Natural Language Processing and the Promise of Big Data

Maddox, Thomas M.; Matheny, Michael

doi:10.1161/circoutcomes.115.002125

Cited by 22 publications

(21 citation statements)

References 11 publications

(16 reference statements)

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“…NLP and cluster analysis resulted in 3 distinguished clustering groups that were labeled as 1) the Weight Loss group, 2) the Illness Prevention group, and 3) the Health Promotion group. [16-20] In a recent study of applying NLP to EHR to automatically assess delivery of weight management counseling in two regions of Kaiser Permanente, it was demonstrated that NLP had similar capabilities as trained medical record abstractors [16]. Additionally, use of a Wikipedia data dump in our NLP analysis in this paper was supported by the study finding by Ramesh and colleagues in 2013 that Wikipedia, compared to MedlinePlus and the Unified Medical Language System, significantly improved EHR note readability [19].…”

Section: Discussionmentioning

confidence: 99%

“…In this paper, semi-structured interview data collected at a 12-month telephone interview (study exit) were analyzed by natural language processing (NLP), a field of computer science which incorporates artificial intelligence and computational linguistics [15] to formulate algorithms used to extract information from textual inputs. Use of NLP in clinical and medical research began to appear in the 1980s, primarily by applying it to electronic health records (EHRs) [16-19], while NLP was brought into broader use more recently [20]. However, its application to behavioral research is still in its infancy.…”

Section: Introductionmentioning

confidence: 99%

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Applying Natural Language Processing to Understand Motivational Profiles for Maintaining Physical Activity After a Mobile App and Accelerometer-Based Intervention: The mPED Randomized Controlled Trial

Fukuoka¹,

Lindgren²,

Mintz³

et al. 2018

JMIR Mhealth Uhealth

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BackgroundRegular physical activity is associated with reduced risk of chronic illnesses. Despite various types of successful physical activity interventions, maintenance of activity over the long term is extremely challenging.ObjectiveThe aims of this original paper are to 1) describe physical activity engagement post intervention, 2) identify motivational profiles using natural language processing (NLP) and clustering techniques in a sample of women who completed the physical activity intervention, and 3) compare sociodemographic and clinical data among these identified cluster groups.MethodsIn this cross-sectional analysis of 203 women completing a 12-month study exit (telephone) interview in the mobile phone-based physical activity education study were examined. The mobile phone-based physical activity education study was a randomized, controlled trial to test the efficacy of the app and accelerometer intervention and its sustainability over a 9-month period. All subjects returned the accelerometer and stopped accessing the app at the last 9-month research office visit. Physical engagement and motivational profiles were assessed by both closed and open-ended questions, such as “Since your 9-month study visit, has your physical activity been more, less, or about the same (compared to the first 9 months of the study)?” and, “What motivates you the most to be physically active?” NLP and cluster analysis were used to classify motivational profiles. Descriptive statistics were used to compare participants’ baseline characteristics among identified groups.ResultsApproximately half of the 2 intervention groups (Regular and Plus) reported that they were still wearing an accelerometer and engaging in brisk walking as they were directed during the intervention phases. These numbers in the 2 intervention groups were much higher than the control group (overall P=.01 and P=.003, respectively). Three clusters were identified through NLP and named as the Weight Loss group (n=19), the Illness Prevention group (n=138), and the Health Promotion group (n=46). The Weight Loss group was significantly younger than the Illness Prevention and Health Promotion groups (overall P<.001). The Illness Prevention group had a larger number of Caucasians as compared to the Weight Loss group (P=.001), which was composed mostly of those who identified as African American, Hispanic, or mixed race. Additionally, the Health Promotion group tended to have lower BMI scores compared to the Illness Prevention group (overall P=.02). However, no difference was noted in the baseline moderate-to-vigorous intensity activity level among the 3 groups (overall P>.05).ConclusionsThe findings could be relevant to tailoring a physical activity maintenance intervention. Furthermore, the findings from NLP and cluster analysis are useful methods to analyze short free text to differentiate motivational profiles. As more sophisticated NL tools are developed in the future, the potential of NLP application in behavioral research will broaden.Trial RegistrationClinical...

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Applying Natural Language Processing to Understand Motivational Profiles for Maintaining Physical Activity After a Mobile App and Accelerometer-Based Intervention: The mPED Randomized Controlled Trial

Fukuoka¹,

Lindgren²,

Mintz³

et al. 2018

JMIR Mhealth Uhealth

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“…Finally, innovations in methods such as natural language processing are needed to reliably categorize the large amount of unstructured data that currently exist in EHRs. [45][46][47] As these and other innovations occur, EHR data will occupy a central role in generating meaningful knowledge in support of LHSs.…”

Section: Ehr Datamentioning

confidence: 99%

“…Thus, there is a need for consistent care documentation, common data elements, data interoperability, and capture of semistructured and structured data to fully harness data necessary for effective predictive analytics. 45,89,[116][117][118] Second, emerging data sources such as genomic information will require organization in a manner that can be effectively applied to analytics engines. Third, there is significant complexity in techniques that assess large amounts of data and generate insights.…”

Section: Predictive Analyticsmentioning

confidence: 99%

The Learning Healthcare System and Cardiovascular Care: A Scientific Statement From the American Heart Association

Maddox¹,

Albert²,

Borden³

et al. 2017

Circulation

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118

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ABSTRACT:The learning healthcare system uses health information technology and the health data infrastructure to apply scientific evidence at the point of clinical care while simultaneously collecting insights from that care to promote innovation in optimal healthcare delivery and to fuel new scientific discovery. To achieve these goals, the learning healthcare system requires systematic redesign of the current healthcare system, focusing on 4 major domains: science and informatics, patient-clinician partnerships, incentives, and development of a continuous learning culture. This scientific statement provides an overview of how these learning healthcare system domains can be realized in cardiovascular disease care. Current cardiovascular disease care innovations in informatics, data uses, patient engagement, continuous learning culture, and incentives are profiled. In addition, recommendations for next steps for the development of a learning healthcare system in cardiovascular care are presented.H ealth care has never been more complex. Clinicians and patients must make decisions that integrate the continually evolving scientific evidence base with hundreds of individual data points such as patients' vital signs, symptoms, comorbidities, medications, test results, and preferences. Furthermore, once these decisions are made, little information is available about their impact, limiting the ability to learn from and ultimately improve care delivery. This inability of the healthcare system to learn from its operation results in significant inefficiencies, substantial costs, and suboptimal health outcomes.The creation of a learning healthcare system (LHS) can potentially address these issues. The LHS uses health information technology and the health data infrastructure to apply scientific evidence at the point of clinical care while simultaneously collecting insights from that care to promote innovation in optimal healthcare delivery and to fuel new scientific discovery 1,2 (Figure 1). Thus, the LHS enables rapid, iterative learning in which "evidence informs practice, and practice informs evidence." 2 The authors argued that recent advances in information processing and connectivity, healthcare organizational design, and reimbursement policies centered on quality rather than quantity of care provided the necessary tools for the creation of the LHS. Accordingly, they called for systematic redesign of the healthcare system, focusing on 4 major domains: science and informatics, patient-clinician partnerships, incentives, and development of a continuous learning culture.

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“…These studies focused on applying natural language processing (NLP) for information extraction, such as identifying people with type 2 DM with a specific phenotype, estimating the occurrence of hypoglycaemia and extracting the lab test results . However, limitations exist when NLP techniques were used in these studies, such as disagreement with manual classification, decreasing accuracy with semantically complex sentences, having difficulty distinguishing acronyms and abbreviations with different meanings and demonstrating successes in specific research settings . Considering the lack of NLP applications for information discovery in diabetes education using EHR physician note sections, we opted to manually code clinic notes in this study as a feasibility study.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of provider documentation patterns as a tool to deliver ongoing patient‐centred diabetes education and support

Patel

Khan

et al. 2019

Int J Clin Pract

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Background Diabetes mellitus (DM) is one of the most common chronic diseases in the world. As a disease with long‐term complications requiring changes in management, DM requires not only education at the time of diagnosis, but ongoing diabetes self‐management education and support (DSME/S). In the United States, however, only a small proportion of people with DM receive DSME/S, although evidence supports benefits of ongoing DSME/S. The diabetes education that providers deliver during follow‐up visits may be an important source for DSME/S for many people with DM. Methods We collected 200 clinic notes of follow‐up visits for 100 adults with DM and studied the History of Present Illness (HPI) and Impression and Plan (I&P) sections. Using a codebook based on the seven principles of American Association of Diabetes Educators Self‐Care Behaviors (AADE7), we conducted a multi‐step deductive thematic analysis to determine the patterns of DSME/S information occurrence in clinic notes. Additionally, we used the generalised linear mixed models for investigating whether providers delivered DSME/S to people with DM based on patient characteristics. Results During follow‐up visits, Monitoring was the most common self‐care behaviour mentioned in both HPI and I&P sections. Being Active was the least common self‐care behaviour mentioned in the HPI section and Healthy Coping was the least common self‐care behaviour mentioned in the I&P section. We found providers delivered more information on Healthy Eating to men compared to women in I&P section. Generally, providers delivered DSME/S to people with DM regardless of patient characteristics. Conclusions This study focused on the frequency distribution of information providers delivered to the people with DM during follow‐up clinic visits based on the AADE7. The results may indicate a lack of patient‐centred education when people with DM visit providers for ongoing management. Further studies are needed to identify the underlying reasons why providers have difficulty delivering patient‐centred education.

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Natural Language Processing and the Promise of Big Data

Cited by 22 publications

References 11 publications

Applying Natural Language Processing to Understand Motivational Profiles for Maintaining Physical Activity After a Mobile App and Accelerometer-Based Intervention: The mPED Randomized Controlled Trial

Applying Natural Language Processing to Understand Motivational Profiles for Maintaining Physical Activity After a Mobile App and Accelerometer-Based Intervention: The mPED Randomized Controlled Trial

The Learning Healthcare System and Cardiovascular Care: A Scientific Statement From the American Heart Association

Evaluation of provider documentation patterns as a tool to deliver ongoing patient‐centred diabetes education and support

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