embeddings that are pre-trained on a large unannotated corpus of relevant documents and further fine-tuned to the task perform rather well. However, the augmentation of word embeddings with semantic features can help improve the performance (primarily by boosting precision) of drug-related named entity recognition from electronic health records

Mining social media data for biomedical signals and health-related behavior

Social media data has been increasingly used to study biomedical and health-related phenomena. From cohort level discussions of a condition to planetary level analyses of sentiment, social media has provided scientists with unprecedented amounts of data to study human behavior and response associated with a variety of health conditions and medical treatments. Here we review recent work in mining social media for biomedical, epidemiological, and social phenomena information relevant to the multilevel complexity of human health. We pay particular attention to topics where social media data analysis has shown the most progress, including pharmacovigilance, sentiment analysis especially for mental health, and other areas. We also discuss a variety of innovative uses of social media data for health-related applications and important limitations in social media data access and use.Comment: To appear in the Annual Review of Biomedical Data Scienc

Extracting clinical knowledge from electronic medical records

As the adoption of Electronic Medical Records (EMRs) rises in the healthcare institutions, these resources’ importance increases due to all clinical information they contain about patients. However, the unstructured information in the form of clinical narratives present in these records makes it hard to extract and structure useful clinical knowledge. This unstructured information limits the potential of the EMRs because the clinical information these records contain can be used to perform essential tasks inside healthcare institutions such as searching, summarization, decision support and statistical analysis, as well as be used to support management decisions or serve for research. These tasks can only be done if the unstructured clinical information from the narratives is appropriately extracted, structured and processed in clinical knowledge. Usually, this information extraction and structuration in clinical knowledge is performed manually by healthcare practitioners, which is not efficient and is error-prone. This research aims to propose a solution to this problem, by using Machine Translation (MT) from the Portuguese language to the English language, Natural Language Processing (NLP) and Information Extraction (IE) techniques. With the help of these techniques, the goal is to develop a prototype pipeline modular system that can extract clinical knowledge from unstructured clinical information contained in Portuguese EMRs, in an automated way, in order to help EMRs to fulfil their potential and consequently help the Portuguese hospital involved in this research. This research also intends to show that this generic prototype system and approach can potentially be applied to other hospitals, even if they don’t use the Portuguese language.Com a adopção cada vez maior das instituições de saúde face aos Processos Clínicos Electrónicos (PCE), estes documentos ganham cada vez mais importância em contexto clínico, devido a toda a informação clínica que contêm relativamente aos pacientes. No entanto, a informação não estruturada na forma de narrativas clínicas presente nestes documentos electrónicos, faz com que seja difícil extrair e estruturar deles conhecimento clínico. Esta informação não estruturada limita o potencial dos PCE, uma vez que essa mesma informação, caso seja extraída e estruturada devidamente, pode servir para que as instituições de saúde possam efectuar actividades importantes com maior eficiência e sucesso, como por exemplo actividades de pesquisa, sumarização, apoio à decisão, análises estatísticas, suporte a decisões de gestão e de investigação. Este tipo de actividades apenas podem ser feitas com sucesso caso a informação clínica não estruturada presente nos PCE seja devidamente extraída, estruturada e processada em conhecimento clínico. Habitualmente, esta extração é realizada manualmente pelos profissionais médicos, o que não é eficiente e é susceptível a erros. Esta dissertação pretende então propôr uma solução para este problema, ao utilizar técnicas de Tradução Automática (TA) da língua portuguesa para a língua inglesa, Processamento de Linguagem Natural (PLN) e Extração de Informação (EI). O objectivo é desenvolver um sistema protótipo de módulos em série que utilize estas técnicas, possibilitando a extração de conhecimento clínico, de uma forma automática, de informação clínica não estruturada presente nos PCE de um hospital português. O principal objetivo é ajudar os PCE a atingirem todo o seu potencial em termos de conhecimento clínico que contêm e consequentemente ajudar o hospital português em questão envolvido nesta dissertação, demonstrando também que este sistema protótipo e esta abordagem podem potencialmente ser aplicados a outros hospitais, mesmo que não sejam de língua portuguesa

Performance and error analysis of three part of speech taggers on health texts

Increasingly, natural language processing (NLP) techniques are being developed and utilized in a variety of biomedical domains. Part of speech tagging is a critical step in many NLP applications. Currently, we are developing a NLP tool for text simplification. As part of this effort, we set off to evaluate several part of speech (POS) taggers. We selected 120 sentences (2375 tokens) from a corpus of six types of diabetes-related health texts and asked human reviewers to tag each word in these sentences to create a "Gold Standard." We then tested each of the three POS taggers against the "Gold Standard." One tagger (dTagger) had been trained on health texts and the other two (MaxEnt and Curran & Clark) were trained on general news articles. We analyzed the errors and placed them into five categories: systematic, close, subtle, difficult source, and other. The three taggers have relatively similar rates of success: dTagger, MaxEnt, and Curran & Clark had 87%, 89% and 90% agreement with the gold standard, respectively. These rates of success are lower than published rates for these taggers. This is probably due to our testing them on a corpus that differs significantly from their training corpora. The taggers made different errors: the dTagger, which had been trained on a set of medical texts (MedPost), made fewer errors on medical terms than MaxEnt and Curran & Clark. The latter two taggers performed better on non-medical terms and we found the difference between their performance and that of dTagger was statistically significant. Our findings suggest that the three POS taggers have similar correct tagging rates, though they differ in the types of errors they make. For the task of text simplification, we are inclined to perform additional training of the Curran & Clark tagger with the Medpost corpus because both the fine grained tagging provided by this tool and the correct recognition of medical terms are equally important

A Review on Adverse Drug Reaction Detection Techniques

The detection of adverse drug reactions (ADRs) is an important piece of information for determining a patient’s view of a single drug. This study attempts to consider and discuss this feature of drug reviews in medical opinion-mining systems. This paper discusses the literature that summarizes the background of this work. To achieve this aim, the first discusses a survey on detecting ADRs and side effects, followed by an examination of biomedical text mining that focuses on identifying the specific relationships involving ADRs. Finally, we will provide a general overview of sentiment analysis, particularly from a medical perspective. This study presents a survey on ADRs extracted from drug review sentences on social media, utilizing and comparing different techniques

Utilizing Consumer Health Posts for Pharmacovigilance: Identifying Underlying Factors Associated with Patients’ Attitudes Towards Antidepressants

Non-adherence to antidepressants is a major obstacle to antidepressants therapeutic benefits, resulting in increased risk of relapse, emergency visits, and significant burden on individuals and the healthcare system. Several studies showed that non-adherence is weakly associated with personal and clinical variables, but strongly associated with patients’ beliefs and attitudes towards medications. The traditional methods for identifying the key dimensions of patients’ attitudes towards antidepressants are associated with some methodological limitations, such as concern about confidentiality of personal information. In this study, attempts have been made to address the limitations by utilizing patients’ self report experiences in online healthcare forums to identify underlying factors affecting patients attitudes towards antidepressants. The data source of the study was a healthcare forum called “askapatients.com”. 892 patients’ reviews were randomly collected from the forum for the four most commonly prescribed antidepressants including Sertraline (Zoloft) and Escitalopram (Lexapro) from SSRI class, and Venlafaxine (Effexor) and duloxetine (Cymbalta) from SNRI class. Methodology of this study is composed of two main phases: I) generating structured data from unstructured patients’ drug reviews and testing hypotheses concerning attitude, II) identification and normalization of Adverse Drug Reactions (ADRs), Withdrawal Symptoms (WDs) and Drug Indications (DIs) from the posts, and mapping them to both The UMLS and SNOMED CT concepts. Phase II also includes testing the association between ADRs and attitude. The result of the first phase of this study showed that “experience of adverse drug reactions”, “perceived distress received from ADRs”, “lack of knowledge about medication’s mechanism”, “withdrawal experience”, “duration of usage”, and “drug effectiveness” are strongly associated with patients attitudes. However, demographic variables including “age” and “gender” are not associated with attitude. Analysis of the data in second phase of the study showed that from 6,534 identified entities, 73% are ADRs, 12% are WDs, and 15 % are drug indications. In addition, psychological and cognitive expressions have higher variability than physiological expressions. All three types of entities were mapped to 811 UMLS and SNOMED CT concepts. Testing the association between ADRs and attitude showed that from twenty-one physiological ADRs specified in the ASEC questionnaire, “dry mouth”, “increased appetite”, “disorientation”, “yawning”, “weight gain”, and “problem with sexual dysfunction” are associated with attitude. A set of psychological and cognitive ADRs, such as “emotional indifference” and “memory problem were also tested that showed significance association between these types of ADRs and attitude. The findings of this study have important implications for designing clinical interventions aiming to improve patients\u27 adherence towards antidepressants. In addition, the dataset generated in this study has significant implications for improving performance of text-mining algorithms aiming to identify health related information from consumer health posts. Moreover, the dataset can be used for generating and testing hypotheses related to ADRs associated with psychiatric mediations, and identifying factors associated with discontinuation of antidepressants. The dataset and guidelines of this study are available at https://sites.google.com/view/pharmacovigilanceinpsychiatry/hom