Translational NLP: A New Paradigm and General Principles for Natural Language Processing Research

Newman-Griffis, Denis; Lehman, Jill Fain; Rosé, Carolyn Penstein; Hochheiser, Harry

doi:10.18653/v1/2021.naacl-main.325

Cited by 10 publications

(7 citation statements)

References 56 publications

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“…Rigby et al [ 91 ] identified several aspects of mental health care that are still challenging for mental health informatics 2 decades later, including the importance of a longitudinal view. By identifying clear links to existing NLP research, our framework can serve to guide translational NLP research [ 92 ] in the mental health domain. This work can help identify both processes for translating existing NLP technologies into robust solutions for application in mental health research and care as well as new research questions for progress on the needs of mental health informatics.…”

Section: Discussionmentioning

confidence: 99%

Information Extraction Framework for Disability Determination Using a Mental Functioning Use-Case

Zirikly¹,

Desmet²,

Newman-Griffis³

et al. 2022

JMIR Med Inform

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Natural language processing (NLP) in health care enables transformation of complex narrative information into high value products such as clinical decision support and adverse event monitoring in real time via the electronic health record (EHR). However, information technologies for mental health have consistently lagged because of the complexity of measuring and modeling mental health and illness. The use of NLP to support management of mental health conditions is a viable topic that has not been explored in depth. This paper provides a framework for the advanced application of NLP methods to identify, extract, and organize information on mental health and functioning to inform the decision-making process applied to assessing mental health. We present a use-case related to work disability, guided by the disability determination process of the US Social Security Administration (SSA). From this perspective, the following questions must be addressed about each problem that leads to a disability benefits claim: When did the problem occur and how long has it existed? How severe is it? Does it affect the person’s ability to work? and What is the source of the evidence about the problem? Our framework includes 4 dimensions of medical information that are central to assessing disability—temporal sequence and duration, severity, context, and information source. We describe key aspects of each dimension and promising approaches for application in mental functioning. For example, to address temporality, a complete functional timeline must be created with all relevant aspects of functioning such as intermittence, persistence, and recurrence. Severity of mental health symptoms can be successfully identified and extracted on a 4-level ordinal scale from absent to severe. Some NLP work has been reported on the extraction of context for specific cases of wheelchair use in clinical settings. We discuss the links between the task of information source assessment and work on source attribution, coreference resolution, event extraction, and rule-based methods. Gaps were identified in NLP applications that directly applied to the framework and in existing relevant annotated data sets. We highlighted NLP methods with the potential for advanced application in the field of mental functioning. Findings of this work will inform the development of instruments for supporting SSA adjudicators in their disability determination process. The 4 dimensions of medical information may have relevance for a broad array of individuals and organizations responsible for assessing mental health function and ability. Further, our framework with 4 specific dimensions presents significant opportunity for the application of NLP in the realm of mental health and functioning beyond the SSA setting, and it may support the development of robust tools and methods for decision-making related to clinical care, program implementation, and other outcomes.

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Section: Discussionmentioning

confidence: 99%

Information Extraction Framework for Disability Determination Using a Mental Functioning Use-Case

Zirikly¹,

Desmet²,

Newman-Griffis³

et al. 2022

JMIR Med Inform

Self Cite

View full text Add to dashboard Cite

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“…A generic approach to NLP model development for peer‐feedback can be derived following the recently proposed framework of Translational NLP (Newman‐Griffis et al, 2021). Based on the automation goal and available data, an appropriate NLP task type is chosen, along with the formal definitions of the prediction target, textual input, and evaluation metric.…”

Section: Educational Research and Nlp For Adaptive Peer‐feedback Supportmentioning

confidence: 99%

Using natural language processing to support peer‐feedback in the age of artificial intelligence: A cross‐disciplinary framework and a research agenda

Bauer

Greisel

Kuznetsov

et al. 2023

Brit J Educational Tech

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Advancements in artificial intelligence are rapidly increasing. The new‐generation large language models, such as ChatGPT and GPT‐4, bear the potential to transform educational approaches, such as peer‐feedback. To investigate peer‐feedback at the intersection of natural language processing (NLP) and educational research, this paper suggests a cross‐disciplinary framework that aims to facilitate the development of NLP‐based adaptive measures for supporting peer‐feedback processes in digital learning environments. To conceptualize this process, we introduce a peer‐feedback process model, which describes learners' activities and textual products. Further, we introduce a terminological and procedural scheme that facilitates systematically deriving measures to foster the peer‐feedback process and how NLP may enhance the adaptivity of such learning support. Building on prior research on education and NLP, we apply this scheme to all learner activities of the peer‐feedback process model to exemplify a range of NLP‐based adaptive support measures. We also discuss the current challenges and suggest directions for future cross‐disciplinary research on the effectiveness and other dimensions of NLP‐based adaptive support for peer‐feedback. Building on our suggested framework, future research and collaborations at the intersection of education and NLP can innovate peer‐feedback in digital learning environments. Practitioner notesWhat is already known about this topic There is considerable research in educational science on peer‐feedback processes. Natural language processing facilitates the analysis of students' textual data. There is a lack of systematic orientation regarding which NLP techniques can be applied to which data to effectively support the peer‐feedback process. What this paper adds A comprehensive overview model that describes the relevant activities and products in the peer‐feedback process. A terminological and procedural scheme for designing NLP‐based adaptive support measures. An application of this scheme to the peer‐feedback process results in exemplifying the use cases of how NLP may be employed to support each learner activity during peer‐feedback. Implications for practice and/or policy To boost the effectiveness of their peer‐feedback scenarios, instructors and instructional designers should identify relevant leverage points, corresponding support measures, adaptation targets and automation goals based on theory and empirical findings. Management and IT departments of higher education institutions should strive to provide digital tools based on modern NLP models and integrate them into the respective learning management systems; those tools should help in translating the automation goals requested by their instructors into prediction targets, take relevant data as input and allow for evaluating the predictions.

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“…However, the progress notes from the nursing chart remained in Chinese because such information can be used for natural language processing. Some embedding features might be lost or modified when they are translated into other languges ( 24 ). In the meantime, some impossible date entries (follow-up date earlier than the discharge date), impossible values from the nursing chart (i.e., respiratory rate = 2), and outliers (i.e., tidal volume = 30) were either removed or updated after a manual check.…”

Section: Data Recordsmentioning

confidence: 99%

Critical Care Database Comprising Patients With Infection

Chen

Zhu

et al. 2022

Front. Public Health

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Patients treated in the intensive care unit (ICU) are closely monitored and receive intensive treatment. Such aggressive monitoring and treatment will generate high-granularity data from both electronic healthcare records and nursing charts. These data not only provide infrastructure for daily clinical practice but also can help to inform clinical studies. It is technically challenging to integrate and cleanse medical data from a variety of sources. Although there are several open-access critical care databases from western countries, there is a lack of this kind of database for Chinese adult patients. We established a critical care database involving patients with infection. A large proportion of these patients have sepsis and/or septic shock. High-granularity data comprising laboratory findings, baseline characteristics, medications, international statistical classification of diseases (ICD) code, nursing charts, and follow-up results were integrated to generate a comprehensive database. The database can be utilized for a variety of clinical studies. The dataset is fully accessible at PhysioNet(https://physionet.org/content/icu-infection-zigong-fourth/1.0/).

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Translational NLP: A New Paradigm and General Principles for Natural Language Processing Research

Cited by 10 publications

References 56 publications

Information Extraction Framework for Disability Determination Using a Mental Functioning Use-Case

Information Extraction Framework for Disability Determination Using a Mental Functioning Use-Case

Using natural language processing to support peer‐feedback in the age of artificial intelligence: A cross‐disciplinary framework and a research agenda

Critical Care Database Comprising Patients With Infection

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