1,721,281 research outputs found
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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
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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
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
Immune modulators in disease: integrating knowledge from the biomedical literature and gene expression
Full text linkCytokines play a central role in both health and disease, modulating immune responses and acting as diagnostic markers and therapeutic targets. This work takes a systems-level approach for integration and examination of immune patterns, such as cytokine gene expression with information from biomedical literature, and applies it in the context of disease, with the objective of identifying potentially useful relationships and areas for future research.We present herein the integration and analysis of immune-related knowledge, namely, information derived from biomedical literature and gene expression arrays. Cytokine-disease associations were captured from over 2.4 million PubMed records, in the form of Medical Subject Headings descriptor co-occurrences, as well as from gene expression arrays. Clustering of cytokine-disease co-occurrences from biomedical literature is shown to reflect current medical knowledge as well as potentially novel relationships between diseases. A correlation analysis of cytokine gene expression in a variety of diseases revealed compelling relationships. Finally, a novel analysis comparing cytokine gene expression in different diseases to parallel associations captured from the biomedical literature was used to examine which associations are interesting for further investigation.We demonstrate the usefulness of capturing Medical Subject Headings descriptor co-occurrences from biomedical publications in the generation of valid and potentially useful hypotheses. Furthermore, integrating and comparing descriptor co-occurrences with gene expression data was shown to be useful in detecting new, potentially fruitful, and unaddressed areas of research.Using integrated large-scale data captured from the scientific literature and experimental data, a better understanding of the immune mechanisms underlying disease can be achieved and applied to research
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
Exploring genomic medicine using integrative biology
Full text linkThesis (Ph. D.)--Harvard-MIT Division of Health Sciences and Technology, 2004.Includes bibliographical references (p. 215-227).Instead of focusing on the cell, or the genotype, or on any single measurement modality, using integrative biology allows us to think holistically and horizontally. A disease like diabetes can lead to myocardial infarction, nephropathy, and neuropathy; to study diabetes in genomic medicine would require reasoning from a disease to all its various complications to the genome and back. I am studying the process of intersecting nearly-comprehensive data sets in molecular biology, across three representative modalities (microarrays, RNAi and quantitative trait loci) out of the more than 30 available today. This is difficult because the semantics and context of each experiment performed becomes more important, necessitating a detailed knowledge about the biological domain. I addressed this problem by using all public microarray data from NIH, unifying 50 million expression measurements with standard gene identifiers and representing the experimental context of each using the Unified Medical Language System, a vocabulary of over 1 million concepts. I created an automated system to join data sets related by experimental context.(cont.) I evaluated this system by finding genes significantly involved in multiple experiments directly and indirectly related to diabetes and adipogenesis and found genes known to be involved in these diseases and processes. As a model first step into integrative biology, I then took known quantitative trait loci in the rat involved in glucose metabolism and build an expert system to explain possible biological mechanisms for these genetic data using the modeled genomic data. The system I have created can link diseases from the ICD-9 billing code level down to the genetic, genomic, and molecular level. In a sense, this is the first automated system built to study the new field of genomic medicine.by Atul Janardhan Butte.Ph.D
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