1,721,342 research outputs found
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Deep Learning Across Healthcare Spectrums: Genomic Insights, Social Determinants Analysis, and Imaging Diagnostics in Complex Diseases
Full text linkThe burgeoning interest in leveraging deep learning within the medical field heralds a promising frontier for enhancing disease understanding and patient care. Yet, this technological advance is not without its challenges. One significant issue is the underutilization of diverse data types; medical records and biological factors, while crucial, do not encompass the entirety of necessary information. Social Determinants of Health (SDoH), for instance, play a pivotal role in disease comprehension but are often neglected in research. Furthermore, while deep learning holds potential for diagnosis and aiding clinical decisions, the absence of rigorous external validation undermines its reliability. Many models, despite performing well in initial settings, falter under broader, real-world scrutiny. Additionally, the tendency to harness large datasets and maximize feature inclusion for disease analysis sometimes overshadows the value of engineered features. These more targeted, hypothesis-driven attributes can sometimes offer clearer insights into disease mechanisms, a nuance that is frequently overlooked in the rush towards big data approaches.These challenges manifest distinctly across different data modalities in medical research. In the realm of Electronic Health Records (EHR), the exploration of disease mechanisms often prioritizes medical data, inadvertently sidelining non-medical but equally vital Social Determinants of Health (SDoH) such as financial stability, mental health, and physical activity. This oversight can skew our understanding of disease etiology and patient outcomes. In medical imaging, the rapid development and deployment of deep learning models boast of enhanced diagnostic accuracy. Yet, this domain is particularly susceptible to the pitfalls of insufficient external validation. Minor perturbations or "noise" within the imaging data can dramatically compromise the predictive reliability of these models, emphasizing the need for robust validation processes. Genomic studies, on the other hand, face the challenge of signal dilution amidst the vast array of genomic features. The pursuit of correlations across tens of thousands of genes often overlooks the critical influence of covariates and noise, potentially obscuring the true biological signals vital for understanding disease processes. Each of these issues highlights the complexity of medical data analysis and the need for nuanced approaches that consider the full spectrum of relevant factors.This dissertation is dedicated to the development and application of innovative computational strategies, employing practical deep learning techniques to address these prevailing challenges. Firstly, it underscores the necessity of integrating comprehensive and meaningful features in deep learning research, with a particular emphasis on the inclusion of Social Determinants of Health (SDoH) factors, to present a more holistic view of disease mechanisms. Secondly, it demonstrates the imperative role of high-quality data, coupled with human feedback and rigorous external validation, in enhancing the reliability and applicability of deep learning frameworks within the medical domain. Thirdly, the dissertation advocates for the strategic use of high-level feature engineering, as opposed to relying on an overwhelming volume of features, to decipher complex biological systems
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Uncovering strategies for personalized treatment selection using large language models
Full text linkHealthcare data has never been so accessible to patients and physicians, from smartphones and other remote monitoring devices to improved access for patients to their own Electronic Medical Record (EMR) history and clinical notes. Despite the ubiquity of healthcare data collection and distribution, there remains a significant gap in understanding the impacts of this data on clinical care. Insights from these digital health tools and downstream clinical decision-making processes are often only captured in medical notes, which are complex, sparse, unstructured, and difficult to model even with traditional deep learning methods. Only recently have large language models (LLMs) emerged that are capable of zero- or few-shot clinical language, without the need for large, manually annotated datasets. In this dissertation, I develop methods to apply LLMs to healthcare data, particularly for identifying points of actionable insights for both digital and pharmaceutical therapeutics. These approaches demonstrate the ways in which digital health products and passive monitoring can impact clinical care, and identify reasons for medication class switching that take into account the complexities of patient care beyond lab values and diagnosis codes. While careful, rigorous research is needed to ensure that these approaches are effective in facilitating patient care improvements and to reduce any potential for harm, the rapid pace of language model development provides an extraordinary opportunity to transform clinical practice. These new methods allow us to take an unprecedented look at the conversations, decisions, and medical expertise captured in billions of clinical notes and other clinical text, and to learn from this shared knowledge to accelerate medical research, improve clinical guidelines, and personalize patient care
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DEEP LEARNING IN PERSONALIZED MEDICINE: APPLICATIONS IN PATIENT SIMILARITY, PROGNOSIS, AND OPTIMAL TREATMENT SELECTION
Full text linkTwo information technology revolutions are colliding in medicine. The first revolution has been the digitalization of health data, specifically Electronic Health Records (EHR). These records contain the details of who we are as patients, our ailments, treatments, and outcomes. Tragically, despite billions of dollars in investment from the US government, hardly any of this data is being utilized to better understand medicine or improve healthcare. This is largely because the data is voluminous, sparse, complex, and poorly formatted; making it unsuitable for traditional analytics methods. However the second revolution, modern Artificial Intelligence, specifically deep learning, provides tools, in the form of algorithms, to address exactly these problems. The primary difference between these modern algorithms and older ones is that the former are able to learn, more or less on their own, how to transform large complex data into a format that makes it easier to use and learn from. In this dissertation, I have developed methods to apply deep learning to digital health data. Doing so, I have shown that we can predict the future health of individual patients with highly complex diseases, produced approaches to understand and leverage what these complex models are learning, and provided a framework for how healthcare systems of the near future could automatically learn to improve care daily. For the first time in history, we are in a position to learn from the combined knowledge of tens of thousands of physicians and their experiences caring for hundreds of millions of patients. The potential transformations to healthcare are difficult to fully fathom, but certainly include safer, more powerful and efficient medicine, and a rapid speed up in new medical discoveries and treatments. Despite the promise, we must proceed carefully, balancing the great need to collectively use our data for better medicine with the individual right to privacy
Mutual information relevance networks : functional genomic networks built from pair-wise entropy measurements
No full textThis electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Thesis (S.M.)--Harvard--Massachusetts Institute of Technology Division of Health Sciences and Technology, 2002.Includes bibliographical references (leaves 27-28).by Atul Janardhan Butte.Thesis (S.M.)--Harvard--Massachusetts Institute of Technology Division of Health Sciences and Technology, 2002
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
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Investigating Disparities in Treatment Quality and Decision-Making in the UC Health System
Full text linkSocial Determinants of Health (SDOH) are non-medical factors including socioeconomic status, chronic disease burden, and mental health, that significantly influence healthcare access and outcomes. While these factors are important for understanding disease risk and disparities, they remain underrepresented in Electronic Health Records (EHRs). EHRs primarily capture relevant clinical data such as diagnoses, medications, and lab results. However, integrating SDOH offers a transformative approach to understanding health outcomes, disease risk, and healthcare disparities, particularly those shaped by socioeconomic and environmental conditions, through computational research and artificial intelligence.This dissertation highlights the importance of incorporating SDOH into real-world evidence (RWE) studies to advance research on disease risk and health outcomes. Improvements in EHR infrastructure, including diagnosis codes, wearable technology, and census tract information, allow for deeper insights into health disparities. This work examines the challenges and opportunities related to integrating SDOH into EHRs, explores their growing role in RWE, and identifies pathways for equitable and actionable reporting of study outcomes.Using de-identified data from the University of California Health Data Warehouse, this dissertation applies statistical modeling, machine learning, and generative artificial intelligence to investigate treatment disparities in two conditions: Type 2 diabetes and Multiple Myeloma. In the diabetes study, patients with lower socioeconomic status were more likely to receive less optimal second-line therapies, highlighting disparities in treatment pathways. In the Multiple Myeloma study, access to CAR-T therapy was significantly associated with treatment location and patient race and ethnicity, highlighting barriers to innovative therapies for diverse population groups.This dissertation further examines the impact of SDOH on treatment decisions, unmet clinical needs, and barriers to equitable care. By integrating SDOH metrics with EHR data, this work informs data-driven strategies to improve access to advanced therapies and address healthcare disparities. These findings aim is to inform policies that ensure all patient populations have fair and consistent access to advanced and innovative medical treatments
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
Full text linkWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
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