Browsing by Subject "Preventive care"

Now showing 1 - 3 of 3
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    Biomedical concept association and clustering using word embeddings
    (2018-12) Shah, Setu; Luo, Xiao; El-Sharkawy, Mohamed; King, Brian
    Biomedical data exists in the form of journal articles, research studies, electronic health records, care guidelines, etc. While text mining and natural language processing tools have been widely employed across various domains, these are just taking off in the healthcare space. A primary hurdle that makes it difficult to build artificial intelligence models that use biomedical data, is the limited amount of labelled data available. Since most models rely on supervised or semi-supervised methods, generating large amounts of pre-processed labelled data that can be used for training purposes becomes extremely costly. Even for datasets that are labelled, the lack of normalization of biomedical concepts further affects the quality of results produced and limits the application to a restricted dataset. This affects reproducibility of the results and techniques across datasets, making it difficult to deploy research solutions to improve healthcare services. The research presented in this thesis focuses on reducing the need to create labels for biomedical text mining by using unsupervised recurrent neural networks. The proposed method utilizes word embeddings to generate vector representations of biomedical concepts based on semantics and context. Experiments with unsupervised clustering of these biomedical concepts show that concepts that are similar to each other are clustered together. While this clustering captures different synonyms of the same concept, it also captures the similarities between various diseases and the symptoms that those diseases are symptomatic of. To test the performance of the concept vectors on corpora of documents, a document vector generation method that utilizes these concept vectors is also proposed. The document vectors thus generated are used as an input to clustering algorithms, and the results show that across multiple corpora, the proposed methods of concept and document vector generation outperform the baselines and provide more meaningful clustering. The applications of this document clustering are huge, especially in the search and retrieval space, providing clinicians, researchers and patients more holistic and comprehensive results than relying on the exclusive term that they search for. At the end, a framework for extracting clinical information that can be mapped to electronic health records from preventive care guidelines is presented. The extracted information can be integrated with the clinical decision support system of an electronic health record. A visualization tool to better understand and observe patient trajectories is also explored. Both these methods have potential to improve the preventive care services provided to patients.
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    Perspectives of family medicine physicians on the importance of adolescent preventive care: a multivariate analysis
    (BioMed Central, 2016) Taylor, Jaime L.; Aalsma, Matthew C.; Gilbert, Amy L.; Hensel, Devon J.; Rickert, Vaughn I.; Department of Pediatrics, IU School of Medicine
    BACKGROUND: The study objective was to identify commonalities amongst family medicine physicians who endorse annual adolescent visits. METHODS: A nationally weighted representative on-line survey was used to explore pediatrician (N = 204) and family medicine physicians (N = 221) beliefs and behaviors surrounding adolescent wellness. Our primary outcome was endorsement that adolescents should receive annual preventive care visits. RESULTS: Pediatricians were significantly more likely (p < .01) to endorse annual well visits. Among family medicine physicians, bivariate comparisons were conducted between those who endorsed an annual visit (N = 164) compared to those who did not (N = 57) with significant predictors combined into two multivariate logistic regression models. Model 1 controlled for: patient race, proportion of 13-17 year olds in provider's practice, discussion beliefs scale and discussion behaviors with parents scale. Model 2 controlled for the same first three variables as well as discussion behaviors with adolescents scale. Model 1 showed for each discussion beliefs scale topic selected, family medicine physicians had 1.14 increased odds of endorsing annual visits (p < .001) and had 1.11 greater odds of endorsing annual visits with each one-point increase in discussion behaviors with parents scale (p = .51). Model 2 showed for each discussion beliefs scale topic selected, family medicine physicians had 1.15 increased odds of also endorsing the importance of annual visits (p < .001). CONCLUSIONS: Family medicine physicians that endorse annual visits are significantly more likely to affirm they hold strong beliefs about topics that should be discussed during the annual exam. They also act on these beliefs by talking to parents of teens about these topics. This group appears to focus on quality of care in thought and deed.
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    Zero-shot learning to extract assessment criteria and medical services from the preventive healthcare guidelines using large language models
    (Oxford University Press, 2024) Luo, Xiao; Tahabi, Fattah Muhammad; Marc, Tressica; Haunert, Laura Ann; Storey, Susan; Biostatistics and Health Data Science, Richard M. Fairbanks School of Public Health
    Objectives: The integration of these preventive guidelines with Electronic Health Records (EHRs) systems, coupled with the generation of personalized preventive care recommendations, holds significant potential for improving healthcare outcomes. Our study investigates the feasibility of using Large Language Models (LLMs) to automate the assessment criteria and risk factors from the guidelines for future analysis against medical records in EHR. Materials and methods: We annotated the criteria, risk factors, and preventive medical services described in the adult guidelines published by United States Preventive Services Taskforce and evaluated 3 state-of-the-art LLMs on extracting information in these categories from the guidelines automatically. Results: We included 24 guidelines in this study. The LLMs can automate the extraction of all criteria, risk factors, and medical services from 9 guidelines. All 3 LLMs perform well on extracting information regarding the demographic criteria or risk factors. Some LLMs perform better on extracting the social determinants of health, family history, and preventive counseling services than the others. Discussion: While LLMs demonstrate the capability to handle lengthy preventive care guidelines, several challenges persist, including constraints related to the maximum length of input tokens and the tendency to generate content rather than adhering strictly to the original input. Moreover, the utilization of LLMs in real-world clinical settings necessitates careful ethical consideration. It is imperative that healthcare professionals meticulously validate the extracted information to mitigate biases, ensure completeness, and maintain accuracy. Conclusion: We developed a data structure to store the annotated preventive guidelines and make it publicly available. Employing state-of-the-art LLMs to extract preventive care criteria, risk factors, and preventive care services paves the way for the future integration of these guidelines into the EHR.