207 research outputs found
Deepr: A Convolutional Net for Medical Records
Feature engineering remains a major bottleneck when creating predictive
systems from electronic medical records. At present, an important missing
element is detecting predictive regular clinical motifs from irregular episodic
records. We present Deepr (short for Deep record), a new end-to-end deep
learning system that learns to extract features from medical records and
predicts future risk automatically. Deepr transforms a record into a sequence
of discrete elements separated by coded time gaps and hospital transfers. On
top of the sequence is a convolutional neural net that detects and combines
predictive local clinical motifs to stratify the risk. Deepr permits
transparent inspection and visualization of its inner working. We validate
Deepr on hospital data to predict unplanned readmission after discharge. Deepr
achieves superior accuracy compared to traditional techniques, detects
meaningful clinical motifs, and uncovers the underlying structure of the
disease and intervention space
Advancing Biomedicine with Graph Representation Learning: Recent Progress, Challenges, and Future Directions
Graph representation learning (GRL) has emerged as a pivotal field that has
contributed significantly to breakthroughs in various fields, including
biomedicine. The objective of this survey is to review the latest advancements
in GRL methods and their applications in the biomedical field. We also
highlight key challenges currently faced by GRL and outline potential
directions for future research.Comment: Accepted by 2023 IMIA Yearbook of Medical Informatic
Graph-Driven Generative Models for Heterogeneous Multi-Task Learning
We propose a novel graph-driven generative model, that unifies multiple
heterogeneous learning tasks into the same framework. The proposed model is
based on the fact that heterogeneous learning tasks, which correspond to
different generative processes, often rely on data with a shared graph
structure. Accordingly, our model combines a graph convolutional network (GCN)
with multiple variational autoencoders, thus embedding the nodes of the graph
i.e., samples for the tasks) in a uniform manner while specializing their
organization and usage to different tasks. With a focus on healthcare
applications (tasks), including clinical topic modeling, procedure
recommendation and admission-type prediction, we demonstrate that our method
successfully leverages information across different tasks, boosting performance
in all tasks and outperforming existing state-of-the-art approaches.Comment: Accepted by AAAI-202
Modern Views of Machine Learning for Precision Psychiatry
In light of the NIMH's Research Domain Criteria (RDoC), the advent of
functional neuroimaging, novel technologies and methods provide new
opportunities to develop precise and personalized prognosis and diagnosis of
mental disorders. Machine learning (ML) and artificial intelligence (AI)
technologies are playing an increasingly critical role in the new era of
precision psychiatry. Combining ML/AI with neuromodulation technologies can
potentially provide explainable solutions in clinical practice and effective
therapeutic treatment. Advanced wearable and mobile technologies also call for
the new role of ML/AI for digital phenotyping in mobile mental health. In this
review, we provide a comprehensive review of the ML methodologies and
applications by combining neuroimaging, neuromodulation, and advanced mobile
technologies in psychiatry practice. Additionally, we review the role of ML in
molecular phenotyping and cross-species biomarker identification in precision
psychiatry. We further discuss explainable AI (XAI) and causality testing in a
closed-human-in-the-loop manner, and highlight the ML potential in multimedia
information extraction and multimodal data fusion. Finally, we discuss
conceptual and practical challenges in precision psychiatry and highlight ML
opportunities in future research
Graph Representation Learning in Biomedicine
Biomedical networks are universal descriptors of systems of interacting
elements, from protein interactions to disease networks, all the way to
healthcare systems and scientific knowledge. With the remarkable success of
representation learning in providing powerful predictions and insights, we have
witnessed a rapid expansion of representation learning techniques into
modeling, analyzing, and learning with such networks. In this review, we put
forward an observation that long-standing principles of networks in biology and
medicine -- while often unspoken in machine learning research -- can provide
the conceptual grounding for representation learning, explain its current
successes and limitations, and inform future advances. We synthesize a spectrum
of algorithmic approaches that, at their core, leverage graph topology to embed
networks into compact vector spaces, and capture the breadth of ways in which
representation learning is proving useful. Areas of profound impact include
identifying variants underlying complex traits, disentangling behaviors of
single cells and their effects on health, assisting in diagnosis and treatment
of patients, and developing safe and effective medicines
Bidirectional Representation Learning from Transformers using Multimodal Electronic Health Record Data to Predict Depression
Advancements in machine learning algorithms have had a beneficial impact on
representation learning, classification, and prediction models built using
electronic health record (EHR) data. Effort has been put both on increasing
models' overall performance as well as improving their interpretability,
particularly regarding the decision-making process. In this study, we present a
temporal deep learning model to perform bidirectional representation learning
on EHR sequences with a transformer architecture to predict future diagnosis of
depression. This model is able to aggregate five heterogenous and
high-dimensional data sources from the EHR and process them in a temporal
manner for chronic disease prediction at various prediction windows. We applied
the current trend of pretraining and fine-tuning on EHR data to outperform the
current state-of-the-art in chronic disease prediction, and to demonstrate the
underlying relation between EHR codes in the sequence. The model generated the
highest increases of precision-recall area under the curve (PRAUC) from 0.70 to
0.76 in depression prediction compared to the best baseline model. Furthermore,
the self-attention weights in each sequence quantitatively demonstrated the
inner relationship between various codes, which improved the model's
interpretability. These results demonstrate the model's ability to utilize
heterogeneous EHR data to predict depression while achieving high accuracy and
interpretability, which may facilitate constructing clinical decision support
systems in the future for chronic disease screening and early detection.Comment: in IEEE Journal of Biomedical and Health Informatics (2021
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