Human and automated coding of rehabilitation discharge summaries according to the International Classification of Functioning, Disability, and Health

Natural Language Processing to Classify Caregiver Strategies Supporting Participation Among Children and Youth with Craniofacial Microsomia and Other Childhood-Onset Disabilities

Customizing participation-focused pediatric rehabilitation interventions is an important but also complex and potentially resource intensive process, which may benefit from automated and simplified steps. This research aimed at applying natural language processing to develop and identify a best performing predictive model that classifies caregiver strategies into participation-related constructs, while filtering out non-strategies. We created a dataset including 1,576 caregiver strategies obtained from 236 families of children and youth (11-17 years) with craniofacial microsomia or other childhood-onset disabilities. These strategies were annotated to four participation-related constructs and a non-strategy class. We experimented with manually created features (i.e., speech and dependency tags, predefined likely sets of words, dense lexicon features (i.e., Unified Medical Language System (UMLS) concepts)) and three classical methods (i.e., logistic regression, naïve Bayes, support vector machines (SVM)). We tested a series of binary and multinomial classification tasks applying 10-fold cross-validation on the training set (80%) to test the best performing model on the held-out test set (20%). SVM using term frequency-inverse document frequency (TF-IDF) was the best performing model for all four classification tasks, with accuracy ranging from 78.10 to 94.92% and a macro-averaged F1-score ranging from 0.58 to 0.83. Manually created features only increased model performance when filtering out non-strategies. Results suggest pipelined classification tasks (i.e., filtering out non-strategies; classification into intrinsic and extrinsic strategies; classification into participation-related constructs) for implementation into participation-focused pediatric rehabilitation interventions like Participation and Environment Measure Plus (PEM+) among caregivers who complete the Participation and Environment Measure for Children and Youth (PEM-CY).

Supplementary Information

The online version contains supplementary material available at 10.1007/s41666-023-00149-y.

Automated recognition of functioning, activity and participation in COVID-19 from electronic patient records by natural language processing: a proof- of- concept

PURPOSE

To address the feasibility, reliability and internal validity of natural language processing (NLP) for automated functional assessment of hospitalised COVID-19 patients in key International Classification of Functioning, Disability and Health (ICF) categories and levels from unstructured text in electronic health records (EHR) from a large teaching hospital.

MATERIALS AND METHODS

Eight human annotators assigned four ICF categories to relevant sentences: Emotional functions, Exercise tolerance, Walking and Moving, Work and Employment and their ICF levels (Functional Ambulation Categories for Walking and Moving, metabolic equivalents for Exercise tolerance). A linguistic neural network-based model was trained on 80% of the annotated sentences; inter-annotator agreement (IAA, Cohen's kappa), a weighted score of precision and recall (F1) and RMSE for level detection were assessed for the remaining 20%.

RESULTS

In total 4112 sentences of non-COVID-19 and 1061 of COVID-19 patients were annotated. Average IAA was 0.81; F1 scores were 0.7 for Walking and Moving and Emotional functions; RMSE for Walking and Moving (5- level scale) was 1.17 for COVID-19 patients.

CONCLUSION

Using a limited amount of annotated EHR sentences, a proof-of-concept was obtained for automated functional assessment of COVID-19 patients in ICF categories and levels. This allows for instantaneous assessment of the functional consequences of new diseases like COVID-19 for large numbers of patients.Key messagesHospitalised Covid-19 survivors may persistently suffer from low physical and mental functioning and a reduction in overall quality of life requiring appropriate and personalised rehabilitation strategies.For this, assessment of functioning within multiple domains and categories of the International Classification of Function is required, which is cumbersome using structured data.We show a proof-of-concept using Natural Language Processing techniques to automatically derive the aforementioned information from free-text notes within the Electronic Health Record of a large academic teaching hospital.

A roadmap to reduce information inequities in disability with digital health and natural language processing

People with disabilities disproportionately experience negative health outcomes. Purposeful analysis of information on all aspects of the experience of disability across individuals and populations can guide interventions to reduce health inequities in care and outcomes. Such an analysis requires more holistic information on individual function, precursors and predictors, and environmental and personal factors than is systematically collected in current practice. We identify 3 key information barriers to more equitable information: (1) a lack of information on contextual factors that affect a person's experience of function; (2) underemphasis of the patient's voice, perspective, and goals in the electronic health record; and (3) a lack of standardized locations in the electronic health record to record observations of function and context. Through analysis of rehabilitation data, we have identified ways to mitigate these barriers through the development of digital health technologies to better capture and analyze information about the experience of function. We propose 3 directions for future research on using digital health technologies, particularly natural language processing (NLP), to facilitate capturing a more holistic picture of a patient's unique experience: (1) analyzing existing information on function in free text documentation; (2) developing new NLP-driven methods to collect information on contextual factors; and (3) collecting and analyzing patient-reported descriptions of personal perceptions and goals. Multidisciplinary collaboration between rehabilitation experts and data scientists to advance these research directions will yield practical technologies to help reduce inequities and improve care for all populations.

Extracting body function information using rule-based methods: Highlighting structure and formatting challenges in clinical text

This paper describes the identification of body function (BF) mentions within the clinical text within a large, national, heterogeneous corpus to highlight structural challenges presented by the clinical text. BF in clinical documents provides information on dysfunction or impairments in the function or structure of organ systems or organs. BF mentions are embedded in highly formatted structures where the formats include implied scoping boundaries that confound existing natural language processing segmentation and document decomposition techniques. This paper describes follow-up work to adapt a rule-based system created using National Institutes of Health records to a larger, more challenging corpus of Social Security Administration data. Results of these systems provide a baseline for future work to improve document decomposition techniques.

Linking Free Text Documentation of Functioning and Disability to the ICF With Natural Language Processing

Background: Invaluable information on patient functioning and the complex interactions that define it is recorded in free text portions of the Electronic Health Record (EHR). Leveraging this information to improve clinical decision-making and conduct research requires natural language processing (NLP) technologies to identify and organize the information recorded in clinical documentation.

Methods: We used natural language processing methods to analyze information about patient functioning recorded in two collections of clinical documents pertaining to claims for federal disability benefits from the U.S. Social Security Administration (SSA). We grounded our analysis in the International Classification of Functioning, Disability, and Health (ICF), and used the Activities and Participation domain of the ICF to classify information about functioning in three key areas: mobility, self-care, and domestic life. After annotating functional status information in our datasets through expert clinical review, we trained machine learning-based NLP models to automatically assign ICF categories to mentions of functional activity.

Results: We found that rich and diverse information on patient functioning was documented in the free text records. Annotation of 289 documents for Mobility information yielded 2,455 mentions of Mobility activities and 3,176 specific actions corresponding to 13 ICF-based categories. Annotation of 329 documents for Self-Care and Domestic Life information yielded 3,990 activity mentions and 4,665 specific actions corresponding to 16 ICF-based categories. NLP systems for automated ICF coding achieved over 80% macro-averaged F-measure on both datasets, indicating strong performance across all ICF categories used.

Conclusions: Natural language processing can help to navigate the tradeoff between flexible and expressive clinical documentation of functioning and standardizable data for comparability and learning. The ICF has practical limitations for classifying functional status information in clinical documentation but presents a valuable framework for organizing the information recorded in health records about patient functioning. This study advances the development of robust, ICF-based NLP technologies to analyze information on patient functioning and has significant implications for NLP-powered analysis of functional status information in disability benefits management, clinical care, and research.

Automated Coding of Under-Studied Medical Concept Domains: Linking Physical Activity Reports to the International Classification of Functioning, Disability, and Health

Linking clinical narratives to standardized vocabularies and coding systems is a key component of unlocking the information in medical text for analysis. However, many domains of medical concepts, such as functional outcomes and social determinants of health, lack well-developed terminologies that can support effective coding of medical text. We present a framework for developing natural language processing (NLP) technologies for automated coding of medical information in under-studied domains, and demonstrate its applicability through a case study on physical mobility function. Mobility function is a component of many health measures, from post-acute care and surgical outcomes to chronic frailty and disability, and is represented as one domain of human activity in the International Classification of Functioning, Disability, and Health (ICF). However, mobility and other types of functional activity remain under-studied in the medical informatics literature, and neither the ICF nor commonly-used medical terminologies capture functional status terminology in practice. We investigated two data-driven paradigms, classification and candidate selection, to link narrative observations of mobility status to standardized ICF codes, using a dataset of clinical narratives from physical therapy encounters. Recent advances in language modeling and word embedding were used as features for established machine learning models and a novel deep learning approach, achieving a macro-averaged F-1 score of 84% on linking mobility activity reports to ICF codes. Both classification and candidate selection approaches present distinct strengths for automated coding in under-studied domains, and we highlight that the combination of (i) a small annotated data set; (ii) expert definitions of codes of interest; and (iii) a representative text corpus is sufficient to produce high-performing automated coding systems. This research has implications for continued development of language technologies to analyze functional status information, and the ongoing growth of NLP tools for a variety of specialized applications in clinical care and research.

A comprehensive study of mobility functioning information in clinical notes: Entity hierarchy, corpus annotation, and sequence labeling

BACKGROUND

Secondary use of Electronic Health Records (EHRs) has mostly focused on health conditions (diseases and drugs). Function is an important health indicator in addition to morbidity and mortality. Nevertheless, function has been overlooked in accessing patients' health status. The World Health Organization (WHO)'s International Classification of Functioning, Disability and Health (ICF) is considered the international standard for describing and coding function and health states. We pioneer the first comprehensive analysis and identification of functioning concepts in the Mobility domain of the ICF.

RESULTS

Using physical therapy notes at the National Institutes of Health's Clinical Center, we induced a hierarchical order of mobility-related entities including 5 entities types, 3 relations, 8 attributes, and 33 attribute values. Two domain experts manually curated a gold standard corpus of 14,281 nested entity mentions from 400 clinical notes. Inter-annotator agreement (IAA) of exact matching averaged 92.3 % F1-score on mention text spans, and 96.6 % Cohen's kappa on attributes assignments. A high-performance Ensemble machine learning model for named entity recognition (NER) was trained and evaluated using the gold standard corpus. Average F1-score on exact entity matching of our Ensemble method (84.90 %) outperformed popular NER methods: Conditional Random Field (80.4 %), Recurrent Neural Network (81.82 %), and Bidirectional Encoder Representations from Transformers (82.33 %).

CONCLUSIONS

The results of this study show that mobility functioning information can be reliably captured from clinical notes once adequate resources are provided for sequence labeling methods. We expect that functioning concepts in other domains of the ICF can be identified in similar fashion.

Can structured EHR data support clinical coding? A data mining approach

Structured data formats are gaining momentum in electronic health records and can be leveraged for decision support and research. Nevertheless, such structured data formats have not been explored for clinical coding, which is an essential process requiring significant manual workload in health organisations. This article explores the extent to which fully structured clinical data can support assignment of clinical codes to inpatient episodes, through a methodology that tackles high dimensionality issues, addresses the multi-label nature of coding and optimises model parameters. The methodology encompasses transformation of raw data to define a feature set, build a data matrix representation, and testing combinations of feature selection methods with machine learning models to predict code assignment. The methodology was tested with a real hospital dataset and showed varying predictive power across codes, while demonstrating the potential of leveraging structuring data to reduce workload and increase efficiency in clinical coding.

Broadening horizons: the case for capturing function and the role of health informatics in its use

Background

Human activity and the interaction between health conditions and activity is a critical part of understanding the overall function of individuals. The World Health Organization’s International Classification of Functioning, Disability and Health (ICF) models function as all aspects of an individual’s interaction with the world, including organismal concepts such as individual body structures, functions, and pathologies, as well as the outcomes of the individual’s interaction with their environment, referred to as activity and participation. Function, particularly activity and participation outcomes, is an important indicator of health at both the level of an individual and the population level, as it is highly correlated with quality of life and a critical component of identifying resource needs. Since it reflects the cumulative impact of health conditions on individuals and is not disease specific, its use as a health indicator helps to address major barriers to holistic, patient-centered care that result from multiple, and often competing, disease specific interventions. While the need for better information on function has been widely endorsed, this has not translated into its routine incorporation into modern health systems.

Purpose

We present the importance of capturing information on activity as a core component of modern health systems and identify specific steps and analytic methods that can be used to make it more available to utilize in improving patient care. We identify challenges in the use of activity and participation information, such as a lack of consistent documentation and diversity of data specificity and representation across providers, health systems, and national surveys. We describe how activity and participation information can be more effectively captured, and how health informatics methodologies, including natural language processing (NLP), can enable automatically locating, extracting, and organizing this information on a large scale, supporting standardization and utilization with minimal additional provider burden. We examine the analytic requirements and potential challenges of capturing this information with informatics, and describe how data-driven techniques can combine with common standards and documentation practices to make activity and participation information standardized and accessible for improving patient care.

Recommendations

We recommend four specific actions to improve the capture and analysis of activity and participation information throughout the continuum of care: (1) make activity and participation annotation standards and datasets available to the broader research community; (2) define common research problems in automatically processing activity and participation information; (3) develop robust, machine-readable ontologies for function that describe the components of activity and participation information and their relationships; and (4) establish standards for how and when to document activity and participation status during clinical encounters. We further provide specific short-term goals to make significant progress in each of these areas within a reasonable time frame.

Development of a cardiac-centered frailty ontology

BACKGROUND

A Cardiac-centered Frailty Ontology can be an important foundation for using NLP to assess patient frailty. Frailty is an important consideration when making patient treatment decisions, particularly in older adults, those with a cardiac diagnosis, or when major surgery is a consideration. Clinicians often report patient's frailty in progress notes and other documentation. Frailty is recorded in many different ways in patient records and many different validated frailty-measuring instruments are available, with little consistency across instruments. We specifically explored concepts relevant to decisions regarding cardiac interventions. We based our work on text found in a large corpus of clinical notes from the Department of Veterans Affairs (VA) national Electronic Health Record (EHR) database.

RESULTS

The full ontology has 156 concepts, with 246 terms. It includes 86 concepts we expect to find in clinical documents, with 12 qualifier values. The remaining 58 concepts represent hierarchical groups (e.g., physical function findings). Our top-level class is clinical finding, which has children clinical history finding, instrument finding, and physical examination finding, reflecting the OGMS definition of clinical finding. Instrument finding is any score found for the existing frailty instruments. Within our ontology, we used SNOMED-CT concepts where possible. Some of the 86 concepts we expect to find in clinical documents are associated with the properties like ability interpretation. The concept ability to walk can either be able, assisted or unable. Each concept-property level pairing gets a different frailty score. Each scored concept received three scores: a frailty score, a relevance to cardiac decisions score, and a likelihood of resolving after the recommended intervention score. The ontology includes the relationship between scores from ten frailty instruments and frailty as assessed using ontology concepts. It also included rules for mapping ontology elements to instrument items for three common frailty assessment instruments. Ontology elements are used in two clinical NLP systems.

CONCLUSIONS

We developed and validated a Cardiac-centered Frailty Ontology, which is a machine-interoperable description of frailty that reflects all the areas that clinicians consider when deciding which cardiac intervention will best serve the patient as well as frailty indications generally relevant to medical decisions. The ontology owl file is available on Bioportal at http://bioportal.bioontology.org/ontologies/CCFO .

The International Classification of Functioning, Disability and Health (ICF) in Electronic Health Records. A Systematic Literature Review

BACKGROUND

The International Classification of Functioning, Disability and Health (ICF) is the World Health Organization's standard for describing health and health-related states. Examples of how the ICF has been used in Electronic Health Records (EHRs) have not been systematically summarized and described yet.

OBJECTIVES

To provide a systematic review of peer-reviewed literature about the ICF's use in EHRs, including related challenges and benefits.

METHODS

Peer-reviewed literature, published between January 2001 and July 2015 was retrieved from Medline^®, CINAHL^®, Scopus^®, and ProQuest^® Social Sciences using search terms related to ICF and EHR concepts. Publications were categorized according to three groups: Requirement specification, development and implementation. Information extraction was conducted according to a qualitative content analysis method, deductively informed by the evaluation framework for Health Information Systems: Human, Organization and Technology-fit (HOT-fit).

RESULTS

Of 325 retrieved articles, 17 publications were included; 4 were categorized as requirement specification, 7 as development, and 6 as implementation publications. Information regarding the HOT-fit evaluation framework was summarized. Main benefits of using the ICF in EHRs were its unique comprehensive perspective on health and its interdisciplinary focus. Main challenges included the fact that the ICF is not structured as a formal terminology as well as the need for a reduced number of ICF codes for more feasible and practical use.

CONCLUSION

Different approaches and technical solutions exist for integrating the ICF in EHRs, such as combining the ICF with other existing standards for EHR or selecting ICF codes with natural language processing. Though the use of the ICF in EHRs is beneficial as this review revealed, the ICF could profit from further improvements such as formalizing the knowledge representation in the ICF to support and enhance interoperability.

The International Classification of Functioning, Disability and Health (ICF) in Electronic Health Records. A Systematic Literature Review

BACKGROUND

The International Classification of Functioning, Disability and Health (ICF) is the World Health Organization's standard for describing health and health-related states. Examples of how the ICF has been used in Electronic Health Records (EHRs) have not been systematically summarized and described yet.

OBJECTIVES

To provide a systematic review of peer-reviewed literature about the ICF's use in EHRs, including related challenges and benefits.

METHODS

Peer-reviewed literature, published between January 2001 and July 2015 was retrieved from Medline^®, CINAHL^®, Scopus^®, and ProQuest^® Social Sciences using search terms related to ICF and EHR concepts. Publications were categorized according to three groups: Requirement specification, development and implementation. Information extraction was conducted according to a qualitative content analysis method, deductively informed by the evaluation framework for Health Information Systems: Human, Organization and Technology-fit (HOT-fit).

RESULTS

Of 325 retrieved articles, 17 publications were included; 4 were categorized as requirement specification, 7 as development, and 6 as implementation publications. Information regarding the HOT-fit evaluation framework was summarized. Main benefits of using the ICF in EHRs were its unique comprehensive perspective on health and its interdisciplinary focus. Main challenges included the fact that the ICF is not structured as a formal terminology as well as the need for a reduced number of ICF codes for more feasible and practical use.

CONCLUSION

Different approaches and technical solutions exist for integrating the ICF in EHRs, such as combining the ICF with other existing standards for EHR or selecting ICF codes with natural language processing. Though the use of the ICF in EHRs is beneficial as this review revealed, the ICF could profit from further improvements such as formalizing the knowledge representation in the ICF to support and enhance interoperability.

A hierarchical method to automatically encode Chinese diagnoses through semantic similarity estimation

Background

The accumulation of medical documents in China has rapidly increased in the past years. We focus on developing a method that automatically performs ICD-10 code assignment to Chinese diagnoses from the electronic medical records to support the medical coding process in Chinese hospitals.

Methods

We propose two encoding methods: one that directly determines the desired code (flat method), and one that hierarchically determines the most suitable code until the desired code is obtained (hierarchical method). Both methods are based on instances from the standard diagnostic library, a gold standard dataset in China. For the first time, semantic similarity estimation between Chinese words are applied in the biomedical domain with the successful implementation of knowledge-based and distributional approaches. Characteristics of the Chinese language are considered in implementing distributional semantics. We test our methods against 16,330 coding instances from our partner hospital.

Results

The hierarchical method outperforms the flat method in terms of accuracy and time complexity. Representing distributional semantics using Chinese characters can achieve comparable performance to the use of Chinese words. The diagnoses in the test set can be encoded automatically with micro-averaged precision of 92.57 %, recall of 89.63 %, and F-score of 91.08 %. A sharp decrease in encoding performance is observed without semantic similarity estimation.

Conclusion

The hierarchical nature of ICD-10 codes can enhance the performance of the automated code assignment. Semantic similarity estimation is demonstrated indispensable in dealing with Chinese medical text. The proposed method can greatly reduce the workload and improve the efficiency of the code assignment process in Chinese hospitals.

Electronic supplementary material

The online version of this article (doi:10.1186/s12911-016-0269-4) contains supplementary material, which is available to authorized users.

Representation of Functional Status Concepts from Clinical Documents and Social Media Sources by Standard Terminologies

Patient-reported functional status is widely recognized as an important patient-centered outcome that adds value to medical care, research, and quality improvement. Functional status outcomes are, however, not routinely or uniformly collected in the medical record, except in certain small patient populations (e.g. geriatrics, nursing home residents). To utilize patient reported functional status for clinical research and practice, we manually collected 2,763 terms from clinical records and social media sites and modeled them on the widely used Short Form-36 Health Survey. We then examined the coverage of the Unified Medical Language System (UMLS) for these functional status terms through automated mapping. Most terms (85.9%) did not have exact matches in the UMLS. The partial matches were prevalent, however, they typically did not capture the terms' exact semantics. Our study suggests that there is a need to extend existing standard terminologies to incorporate functional status terms used by patients and clinicians.

A systematic literature review of automated clinical coding and classification systems

Clinical coding and classification processes transform natural language descriptions in clinical text into data that can subsequently be used for clinical care, research, and other purposes. This systematic literature review examined studies that evaluated all types of automated coding and classification systems to determine the performance of such systems. Studies indexed in Medline or other relevant databases prior to March 2009 were considered. The 113 studies included in this review show that automated tools exist for a variety of coding and classification purposes, focus on various healthcare specialties, and handle a wide variety of clinical document types. Automated coding and classification systems themselves are not generalizable, nor are the results of the studies evaluating them. Published research shows these systems hold promise, but these data must be considered in context, with performance relative to the complexity of the task and the desired outcome.

Linking health and health-related information to the ICF: a systematic review of the literature from 2001 to 2008

INTRODUCTION

In 1976, the World Health Organization (WHO) estimated worldwide disability prevalence at 10%; recent evidence suggests the prevalence is even higher. Given the extent of disability around the world, it is essential for researchers and policy makers to have a uniform language for describing and discussing disability. The International Classification of Functioning, Disability and Health (ICF) is WHO's attempt to provide that standard language. Linking rules were published in 2002 and 2005 suggesting a method for standardising the process of connecting outcome measures to the ICF classification. The objective of this study is to study the extent to which the linking rules have been used by researchers to link health and health-related information to the ICF and collect the feedback about the current practices, applications and areas to improve the linking method.

METHOD

Using a systematic review of health-based literature between 2001 and February 2008, we (1) determined research areas where the linking method is applied, (2) examined the characteristics of studies that linked information to the ICF and (3) described current practices and issues related to the process of linking health and health-related information to the ICF both quantitatively and qualitatively.

RESULTS

The systematic review yielded 109 articles from 58 journals that linked health information to the ICF and 58 of the articles employed published linking rules. The majority of articles were descriptive in nature, used linking for connecting content of health instruments to the ICF and linked English health content. Quality controls such as reliability checks, multiple raters and iterative linking processes were found frequently among users of the linking rules. Qualitative analysis created themes about: preparing units of information, who links to the ICF, reliability, matching or translating concepts from text to ICF categories, information unable or difficult to capture, quantitative reporting standards and overall linking process.

DISCUSSION

This review also shows that the linking process is a useful way to apply the ICF classification in research. With over 100 articles published in 58 peer-reviewed journals across 50 focus areas, linking health and health-related information to the ICF has been shown to be a useful tool for describing, comparing and contrasting information from outcome measures used to collect quantitative data, qualitative research results and clinical patient reports across diagnoses, settings, languages and countries.

Systematic literature review on ICF from 2001 to 2009: its use, implementation and operationalisation

PURPOSE

To present a systematic literature review on the state of the art of the utilisation of the International Classification of Functioning, Disability and Health (ICF) since its release in 2001.

METHOD

The search was conducted through EMBASE, MEDLINE and PsychInfo covering the period between 2001 and December 2009. Papers were included if ICF was mentioned in title or abstract. Papers focussing on the ICF-CY and clinical research on children and youth only were excluded. Papers were assigned to six different groups covering the wide scenario of ICF application.

RESULTS

A total of 672 papers, coming from 34 countries and 211 different journals, were included in the analysis. The majority of publications (30.8%) were conceptual papers or papers reporting clinical and rehabilitation studies (25.9%). One-third of the papers were published in 2008 and 2009.

CONCLUSIONS

The ICF contributed to the development of research on functioning and on disability in clinical, rehabilitation as well as in several other contexts, such as disability eligibility and employment. Diffusion of ICF research and use in a great variety of fields and scientific journals is a proof that a cultural change and a new conceptualisation of functioning and disability is happening.

Biomedical informatics and translational medicine

Biomedical informatics involves a core set of methodologies that can provide a foundation for crossing the "translational barriers" associated with translational medicine. To this end, the fundamental aspects of biomedical informatics (e.g., bioinformatics, imaging informatics, clinical informatics, and public health informatics) may be essential in helping improve the ability to bring basic research findings to the bedside, evaluate the efficacy of interventions across communities, and enable the assessment of the eventual impact of translational medicine innovations on health policies. Here, a brief description is provided for a selection of key biomedical informatics topics (Decision Support, Natural Language Processing, Standards, Information Retrieval, and Electronic Health Records) and their relevance to translational medicine. Based on contributions and advancements in each of these topic areas, the article proposes that biomedical informatics practitioners ("biomedical informaticians") can be essential members of translational medicine teams.

Identification of documented medication non-adherence in physician notes

Medication non-adherence is common and the physicians awareness of it may be an important factor in clinical decision making. Few sources of data on physician awareness of medication non-adherence are available. We have designed an algorithm to identify documentation of medication non-adherence in the text of physician notes. The algorithm recognizes eight semantic classes of documentation of medication non-adherence. We evaluated the algorithm against manual ratings of 200 randomly selected notes of hypertensive patients. The algorithm detected 89% of the notes with documented medication non-adherence with specificity of 84.7% and positive predictive value of 80.2%. In a larger dataset of 1,000 documents, notes that documented medication non-adherence were more likely to report significantly elevated systolic (15.3% vs. 9.0%; p = 0.002) and diastolic (4.1% vs. 1.9%; p = 0.03) blood pressure. This novel clinically validated tool expands the range of information on medication non-adherence available to researchers.

Identifying patient smoking status from medical discharge records

The authors organized a Natural Language Processing (NLP) challenge on automatically determining the smoking status of patients from information found in their discharge records. This challenge was issued as a part of the i2b2 (Informatics for Integrating Biology to the Bedside) project, to survey, facilitate, and examine studies in medical language understanding for clinical narratives. This article describes the smoking challenge, details the data and the annotation process, explains the evaluation metrics, discusses the characteristics of the systems developed for the challenge, presents an analysis of the results of received system runs, draws conclusions about the state of the art, and identifies directions for future research. A total of 11 teams participated in the smoking challenge. Each team submitted up to three system runs, providing a total of 23 submissions. The submitted system runs were evaluated with microaveraged and macroaveraged precision, recall, and F-measure. The systems submitted to the smoking challenge represented a variety of machine learning and rule-based algorithms. Despite the differences in their approaches to smoking status identification, many of these systems provided good results. There were 12 system runs with microaveraged F-measures above 0.84. Analysis of the results highlighted the fact that discharge summaries express smoking status using a limited number of textual features (e.g., "smok", "tobac", "cigar", Social History, etc.). Many of the effective smoking status identifiers benefit from these features.