9,775 research outputs found
Gene Expression based Survival Prediction for Cancer Patients: A Topic Modeling Approach
Cancer is one of the leading cause of death, worldwide. Many believe that
genomic data will enable us to better predict the survival time of these
patients, which will lead to better, more personalized treatment options and
patient care. As standard survival prediction models have a hard time coping
with the high-dimensionality of such gene expression (GE) data, many projects
use some dimensionality reduction techniques to overcome this hurdle. We
introduce a novel methodology, inspired by topic modeling from the natural
language domain, to derive expressive features from the high-dimensional GE
data. There, a document is represented as a mixture over a relatively small
number of topics, where each topic corresponds to a distribution over the
words; here, to accommodate the heterogeneity of a patient's cancer, we
represent each patient (~document) as a mixture over cancer-topics, where each
cancer-topic is a mixture over GE values (~words). This required some
extensions to the standard LDA model eg: to accommodate the "real-valued"
expression values - leading to our novel "discretized" Latent Dirichlet
Allocation (dLDA) procedure. We initially focus on the METABRIC dataset, which
describes breast cancer patients using the r=49,576 GE values, from
microarrays. Our results show that our approach provides survival estimates
that are more accurate than standard models, in terms of the standard
Concordance measure. We then validate this approach by running it on the
Pan-kidney (KIPAN) dataset, over r=15,529 GE values - here using the mRNAseq
modality - and find that it again achieves excellent results. In both cases, we
also show that the resulting model is calibrated, using the recent
"D-calibrated" measure. These successes, in two different cancer types and
expression modalities, demonstrates the generality, and the effectiveness, of
this approach
Probabilistic Clustering of Time-Evolving Distance Data
We present a novel probabilistic clustering model for objects that are
represented via pairwise distances and observed at different time points. The
proposed method utilizes the information given by adjacent time points to find
the underlying cluster structure and obtain a smooth cluster evolution. This
approach allows the number of objects and clusters to differ at every time
point, and no identification on the identities of the objects is needed.
Further, the model does not require the number of clusters being specified in
advance -- they are instead determined automatically using a Dirichlet process
prior. We validate our model on synthetic data showing that the proposed method
is more accurate than state-of-the-art clustering methods. Finally, we use our
dynamic clustering model to analyze and illustrate the evolution of brain
cancer patients over time
Mining and Integration of Structured and Unstructured Electronic Clinical Data for Dementia Detection
Dementia is an increasing problem for the aging population that incurs high medical costs, in part due to the lack of available treatment options. Accordingly, early detection is critical to potentially postpone symptoms and to prepare both healthcare providers and families for a patient\u27s management needs. Current detection methods are typically costly or unreliable, and could greatly benefit from improved recognition of early dementia markers. Identification of such markers may be possible through computational analysis of patients\u27 electronic clinical records. Prior work on has focused on structured data (e.g. test results), but these records often also contain natural language (text) data in the form of patient histories, visit summaries, or other notes, which may be valuable for disease prediction. This thesis has three main goals: to incorporate analysis of the aforementioned electronic medical texts into predictive models of dementia development, to explore the use of topic modeling as a form of interpretable dimensionality reduction to improve prediction and to characterize the texts, and to integrate these models with ones using structured data. This kind of computational modeling could be used in an automated screening system to identify and flag potentially problematic patients for assessment by clinicians. Results support the potential for unstructured clinical text data both as standalone predictors of dementia status when structured data are missing, and as complements to structured data
Triaging Content Severity in Online Mental Health Forums
Mental health forums are online communities where people express their issues
and seek help from moderators and other users. In such forums, there are often
posts with severe content indicating that the user is in acute distress and
there is a risk of attempted self-harm. Moderators need to respond to these
severe posts in a timely manner to prevent potential self-harm. However, the
large volume of daily posted content makes it difficult for the moderators to
locate and respond to these critical posts. We present a framework for triaging
user content into four severity categories which are defined based on
indications of self-harm ideation. Our models are based on a feature-rich
classification framework which includes lexical, psycholinguistic, contextual
and topic modeling features. Our approaches improve the state of the art in
triaging the content severity in mental health forums by large margins (up to
17% improvement over the F-1 scores). Using the proposed model, we analyze the
mental state of users and we show that overall, long-term users of the forum
demonstrate a decreased severity of risk over time. Our analysis on the
interaction of the moderators with the users further indicates that without an
automatic way to identify critical content, it is indeed challenging for the
moderators to provide timely response to the users in need.Comment: Accepted for publication in Journal of the Association for
Information Science and Technology (2017
Linking social media, medical literature, and clinical notes using deep learning.
Researchers analyze data, information, and knowledge through many sources, formats, and methods. The dominant data format includes text and images. In the healthcare industry, professionals generate a large quantity of unstructured data. The complexity of this data and the lack of computational power causes delays in analysis. However, with emerging deep learning algorithms and access to computational powers such as graphics processing unit (GPU) and tensor processing units (TPUs), processing text and images is becoming more accessible. Deep learning algorithms achieve remarkable results in natural language processing (NLP) and computer vision. In this study, we focus on NLP in the healthcare industry and collect data not only from electronic medical records (EMRs) but also medical literature and social media. We propose a framework for linking social media, medical literature, and EMRs clinical notes using deep learning algorithms. Connecting data sources requires defining a link between them, and our key is finding concepts in the medical text. The National Library of Medicine (NLM) introduces a Unified Medical Language System (UMLS) and we use this system as the foundation of our own system. We recognize social media’s dynamic nature and apply supervised and semi-supervised methodologies to generate concepts. Named entity recognition (NER) allows efficient extraction of information, or entities, from medical literature, and we extend the model to process the EMRs’ clinical notes via transfer learning. The results include an integrated, end-to-end, web-based system solution that unifies social media, literature, and clinical notes, and improves access to medical knowledge for the public and experts
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