45 research outputs found
BoXHED2.0: Scalable boosting of dynamic survival analysis
Modern applications of survival analysis increasingly involve time-dependent
covariates. In healthcare settings, such covariates provide dynamic patient
histories that can be used to assess health risks in realtime by tracking the
hazard function. Hazard learning is thus particularly useful in healthcare
analytics, and the open-source package BoXHED 1.0 provides the first
implementation of a gradient boosted hazard estimator that is fully
nonparametric. This paper introduces BoXHED 2.0, a quantum leap over BoXHED 1.0
in several ways. Crucially, BoXHED 2.0 can deal with survival data that goes
far beyond right-censoring and it also supports recurring events. To our
knowledge, this is the only nonparametric machine learning implementation that
is able to do so. Another major improvement is that BoXHED 2.0 is orders of
magnitude more scalable, due in part to a novel data preprocessing step that
sidesteps the need for explicit quadrature when dealing with time-dependent
covariates. BoXHED 2.0 supports the use of GPUs and multicore CPUs, and is
available from GitHub: www.github.com/BoXHED.Comment: 12 page
Can smartwatches replace smartphones for posture tracking?
This paper introduces a human posture tracking platform to identify the human postures of sitting, standing or lying down, based on a smartwatch. This work develops such a system as a proof-of-concept study to investigate a smartwatch's ability to be used in future remote health monitoring systems and applications. This work validates the smartwatches' ability to track the posture of users accurately in a laboratory setting while reducing the sampling rate to potentially improve battery life, the first steps in verifying that such a system would work in future clinical settings. The algorithm developed classifies the transitions between three posture states of sitting, standing and lying down, by identifying these transition movements, as well as other movements that might be mistaken for these transitions. The system is trained and developed on a Samsung Galaxy Gear smartwatch, and the algorithm was validated through a leave-one-subject-out cross-validation of 20 subjects. The system can identify the appropriate transitions at only 10 Hz with an F-score of 0.930, indicating its ability to effectively replace smart phones, if needed
Self-supervised contrastive learning of echocardiogram videos enables label-efficient cardiac disease diagnosis
Advances in self-supervised learning (SSL) have shown that self-supervised
pretraining on medical imaging data can provide a strong initialization for
downstream supervised classification and segmentation. Given the difficulty of
obtaining expert labels for medical image recognition tasks, such an
"in-domain" SSL initialization is often desirable due to its improved label
efficiency over standard transfer learning. However, most efforts toward SSL of
medical imaging data are not adapted to video-based medical imaging modalities.
With this progress in mind, we developed a self-supervised contrastive learning
approach, EchoCLR, catered to echocardiogram videos with the goal of learning
strong representations for efficient fine-tuning on downstream cardiac disease
diagnosis. EchoCLR leverages (i) distinct videos of the same patient as
positive pairs for contrastive learning and (ii) a frame re-ordering pretext
task to enforce temporal coherence. When fine-tuned on small portions of
labeled data (as few as 51 exams), EchoCLR pretraining significantly improved
classification performance for left ventricular hypertrophy (LVH) and aortic
stenosis (AS) over other transfer learning and SSL approaches across internal
and external test sets. For example, when fine-tuning on 10% of available
training data (519 studies), an EchoCLR-pretrained model achieved 0.72 AUROC
(95% CI: [0.69, 0.75]) on LVH classification, compared to 0.61 AUROC (95% CI:
[0.57, 0.64]) with a standard transfer learning approach. Similarly, using 1%
of available training data (53 studies), EchoCLR pretraining achieved 0.82
AUROC (95% CI: [0.79, 0.84]) on severe AS classification, compared to 0.61
AUROC (95% CI: [0.58, 0.65]) with transfer learning. EchoCLR is unique in its
ability to learn representations of medical videos and demonstrates that SSL
can enable label-efficient disease classification from small, labeled datasets
DynImp: Dynamic Imputation for Wearable Sensing Data Through Sensory and Temporal Relatedness
In wearable sensing applications, data is inevitable to be irregularly
sampled or partially missing, which pose challenges for any downstream
application. An unique aspect of wearable data is that it is time-series data
and each channel can be correlated to another one, such as x, y, z axis of
accelerometer. We argue that traditional methods have rarely made use of both
times-series dynamics of the data as well as the relatedness of the features
from different sensors. We propose a model, termed as DynImp, to handle
different time point's missingness with nearest neighbors along feature axis
and then feeding the data into a LSTM-based denoising autoencoder which can
reconstruct missingness along the time axis. We experiment the model on the
extreme missingness scenario ( missing rate) which has not been widely
tested in wearable data. Our experiments on activity recognition show that the
method can exploit the multi-modality features from related sensors and also
learn from history time-series dynamics to reconstruct the data under extreme
missingness.Comment: 5 pages, 2 figures, accepted in ICASSP'202