39,977 research outputs found
Semi-Supervised Approach to Monitoring Clinical Depressive Symptoms in Social Media
With the rise of social media, millions of people are routinely expressing
their moods, feelings, and daily struggles with mental health issues on social
media platforms like Twitter. Unlike traditional observational cohort studies
conducted through questionnaires and self-reported surveys, we explore the
reliable detection of clinical depression from tweets obtained unobtrusively.
Based on the analysis of tweets crawled from users with self-reported
depressive symptoms in their Twitter profiles, we demonstrate the potential for
detecting clinical depression symptoms which emulate the PHQ-9 questionnaire
clinicians use today. Our study uses a semi-supervised statistical model to
evaluate how the duration of these symptoms and their expression on Twitter (in
terms of word usage patterns and topical preferences) align with the medical
findings reported via the PHQ-9. Our proactive and automatic screening tool is
able to identify clinical depressive symptoms with an accuracy of 68% and
precision of 72%.Comment: 8 pages, Advances in Social Networks Analysis and Mining (ASONAM),
2017 IEEE/ACM International Conferenc
Depression and Self-Harm Risk Assessment in Online Forums
Users suffering from mental health conditions often turn to online resources
for support, including specialized online support communities or general
communities such as Twitter and Reddit. In this work, we present a neural
framework for supporting and studying users in both types of communities. We
propose methods for identifying posts in support communities that may indicate
a risk of self-harm, and demonstrate that our approach outperforms strong
previously proposed methods for identifying such posts. Self-harm is closely
related to depression, which makes identifying depressed users on general
forums a crucial related task. We introduce a large-scale general forum dataset
("RSDD") consisting of users with self-reported depression diagnoses matched
with control users. We show how our method can be applied to effectively
identify depressed users from their use of language alone. We demonstrate that
our method outperforms strong baselines on this general forum dataset.Comment: Expanded version of EMNLP17 paper. Added sections 6.1, 6.2, 6.4,
FastText baseline, and CNN-
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
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