Capturing the Patient's Perspective: a Review of Advances in Natural Language Processing of Health-Related Text.

Background: Natural Language Processing (NLP) methods are increasingly being utilized to mine knowledge from unstructured health-related texts. Recent advances in noisy text processing techniques are enabling researchers and medical domain experts to go beyond the information encapsulated in published texts (e.g., clinical trials and systematic reviews) and structured questionnaires, and obtain perspectives from other unstructured sources such as Electronic Health Records (EHRs) and social media posts. Objectives: To review the recently published literature discussing the application of NLP techniques for mining health-related information from EHRs and social media posts. Methods: Literature review included the research published over the last five years based on searches of PubMed, conference proceedings, and the ACM Digital Library, as well as on relevant publications referenced in papers. We particularly focused on the techniques employed on EHRs and social media data. Results: A set of 62 studies involving EHRs and 87 studies involving social media matched our criteria and were included in this paper. We present the purposes of these studies, outline the key NLP contributions, and discuss the general trends observed in the field, the current state of research, and important outstanding problems. Conclusions: Over the recent years, there has been a continuing transition from lexical and rule-based systems to learning-based approaches, because of the growth of annotated data sets and advances in data science. For EHRs, publicly available annotated data is still scarce and this acts as an obstacle to research progress. On the contrary, research on social media mining has seen a rapid growth, particularly because the large amount of unlabeled data available via this resource compensates for the uncertainty inherent to the data. Effective mechanisms to filter out noise and for mapping social media expressions to standard medical concepts are crucial and latent research problems. Shared tasks and other competitive challenges have been driving factors behind the implementation of open systems, and they are likely to play an imperative role in the development of future systems.

Formative evaluation of ontology learning methods for entity discovery by using existing ontologies as reference standards.

OBJECTIVE: Developing a two-step method for formative evaluation of statistical Ontology Learning (OL) algorithms that leverages existing biomedical ontologies as reference standards. METHODS: In the first step optimum parameters are established. A 'gap list' of entities is generated by finding the set of entities present in a later version of the ontology that are not present in an earlier version of the ontology. A named entity recognition system is used to identify entities in a corpus of biomedical documents that are present in the 'gap list', generating a reference standard. The output of the algorithm (new entity candidates), produced by statistical methods, is subsequently compared against this reference standard. An OL method that performs perfectly will be able to learn all of the terms in this reference standard. Using evaluation metrics and precision-recall curves for different thresholds and parameters, we compute the optimum parameters for each method. In the second step, human judges with expertise in ontology development evaluate each candidate suggested by the algorithm configured with the optimum parameters previously established. These judgments are used to compute two performance metrics developed from our previous work: Entity Suggestion Rate (ESR) and Entity Acceptance Rate (EAR). RESULTS: Using this method, we evaluated two statistical OL methods for OL in two medical domains. For the pathology domain, we obtained 49% ESR, 28% EAR with the Lin method and 52% ESR, 39% EAR with the Church method. For the radiology domain, we obtain 87% ESA, 9% EAR using Lin method and 96% ESR, 16% EAR using Church method. CONCLUSION: This method is sufficiently general and flexible enough to permit comparison of any OL method for a specific corpus and ontology of interest.

Psychiatric co-morbidity is associated with increased risk of surgery in Crohn's disease.

BACKGROUND: Psychiatric co-morbidity, in particular major depression and anxiety, is common in patients with Crohn's disease (CD) and ulcerative colitis (UC). Prior studies examining this may be confounded by the co-existence of functional bowel symptoms. Limited data exist examining an association between depression or anxiety and disease-specific endpoints such as bowel surgery. AIMS: To examine the frequency of depression and anxiety (prior to surgery or hospitalisation) in a large multi-institution electronic medical record (EMR)-based cohort of CD and UC patients; to define the independent effect of psychiatric co-morbidity on risk of subsequent surgery or hospitalisation in CD and UC, and to identify the effects of depression and anxiety on healthcare utilisation in our cohort. METHODS: Using a multi-institution cohort of patients with CD and UC, we identified those who also had co-existing psychiatric co-morbidity (major depressive disorder or generalised anxiety). After excluding those diagnosed with such co-morbidity for the first time following surgery, we used multivariate logistic regression to examine the independent effect of psychiatric co-morbidity on IBD-related surgery and hospitalisation. To account for confounding by disease severity, we adjusted for a propensity score estimating likelihood of psychiatric co-morbidity influenced by severity of disease in our models. RESULTS: A total of 5405 CD and 5429 UC patients were included in this study; one-fifth had either major depressive disorder or generalised anxiety. In multivariate analysis, adjusting for potential confounders and the propensity score, presence of mood or anxiety co-morbidity was associated with a 28% increase in risk of surgery in CD (OR: 1.28, 95% CI: 1.03-1.57), but not UC (OR: 1.01, 95% CI: 0.80-1.28). Psychiatric co-morbidity was associated with increased healthcare utilisation. CONCLUSIONS: Depressive disorder or generalised anxiety is associated with a modestly increased risk of surgery in patients with Crohn's disease. Interventions addressing this may improve patient outcomes.

The emerging role of electronic medical records in pharmacogenomics.

Health-care information technology and genotyping technology are both advancing rapidly, creating new opportunities for medical and scientific discovery. The convergence of these two technologies is now facilitating genetic association studies of unprecedented size within the context of routine clinical care. As a result, the medical community will soon be presented with a number of novel opportunities to bring functional genomics to the bedside in the area of pharmacotherapy. By linking biological material to comprehensive medical records, large multi-institutional biobanks are now poised to advance the field of pharmacogenomics through three distinct mechanisms: (i) retrospective assessment of previously known findings in a clinical practice-based setting, (ii) discovery of new associations in huge observational cohorts, and (iii) prospective application in a setting capable of providing real-time decision support. This review explores each of these translational mechanisms within a historical framework.

Effectiveness of lexico-syntactic pattern matching for ontology enrichment with clinical documents.

OBJECTIVE: To evaluate the effectiveness of a lexico-syntactic pattern (LSP) matching method for ontology enrichment using clinical documents. METHODS: Two domains were separately studied using the same methodology. We used radiology documents to enrich RadLex and pathology documents to enrich National Cancer Institute Thesaurus (NCIT). Several known LSPs were used for semantic knowledge extraction. We first retrieved all sentences that contained LSPs across two large clinical repositories, and examined the frequency of the LSPs. From this set, we randomly sampled LSP instances which were examined by human judges. We used a two-step method to determine the utility of these patterns for enrichment. In the first step, domain experts annotated medically meaningful terms (MMTs) from each sentence within the LSP. In the second step, RadLex and NCIT curators evaluated how many of these MMTs could be added to the resource. To quantify the utility of this LSP method, we defined two evaluation metrics: suggestion rate (SR) and acceptance rate (AR). We used these measures to estimate the yield of concepts and relationships, for each of the two domains. RESULTS: For NCIT, the concept SR was 24%, and the relationship SR was 65%. The concept AR was 21%, and the relationship AR was 14%. For RadLex, the concept SR was 37%, and the relationship SR was 55%. The concept AR was 11%, and the relationship AR was 44%. CONCLUSION: The LSP matching method is an effective method for concept and concept relationship discovery in biomedical domains.

Extracting information from textual documents in the electronic health record: a review of recent research.

OBJECTIVES: We examine recent published research on the extraction of information from textual documents in the Electronic Health Record (EHR). METHODS: Literature review of the research published after 1995, based on PubMed, conference proceedings, and the ACM Digital Library, as well as on relevant publications referenced in papers already included. RESULTS: 174 publications were selected and are discussed in this review in terms of methods used, pre-processing of textual documents, contextual features detection and analysis, extraction of information in general, extraction of codes and of information for decision-support and enrichment of the EHR, information extraction for surveillance, research, automated terminology management, and data mining, and de-identification of clinical text. CONCLUSIONS: Performance of information extraction systems with clinical text has improved since the last systematic review in 1995, but they are still rarely applied outside of the laboratory they have been developed in. Competitive challenges for information extraction from clinical text, along with the availability of annotated clinical text corpora, and further improvements in system performance are important factors to stimulate advances in this field and to increase the acceptance and usage of these systems in concrete clinical and biomedical research contexts.

Frame semantics and the domain of functioning, disability and health.

We explore the use of FrameNet (FN) frames and their frame elements to represent content from the International Classification of Functioning, Disability and Health (ICF) Self-Care chapter. Terms were extracted from the ICF and mapped to FN frames. The mappings were validated by an expert. FN provided relevant and nearly complete coverage of ICF terms, suggesting FN may be an important resource to leverage for semantic language processing and knowledge representation in this domain.