107,316 research outputs found
Using Machine Learning and Natural Language Processing to Review and Classify the Medical Literature on Cancer Susceptibility Genes
PURPOSE: The medical literature relevant to germline genetics is growing
exponentially. Clinicians need tools monitoring and prioritizing the literature
to understand the clinical implications of the pathogenic genetic variants. We
developed and evaluated two machine learning models to classify abstracts as
relevant to the penetrance (risk of cancer for germline mutation carriers) or
prevalence of germline genetic mutations. METHODS: We conducted literature
searches in PubMed and retrieved paper titles and abstracts to create an
annotated dataset for training and evaluating the two machine learning
classification models. Our first model is a support vector machine (SVM) which
learns a linear decision rule based on the bag-of-ngrams representation of each
title and abstract. Our second model is a convolutional neural network (CNN)
which learns a complex nonlinear decision rule based on the raw title and
abstract. We evaluated the performance of the two models on the classification
of papers as relevant to penetrance or prevalence. RESULTS: For penetrance
classification, we annotated 3740 paper titles and abstracts and used 60% for
training the model, 20% for tuning the model, and 20% for evaluating the model.
The SVM model achieves 89.53% accuracy (percentage of papers that were
correctly classified) while the CNN model achieves 88.95 % accuracy. For
prevalence classification, we annotated 3753 paper titles and abstracts. The
SVM model achieves 89.14% accuracy while the CNN model achieves 89.13 %
accuracy. CONCLUSION: Our models achieve high accuracy in classifying abstracts
as relevant to penetrance or prevalence. By facilitating literature review,
this tool could help clinicians and researchers keep abreast of the burgeoning
knowledge of gene-cancer associations and keep the knowledge bases for clinical
decision support tools up to date
One-Class Classification: Taxonomy of Study and Review of Techniques
One-class classification (OCC) algorithms aim to build classification models
when the negative class is either absent, poorly sampled or not well defined.
This unique situation constrains the learning of efficient classifiers by
defining class boundary just with the knowledge of positive class. The OCC
problem has been considered and applied under many research themes, such as
outlier/novelty detection and concept learning. In this paper we present a
unified view of the general problem of OCC by presenting a taxonomy of study
for OCC problems, which is based on the availability of training data,
algorithms used and the application domains applied. We further delve into each
of the categories of the proposed taxonomy and present a comprehensive
literature review of the OCC algorithms, techniques and methodologies with a
focus on their significance, limitations and applications. We conclude our
paper by discussing some open research problems in the field of OCC and present
our vision for future research.Comment: 24 pages + 11 pages of references, 8 figure
Fall Prediction and Prevention Systems: Recent Trends, Challenges, and Future Research Directions.
Fall prediction is a multifaceted problem that involves complex interactions between physiological, behavioral, and environmental factors. Existing fall detection and prediction systems mainly focus on physiological factors such as gait, vision, and cognition, and do not address the multifactorial nature of falls. In addition, these systems lack efficient user interfaces and feedback for preventing future falls. Recent advances in internet of things (IoT) and mobile technologies offer ample opportunities for integrating contextual information about patient behavior and environment along with physiological health data for predicting falls. This article reviews the state-of-the-art in fall detection and prediction systems. It also describes the challenges, limitations, and future directions in the design and implementation of effective fall prediction and prevention systems
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