38,360 research outputs found
Affective Medicine: a review of Affective Computing efforts in Medical Informatics
Background: Affective computing (AC) is concerned with emotional interactions performed with and through computers. It is defined as “computing that relates to, arises from, or deliberately influences emotions”. AC enables investigation and understanding of the relation between human emotions and health as well as application of assistive and useful technologies in the medical domain. Objectives: 1) To review the general state of the art in AC and its applications in medicine, and 2) to establish synergies between the research communities of AC and medical informatics. Methods: Aspects related to the human affective state as a determinant of the human health are discussed, coupled with an illustration of significant AC research and related literature output. Moreover, affective communication channels are described and their range of application fields is explored through illustrative examples. Results: The presented conferences, European research projects and research publications illustrate the recent increase of interest in the AC area by the medical community. Tele-home healthcare, AmI, ubiquitous monitoring, e-learning and virtual communities with emotionally expressive characters for elderly or impaired people are few areas where the potential of AC has been realized and applications have emerged. Conclusions: A number of gaps can potentially be overcome through the synergy of AC and medical informatics. The application of AC technologies parallels the advancement of the existing state of the art and the introduction of new methods. The amount of work and projects reviewed in this paper witness an ambitious and optimistic synergetic future of the affective medicine field
An Exploratory Study of Patient Falls
Debate continues between the contribution of education level and clinical expertise in the nursing practice environment. Research suggests a link between Baccalaureate of Science in Nursing (BSN) nurses and positive patient outcomes such as lower mortality, decreased falls, and fewer medication errors. Purpose: To examine if there a negative correlation between patient falls and the level of nurse education at an urban hospital located in Midwest Illinois during the years 2010-2014? Methods: A retrospective crosssectional cohort analysis was conducted using data from the National Database of Nursing Quality Indicators (NDNQI) from the years 2010-2014. Sample: Inpatients aged ≥ 18 years who experienced a unintentional sudden descent, with or without injury that resulted in the patient striking the floor or object and occurred on inpatient nursing units. Results: The regression model was constructed with annual patient falls as the dependent variable and formal education and a log transformed variable for percentage of certified nurses as the independent variables. The model overall is a good fit, F (2,22) = 9.014, p = .001, adj. R2 = .40. Conclusion: Annual patient falls will decrease by increasing the number of nurses with baccalaureate degrees and/or certifications from a professional nursing board-governing body
Application of Natural Language Processing to Determine User Satisfaction in Public Services
Research on customer satisfaction has increased substantially in recent
years. However, the relative importance and relationships between different
determinants of satisfaction remains uncertain. Moreover, quantitative studies
to date tend to test for significance of pre-determined factors thought to have
an influence with no scalable means to identify other causes of user
satisfaction. The gaps in knowledge make it difficult to use available
knowledge on user preference for public service improvement. Meanwhile, digital
technology development has enabled new methods to collect user feedback, for
example through online forums where users can comment freely on their
experience. New tools are needed to analyze large volumes of such feedback. Use
of topic models is proposed as a feasible solution to aggregate open-ended user
opinions that can be easily deployed in the public sector. Generated insights
can contribute to a more inclusive decision-making process in public service
provision. This novel methodological approach is applied to a case of service
reviews of publicly-funded primary care practices in England. Findings from the
analysis of 145,000 reviews covering almost 7,700 primary care centers indicate
that the quality of interactions with staff and bureaucratic exigencies are the
key issues driving user satisfaction across England
The use of clinical, behavioral, and social determinants of health to improve identification of patients in need of advanced care for depression
Indiana University-Purdue University Indianapolis (IUPUI)Depression is the most commonly occurring mental illness the world over. It poses
a significant health and economic burden across the individual and community. Not all
occurrences of depression require the same level of treatment. However, identifying
patients in need of advanced care has been challenging and presents a significant bottleneck
in providing care. We developed a knowledge-driven depression taxonomy comprised of
features representing clinical, behavioral, and social determinants of health (SDH) that
inform the onset, progression, and outcome of depression. We leveraged the depression
taxonomy to build decision models that predicted need for referrals across: (a) the overall
patient population and (b) various high-risk populations. Decision models were built using
longitudinal, clinical, and behavioral data extracted from a population of 84,317 patients
seeking care at Eskenazi Health of Indianapolis, Indiana. Each decision model yielded
significantly high predictive performance. However, models predicting need of treatment
across high-risk populations (ROC’s of 86.31% to 94.42%) outperformed models
representing the overall patient population (ROC of 78.87%). Next, we assessed the value
of adding SDH into each model. For each patient population under study, we built
additional decision models that incorporated a wide range of patient and aggregate-level
SDH and compared their performance against the original models. Models that
incorporated SDH yielded high predictive performance. However, use of SDH did not yield
statistically significant performance improvements. Our efforts present significant
potential to identify patients in need of advanced care using a limited number of clinical
and behavioral features. However, we found no benefit to incorporating additional SDH
into these models. Our methods can also be applied across other datasets in response to a
wide variety of healthcare challenges
Natural Language Processing – Finding the Missing Link for Oncologic Data, 2022
Oncology like most medical specialties, is undergoing a data revolution at the center of which lie vast and growing amounts of clinical data in unstructured, semi-structured and structed formats. Artificial intelligence approaches are widely employed in research endeavors in an attempt to harness electronic medical records data to advance patient outcomes. The use of clinical oncologic data, although collected on large scale, particularly with the increased implementation of electronic medical records, remains limited due to missing, incorrect or manually entered data in registries and the lack of resource allocation to data curation in real world settings. Natural Language Processing (NLP) may provide an avenue to extract data from electronic medical records and as a result has grown considerably in medicine to be employed for documentation, outcome analysis, phenotyping and clinical trial eligibility. Barriers to NLP persist with inability to aggregate findings across studies due to use of different methods and significant heterogeneity at all levels with important parameters such as patient comorbidities and performance status lacking implementation in AI approaches. The goal of this review is to provide an updated overview of natural language processing (NLP) and the current state of its application in oncology for clinicians and researchers that wish to implement NLP to augment registries and/or advance research projects
Social and behavioral determinants of health in the era of artificial intelligence with electronic health records: A scoping review
Background: There is growing evidence that social and behavioral determinants
of health (SBDH) play a substantial effect in a wide range of health outcomes.
Electronic health records (EHRs) have been widely employed to conduct
observational studies in the age of artificial intelligence (AI). However,
there has been little research into how to make the most of SBDH information
from EHRs. Methods: A systematic search was conducted in six databases to find
relevant peer-reviewed publications that had recently been published. Relevance
was determined by screening and evaluating the articles. Based on selected
relevant studies, a methodological analysis of AI algorithms leveraging SBDH
information in EHR data was provided. Results: Our synthesis was driven by an
analysis of SBDH categories, the relationship between SBDH and
healthcare-related statuses, and several NLP approaches for extracting SDOH
from clinical literature. Discussion: The associations between SBDH and health
outcomes are complicated and diverse; several pathways may be involved. Using
Natural Language Processing (NLP) technology to support the extraction of SBDH
and other clinical ideas simplifies the identification and extraction of
essential concepts from clinical data, efficiently unlocks unstructured data,
and aids in the resolution of unstructured data-related issues. Conclusion:
Despite known associations between SBDH and disease, SBDH factors are rarely
investigated as interventions to improve patient outcomes. Gaining knowledge
about SBDH and how SBDH data can be collected from EHRs using NLP approaches
and predictive models improves the chances of influencing health policy change
for patient wellness, and ultimately promoting health and health equity.
Keywords: Social and Behavioral Determinants of Health, Artificial
Intelligence, Electronic Health Records, Natural Language Processing,
Predictive ModelComment: 32 pages, 5 figure
Predictive modeling of housing instability and homelessness in the Veterans Health Administration
OBJECTIVE: To develop and test predictive models of housing instability and homelessness based on responses to a brief screening instrument administered throughout the Veterans Health Administration (VHA).
DATA SOURCES/STUDY SETTING: Electronic medical record data from 5.8 million Veterans who responded to the VHA's Homelessness Screening Clinical Reminder (HSCR) between October 2012 and September 2015.
STUDY DESIGN: We randomly selected 80% of Veterans in our sample to develop predictive models. We evaluated the performance of both logistic regression and random forests—a machine learning algorithm—using the remaining 20% of cases.
DATA COLLECTION/EXTRACTION METHODS: Data were extracted from two sources: VHA's Corporate Data Warehouse and National Homeless Registry.
PRINCIPAL FINDINGS: Performance for all models was acceptable or better. Random forests models were more sensitive in predicting housing instability and homelessness than logistic regression, but less specific in predicting housing instability. Rates of positive screens for both outcomes were highest among Veterans in the top strata of model‐predicted risk.
CONCLUSIONS: Predictive models based on medical record data can identify Veterans likely to report housing instability and homelessness, making the HSCR screening process more efficient and informing new engagement strategies. Our findings have implications for similar instruments in other health care systems.U.S. Department of Veterans Affairs (VA) Health Services Research and Development (HSR&D), Grant/Award Number: IIR 13-334 (IIR 13-334 - U.S. Department of Veterans Affairs (VA) Health Services Research and Development (HSRD))Accepted manuscrip
State of Health Equity Movement, 2011 Update Part C: Compendium of Recommendations DRA Project Report No. 11-03
State of Health Equity Movement, 2011 Update
Part C: Compendium of Recommendations
DRA Project Report No. 11-0
Habituation to pain : a motivational-ethological perspective
Habituation to pain is mainly studied using external pain stimuli in healthy volunteers, often to identify the
underlying brain mechanisms, or to investigate problems in habituation in specific forms of pain (eg, migraine). Although these studies provide insight, they do not address one pertinent question: Why do we habituate to pain? Pain is a warning signal that urges us to react. Habituation to pain may thus be dysfunctional: It could make us unresponsive in situations where sensitivity and swift response to bodily damage are essential. Early theories of habituation were well aware of this argument. Sokolov argued that responding to pain should not decrease, but rather increase with repeated exposure, a phenomenon he called “sensitization.” His position makes intuitive sense: Why would individuals respond less to pain that inherently signals bodily harm? In this topical review, we address this question from a motivational ethological perspective. First, we describe some core characteristics of habituation. Second, we discuss theories that explain how and when habituation occurs. Third, we introduce a motivational-ethological perspective on habituation and explain why habituation occurs. Finally, we discuss how a focus on habituation to
pain introduces important methodological, theoretical, and clinical implications, otherwise overlooked
Associations Between Natural Language Processing (NLP) Enriched Social Determinants of Health and Suicide Death among US Veterans
Importance: Social determinants of health (SDOH) are known to be associated
with increased risk of suicidal behaviors, but few studies utilized SDOH from
unstructured electronic health record (EHR) notes.
Objective: To investigate associations between suicide and recent SDOH,
identified using structured and unstructured data.
Design: Nested case-control study.
Setting: EHR data from the US Veterans Health Administration (VHA).
Participants: 6,122,785 Veterans who received care in the US VHA between
October 1, 2010, and September 30, 2015.
Exposures: Occurrence of SDOH over a maximum span of two years compared with
no occurrence of SDOH.
Main Outcomes and Measures: Cases of suicide deaths were matched with 4
controls on birth year, cohort entry date, sex, and duration of follow-up. We
developed an NLP system to extract SDOH from unstructured notes. Structured
data, NLP on unstructured data, and combining them yielded six, eight and nine
SDOH respectively. Adjusted odds ratios (aORs) and 95% confidence intervals
(CIs) were estimated using conditional logistic regression.
Results: In our cohort, 8,821 Veterans committed suicide during 23,725,382
person-years of follow-up (incidence rate 37.18/100,000 person-years). Our
cohort was mostly male (92.23%) and white (76.99%). Across the five common SDOH
as covariates, NLP-extracted SDOH, on average, covered 80.03% of all SDOH
occurrences. All SDOH, measured by structured data and NLP, were significantly
associated with increased risk of suicide. The SDOH with the largest effects
was legal problems (aOR=2.66, 95% CI=.46-2.89), followed by violence (aOR=2.12,
95% CI=1.98-2.27). NLP-extracted and structured SDOH were also associated with
suicide.
Conclusions and Relevance: NLP-extracted SDOH were always significantly
associated with increased risk of suicide among Veterans, suggesting the
potential of NLP in public health studies.Comment: Submitted to JAMA Network Ope
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