1,804 research outputs found
Risk Prediction of a Multiple Sclerosis Diagnosis
Multiple sclerosis (MS) is a chronic autoimmune disease that affects the
central nervous system. The progression and severity of MS varies by
individual, but it is generally a disabling disease. Although medications have
been developed to slow the disease progression and help manage symptoms, MS
research has yet to result in a cure. Early diagnosis and treatment of the
disease have been shown to be effective at slowing the development of
disabilities. However, early MS diagnosis is difficult because symptoms are
intermittent and shared with other diseases. Thus most previous works have
focused on uncovering the risk factors associated with MS and predicting the
progression of disease after a diagnosis rather than disease prediction. This
paper investigates the use of data available in electronic medical records
(EMRs) to create a risk prediction model; thereby helping clinicians perform
the difficult task of diagnosing an MS patient. Our results demonstrate that
even given a limited time window of patient data, one can achieve reasonable
classification with an area under the receiver operating characteristic curve
of 0.724. By restricting our features to common EMR components, the developed
models also generalize to other healthcare systems
PGB: A PubMed Graph Benchmark for Heterogeneous Network Representation Learning
There has been a rapid growth in biomedical literature, yet capturing the
heterogeneity of the bibliographic information of these articles remains
relatively understudied. Although graph mining research via heterogeneous graph
neural networks has taken center stage, it remains unclear whether these
approaches capture the heterogeneity of the PubMed database, a vast digital
repository containing over 33 million articles. We introduce PubMed Graph
Benchmark (PGB), a new benchmark dataset for evaluating heterogeneous graph
embeddings for biomedical literature. PGB is one of the largest heterogeneous
networks to date and consists of 30 million English articles. The benchmark
contains rich metadata including abstract, authors, citations, MeSH terms, MeSH
hierarchy, and some other information. The benchmark contains three different
evaluation tasks encompassing systematic reviews, node classification, and node
clustering. In PGB, we aggregate the metadata associated with the biomedical
articles from PubMed into a unified source and make the benchmark publicly
available for any future works
Waking up dormant tumors
As appreciation grows for the contribution of the tumor microenvironment to the progression of cancer, new evidence accumulates to support that the participation of stromal cells can extend beyond the local environment. Recently, Elkabets and colleagues demonstrated a systemic interaction between cancer cells and distant bone marrow cells to support the growth of otherwise indolent tumor cells at a secondary site, raising thought-provoking questions regarding the involvement of stromal cells in maintaining metastatic dormancy.National Institutes of Health (U.S.) (NIH grant CA125550)National Institutes of Health (U.S.) (NIH grant CA155370)National Institutes of Health (U.S.) (NIH grant CA151925)National Institutes of Health (U.S.) (NIH grant DK081576)United States. Dept. of Defense (Breast Cancer Research Program Predoctoral Traineeship Award
LogicPrpBank: A Corpus for Logical Implication and Equivalence
Logic reasoning has been critically needed in problem-solving and
decision-making. Although Language Models (LMs) have demonstrated capabilities
of handling multiple reasoning tasks (e.g., commonsense reasoning), their
ability to reason complex mathematical problems, specifically propositional
logic, remains largely underexplored. This lack of exploration can be
attributed to the limited availability of annotated corpora. Here, we present a
well-labeled propositional logic corpus, LogicPrpBank, containing 7093
Propositional Logic Statements (PLSs) across six mathematical subjects, to
study a brand-new task of reasoning logical implication and equivalence. We
benchmark LogicPrpBank with widely-used LMs to show that our corpus offers a
useful resource for this challenging task and there is ample room for model
improvement.Comment: In the 5th AI4ED Workshop, held in conjunction with The 38th AAAI
Conference on Artificial Intelligence, February 202
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