14,575 research outputs found
Using Clinical Notes with Time Series Data for ICU Management
Monitoring patients in ICU is a challenging and high-cost task. Hence,
predicting the condition of patients during their ICU stay can help provide
better acute care and plan the hospital's resources. There has been continuous
progress in machine learning research for ICU management, and most of this work
has focused on using time series signals recorded by ICU instruments. In our
work, we show that adding clinical notes as another modality improves the
performance of the model for three benchmark tasks: in-hospital mortality
prediction, modeling decompensation, and length of stay forecasting that play
an important role in ICU management. While the time-series data is measured at
regular intervals, doctor notes are charted at irregular times, making it
challenging to model them together. We propose a method to model them jointly,
achieving considerable improvement across benchmark tasks over baseline
time-series model. Our implementation can be found at
\url{https://github.com/kaggarwal/ClinicalNotesICU}.Comment: Accepted at EMNLP 201
A Survey on Deep Learning in Medical Image Analysis
Deep learning algorithms, in particular convolutional networks, have rapidly
become a methodology of choice for analyzing medical images. This paper reviews
the major deep learning concepts pertinent to medical image analysis and
summarizes over 300 contributions to the field, most of which appeared in the
last year. We survey the use of deep learning for image classification, object
detection, segmentation, registration, and other tasks and provide concise
overviews of studies per application area. Open challenges and directions for
future research are discussed.Comment: Revised survey includes expanded discussion section and reworked
introductory section on common deep architectures. Added missed papers from
before Feb 1st 201
Artificial Intelligence for In Silico Clinical Trials: A Review
A clinical trial is an essential step in drug development, which is often
costly and time-consuming. In silico trials are clinical trials conducted
digitally through simulation and modeling as an alternative to traditional
clinical trials. AI-enabled in silico trials can increase the case group size
by creating virtual cohorts as controls. In addition, it also enables
automation and optimization of trial design and predicts the trial success
rate. This article systematically reviews papers under three main topics:
clinical simulation, individualized predictive modeling, and computer-aided
trial design. We focus on how machine learning (ML) may be applied in these
applications. In particular, we present the machine learning problem
formulation and available data sources for each task. We end with discussing
the challenges and opportunities of AI for in silico trials in real-world
applications
DuETT: Dual Event Time Transformer for Electronic Health Records
Electronic health records (EHRs) recorded in hospital settings typically
contain a wide range of numeric time series data that is characterized by high
sparsity and irregular observations. Effective modelling for such data must
exploit its time series nature, the semantic relationship between different
types of observations, and information in the sparsity structure of the data.
Self-supervised Transformers have shown outstanding performance in a variety of
structured tasks in NLP and computer vision. But multivariate time series data
contains structured relationships over two dimensions: time and recorded event
type, and straightforward applications of Transformers to time series data do
not leverage this distinct structure. The quadratic scaling of self-attention
layers can also significantly limit the input sequence length without
appropriate input engineering. We introduce the DuETT architecture, an
extension of Transformers designed to attend over both time and event type
dimensions, yielding robust representations from EHR data. DuETT uses an
aggregated input where sparse time series are transformed into a regular
sequence with fixed length; this lowers the computational complexity relative
to previous EHR Transformer models and, more importantly, enables the use of
larger and deeper neural networks. When trained with self-supervised prediction
tasks, that provide rich and informative signals for model pre-training, our
model outperforms state-of-the-art deep learning models on multiple downstream
tasks from the MIMIC-IV and PhysioNet-2012 EHR datasets.Comment: Accepted at MLHC 2023, camera-ready versio
Attentive Dual Embedding for Understanding Medical Concept in Electronic Health Record
Electronic health records contain a wealth of information on a patient’s healthcare over many visits, such as diagnoses, treatments, drugs administered, and so on. The untapped potential of these data in healthcare analytics is vast. However, given that much of medical information is a cause and effect science, new embedding methods are required to ensure the learning representations reflect the comprehensive interplays between medical concepts and their relationships over time. Unlike one-hot encoding, a distributed representation should preserve these complex interactions as high-quality inputs for machine learning-based healthcare analytics tasks. Therefore, we propose a novel attentive dual embedding method called MC2Vec. MC2Vec captures the proximity relationships between medical concepts through a two-step optimization framework that recursively refines the embedding for superior output. The framework comprises a Skip-gram model to generate the initial embedding and an attentive CBOW model to fine-tune the embedding with temporal information gleaned from sequences of patient visits. Experiments with two public datasets demonstrate that MC2Vec’s produces embeddings of higher quality than five state-of-the-art methods
Preserving the knowledge of long clinical texts using aggregated ensembles of large language models
Clinical texts, such as admission notes, discharge summaries, and progress
notes, contain rich and valuable information that can be used for various
clinical outcome prediction tasks. However, applying large language models,
such as BERT-based models, to clinical texts poses two major challenges: the
limitation of input length and the diversity of data sources. This paper
proposes a novel method to preserve the knowledge of long clinical texts using
aggregated ensembles of large language models. Unlike previous studies which
use model ensembling or text aggregation methods separately, we combine
ensemble learning with text aggregation and train multiple large language
models on two clinical outcome tasks: mortality prediction and length of stay
prediction. We show that our method can achieve better results than baselines,
ensembling, and aggregation individually, and can improve the performance of
large language models while handling long inputs and diverse datasets. We
conduct extensive experiments on the admission notes from the MIMIC-III
clinical database by combining multiple unstructured and high-dimensional
datasets, demonstrating our method's effectiveness and superiority over
existing approaches. We also provide a comprehensive analysis and discussion of
our results, highlighting our method's applications and limitations for future
research in the domain of clinical healthcare. The results and analysis of this
study is supportive of our method assisting in clinical healthcare systems by
enabling clinical decision-making with robust performance overcoming the
challenges of long text inputs and varied datasets.Comment: 17 pages, 4 figures, 4 tables, 9 equations and 1 algorith
Is attention all you need in medical image analysis? A review
Medical imaging is a key component in clinical diagnosis, treatment planning
and clinical trial design, accounting for almost 90% of all healthcare data.
CNNs achieved performance gains in medical image analysis (MIA) over the last
years. CNNs can efficiently model local pixel interactions and be trained on
small-scale MI data. The main disadvantage of typical CNN models is that they
ignore global pixel relationships within images, which limits their
generalisation ability to understand out-of-distribution data with different
'global' information. The recent progress of Artificial Intelligence gave rise
to Transformers, which can learn global relationships from data. However, full
Transformer models need to be trained on large-scale data and involve
tremendous computational complexity. Attention and Transformer compartments
(Transf/Attention) which can well maintain properties for modelling global
relationships, have been proposed as lighter alternatives of full Transformers.
Recently, there is an increasing trend to co-pollinate complementary
local-global properties from CNN and Transf/Attention architectures, which led
to a new era of hybrid models. The past years have witnessed substantial growth
in hybrid CNN-Transf/Attention models across diverse MIA problems. In this
systematic review, we survey existing hybrid CNN-Transf/Attention models,
review and unravel key architectural designs, analyse breakthroughs, and
evaluate current and future opportunities as well as challenges. We also
introduced a comprehensive analysis framework on generalisation opportunities
of scientific and clinical impact, based on which new data-driven domain
generalisation and adaptation methods can be stimulated
Deep Learning in Breast Cancer Imaging: A Decade of Progress and Future Directions
Breast cancer has reached the highest incidence rate worldwide among all
malignancies since 2020. Breast imaging plays a significant role in early
diagnosis and intervention to improve the outcome of breast cancer patients. In
the past decade, deep learning has shown remarkable progress in breast cancer
imaging analysis, holding great promise in interpreting the rich information
and complex context of breast imaging modalities. Considering the rapid
improvement in the deep learning technology and the increasing severity of
breast cancer, it is critical to summarize past progress and identify future
challenges to be addressed. In this paper, we provide an extensive survey of
deep learning-based breast cancer imaging research, covering studies on
mammogram, ultrasound, magnetic resonance imaging, and digital pathology images
over the past decade. The major deep learning methods, publicly available
datasets, and applications on imaging-based screening, diagnosis, treatment
response prediction, and prognosis are described in detail. Drawn from the
findings of this survey, we present a comprehensive discussion of the
challenges and potential avenues for future research in deep learning-based
breast cancer imaging.Comment: Survey, 41 page
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