ADVANCING AN INTERDISCIPLINARY SCIENCE OF AGING THROUGH A PRACTICE-BASED DATA SCIENCE APPROACH

Abstract Two hundred and fifty thousand older adults die annually in United States hospitals because of iatrogenic conditions (ICs). Clinicians, aging experts, patient advocates and federal policy makers agree that there is a need to enhance the safety of hospitalized older adults through improved identification and prevention of ICs. To this end, we are building a research program with the goal of enhancing the safety of hospitalized older adults by reducing ICs through an effective learning health system. Leveraging unique electronic data and healthcare system and human resources at the University of Florida, we are applying a state-of-the-art practice-based data science approach to identify risk factors of ICs (e.g., falls) from structured (i.e., nursing, clinical, administrative) and unstructured or text (i.e., registered nurse’s progress notes) data. Our interdisciplinary academic-clinical partnership includes scientific and clinical experts in patient safety, care quality, health outcomes, nursing and health informatics, natural language processing, data science, aging, standardized terminology, clinical decision support, statistics, machine learning, and hospital operations. Results to date have uncovered previously unknown fall risk factors within nursing (i.e., physical therapy initiation), clinical (i.e., number of fall risk increasing drugs, hemoglobin level), and administrative (i.e., Charlson Comorbidity Index, nurse skill mix, and registered nurse staffing ratio) structured data as well as patient cognitive, environmental, workflow, and communication factors in text data. The application of data science methods (i.e., machine learning and text-mining) and findings from this research will be used to develop text-mining pipelines to support sustained data-driven interdisciplinary aging studies to reduce ICs.

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Predicting Inpatient Falls Using Natural Language Processing of Nursing Records Obtained From Japanese Electronic Medical Records: Case-Control Study

JMIR Medical Informatics ◽

10.2196/16970 ◽

2020 ◽

Vol 8 (4) ◽

pp. e16970 ◽

Cited By ~ 1

Author(s):

Hayao Nakatani ◽

Masatoshi Nakao ◽

Hidefumi Uchiyama ◽

Hiroyoshi Toyoshiba ◽

Chikayuki Ochiai

Keyword(s):

Machine Learning ◽

Risk Factors ◽

Health Care ◽

Electronic Medical Records ◽

Language Processing ◽

Medical Records ◽

Fall Risk ◽

Data Set ◽

Inpatient Falls ◽

Risk Factors For Falls

Background Falls in hospitals are the most common risk factor that affects the safety of inpatients and can result in severe harm. Therefore, preventing falls is one of the most important areas of risk management for health care organizations. However, existing methods for predicting falls are laborious and costly. Objective The objective of this study is to verify whether hospital inpatient falls can be predicted through the analysis of a single input—unstructured nursing records obtained from Japanese electronic medical records (EMRs)—using a natural language processing (NLP) algorithm and machine learning. Methods The nursing records of 335 fallers and 408 nonfallers for a 12-month period were extracted from the EMRs of an acute care hospital and randomly divided into a learning data set and test data set. The former data set was subjected to NLP and machine learning to extract morphemes that contributed to separating fallers from nonfallers to construct a model for predicting falls. Then, the latter data set was used to determine the predictive value of the model using receiver operating characteristic (ROC) analysis. Results The prediction of falls using the test data set showed high accuracy, with an area under the ROC curve, sensitivity, specificity, and odds ratio of mean 0.834 (SD 0.005), mean 0.769 (SD 0.013), mean 0.785 (SD 0.020), and mean 12.27 (SD 1.11) for five independent experiments, respectively. The morphemes incorporated into the final model included many words closely related to known risk factors for falls, such as the use of psychotropic drugs, state of consciousness, and mobility, thereby demonstrating that an NLP algorithm combined with machine learning can effectively extract risk factors for falls from nursing records. Conclusions We successfully established that falls among hospital inpatients can be predicted by analyzing nursing records using an NLP algorithm and machine learning. Therefore, it may be possible to develop a fall risk monitoring system that analyzes nursing records daily and alerts health care professionals when the fall risk of an inpatient is increased.

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Predicting Inpatient Falls Using Natural Language Processing of Nursing Records Obtained From Japanese Electronic Medical Records: Case-Control Study (Preprint)

10.2196/preprints.16970 ◽

2019 ◽

Author(s):

Hayao Nakatani ◽

Masatoshi Nakao ◽

Hidefumi Uchiyama ◽

Hiroyoshi Toyoshiba ◽

Chikayuki Ochiai

Keyword(s):

Machine Learning ◽

Risk Factors ◽

Health Care ◽

Electronic Medical Records ◽

Language Processing ◽

Medical Records ◽

Fall Risk ◽

Data Set ◽

Inpatient Falls ◽

Risk Factors For Falls

BACKGROUND Falls in hospitals are the most common risk factor that affects the safety of inpatients and can result in severe harm. Therefore, preventing falls is one of the most important areas of risk management for health care organizations. However, existing methods for predicting falls are laborious and costly. OBJECTIVE The objective of this study is to verify whether hospital inpatient falls can be predicted through the analysis of a single input—unstructured nursing records obtained from Japanese electronic medical records (EMRs)—using a natural language processing (NLP) algorithm and machine learning. METHODS The nursing records of 335 fallers and 408 nonfallers for a 12-month period were extracted from the EMRs of an acute care hospital and randomly divided into a learning data set and test data set. The former data set was subjected to NLP and machine learning to extract morphemes that contributed to separating fallers from nonfallers to construct a model for predicting falls. Then, the latter data set was used to determine the predictive value of the model using receiver operating characteristic (ROC) analysis. RESULTS The prediction of falls using the test data set showed high accuracy, with an area under the ROC curve, sensitivity, specificity, and odds ratio of mean 0.834 (SD 0.005), mean 0.769 (SD 0.013), mean 0.785 (SD 0.020), and mean 12.27 (SD 1.11) for five independent experiments, respectively. The morphemes incorporated into the final model included many words closely related to known risk factors for falls, such as the use of psychotropic drugs, state of consciousness, and mobility, thereby demonstrating that an NLP algorithm combined with machine learning can effectively extract risk factors for falls from nursing records. CONCLUSIONS We successfully established that falls among hospital inpatients can be predicted by analyzing nursing records using an NLP algorithm and machine learning. Therefore, it may be possible to develop a fall risk monitoring system that analyzes nursing records daily and alerts health care professionals when the fall risk of an inpatient is increased.

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The Relationship Between Fear of Falling and Functional Fitness among Older Adults Living in Rest Home

10.35262/jiag.v16i1.5-10 ◽

2020 ◽

Vol 16 (1) ◽

Author(s):

H Tuna ◽

Ö Bozan ◽

B Gürpınar ◽

N İlçin

Keyword(s):

Risk Factors ◽

Older Adults ◽

Fall Risk ◽

Fear Of Falling ◽

Dynamic Balance ◽

Upper Body ◽

Functional Fitness ◽

Fall Risk Factors ◽

Rest Home ◽

History Of

Objective: This study aimed to report the fear of falling and assess its associations with several fall-related characteristics and functional fitness parameters among older adults living in the rest home. Methods: Seventy-eight older adults aged between 65-94 years were included in the study. History of falling and the number of risk factors for falling were recorded. Fear of falling was evaluated with The Falls Efficacy ScaleInternational. Functional fitness was assessed with Senior Fitness Test, including tests for the functional measurement of strength, flexibility, aerobic endurance and dynamic balance. Result: The mean age of participants was 78.46±7.16 years. There were correlations exist between fear of falling and number of fall risk factors, dynamic balance, upper body flexibility and aerobic endurance (p<0.05). Multiple linear regression analysis showed that the parameters with the highest determinants of fear of falling were the dynamic balance and history of falling (p<0.05). Conclusions: In our study, history of falling, number of fall risk factors, flexibility for the upper body, aerobic endurance and dynamic balance were parameters related to fear of falling among older adults, but the most influential factors in fear of falling were dynamic balance and history of falling.

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Modern Clinical Text Mining: A Guide and Review

Annual Review of Biomedical Data Science ◽

10.1146/annurev-biodatasci-030421-030931 ◽

2021 ◽

Vol 4 (1) ◽

Author(s):

Bethany Percha

Keyword(s):

Machine Learning ◽

Text Mining ◽

Data Science ◽

Annual Review ◽

Publication Date ◽

Biomedical Data ◽

Clinical Text ◽

Quality Improvement Research ◽

Comprehensive Survey ◽

Technical Advances

Electronic health records (EHRs) are becoming a vital source of data for healthcare quality improvement, research, and operations. However, much of the most valuable information contained in EHRs remains buried in unstructured text. The field of clinical text mining has advanced rapidly in recent years, transitioning from rule-based approaches to machine learning and, more recently, deep learning. With new methods come new challenges, however, especially for those new to the field. This review provides an overview of clinical text mining for those who are encountering it for the first time (e.g., physician researchers, operational analytics teams, machine learning scientists from other domains). While not a comprehensive survey, this review describes the state of the art, with a particular focus on new tasks and methods developed over the past few years. It also identifies key barriers between these remarkable technical advances and the practical realities of implementation in health systems and in industry. Expected final online publication date for the Annual Review of Biomedical Data Science, Volume 4 is July 2021. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

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Using Tai Chi to Reduce Fall Risk Factors Among Older Adults: An Evaluation of a Community-Based Implementation

Journal of Applied Gerontology ◽

10.1177/0733464817703004 ◽

2017 ◽

Vol 38 (7) ◽

pp. 983-998 ◽

Cited By ~ 5

Author(s):

Mary P. Gallant ◽

Meaghan Tartaglia ◽

Susan Hardman ◽

Kara Burke

Keyword(s):

Risk Factors ◽

Older Adults ◽

Fall Risk ◽

Tai Chi ◽

Community Based ◽

Fall Risk Factors

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Multidimensional Sleep and Mortality in Older Adults: A Machine-Learning Comparison With Other Risk Factors

The Journals of Gerontology Series A ◽

10.1093/gerona/glz044 ◽

2019 ◽

Vol 74 (12) ◽

pp. 1903-1909 ◽

Cited By ~ 9

Author(s):

Meredith L Wallace ◽

Daniel J Buysse ◽

Susan Redline ◽

Katie L Stone ◽

Kristine Ensrud ◽

...

Keyword(s):

Machine Learning ◽

Risk Factors ◽

Older Adults ◽

Cardiovascular Mortality ◽

Predictive Ability ◽

Future Research ◽

Applied Machine Learning ◽

Predictive Variables ◽

Time In Bed

Abstract Background Sleep characteristics related to duration, timing, continuity, and sleepiness are associated with mortality in older adults, but rarely considered in health recommendations. We applied machine learning to: (i) establish the predictive ability of a multidimensional self-reported sleep domain for all-cause and cardiovascular mortality in older adults relative to other established risk factors and (ii) to identify which sleep characteristics are most predictive. Methods The analytic sample includes N = 8,668 older adults (54% female) aged 65–99 years with self-reported sleep characterization and longitudinal follow-up (≤15.5 years), aggregated from three epidemiological cohorts. We used variable importance (VIMP) metrics from a random survival forest to rank the predictive abilities of 47 measures and domains to which they belong. VIMPs > 0 indicate predictive variables/domains. Results Multidimensional sleep was a significant predictor of all-cause (VIMP [99.9% confidence interval {CI}] = 0.94 [0.60, 1.29]) and cardiovascular (1.98 [1.31, 2.64]) mortality. For all-cause mortality, it ranked below that of the sociodemographic (3.94 [3.02, 4.87]), physical health (3.79 [3.01, 4.57]), and medication (1.33 [0.94, 1.73]) domains but above that of the health behaviors domain (0.22 [0.06, 0.38]). The domains were ranked similarly for cardiovascular mortality. The most predictive individual sleep characteristics across outcomes were time in bed, hours spent napping, and wake-up time. Conclusion Multidimensional sleep is an important predictor of mortality that should be considered among other more routinely used predictors. Future research should develop tools for measuring multidimensional sleep—especially those incorporating time in bed, napping, and timing—and test mechanistic pathways through which these characteristics relate to mortality.

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Cognitive Compliance: Assessing Regulatory Risk in Financial Advice Documents

Proceedings of the AAAI Conference on Artificial Intelligence ◽

10.1609/aaai.v34i09.7105 ◽

2020 ◽

Vol 34 (09) ◽

pp. 13636-13637

Author(s):

Wanita Sherchan ◽

Sue Ann Chen ◽

Simon Harris ◽

Nebula Alam ◽

Khoi-Nguyen Tran ◽

...

Keyword(s):

Machine Learning ◽

Risk Factors ◽

Deep Learning ◽

Language Processing ◽

Regulatory Compliance ◽

Rapid Identification ◽

Traffic Light ◽

Risk Status ◽

Government Regulations ◽

Financial Advice

This paper describes Cognitive Compliance - a solution that automates the complex manual process of assessing regulatory compliance of personal financial advice. The solution uses natural language processing (NLP), machine learning and deep learning to characterise the regulatory risk status of personal financial advice documents with traffic light rating for various risk factors. This enables comprehensive coverage of the review and rapid identification of documents at high risk of non-compliance with government regulations.

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