Self-Supervised contrastive learning has emerged as a powerful method for obtaining high-quality representations from unlabeled data. However, feature suppression has recently been identified in standard contrastive learning ($e.g.$, SimCLR, CLIP): in a single end-to-end training stage, the contrastive model captures only parts of the shared information across contrasting views, while ignore the other potentially useful information. With feature suppression, contrastive models often fail to learn sufficient representations capable for various downstream tasks. To mitigate the feature suppression problem and ensure the contrastive model to learn comprehensive representations, we develop a novel Multistage Contrastive Learning (MCL) framework. Unlike standard contrastive learning that often result in feature suppression, MCL progressively learn new features that have not been explored in the previous stage, while maintaining the well-learned features. Extensive experiments conducted on various publicly available benchmarks validate the effectiveness of our proposed framework. In addition, we demonstrate that the proposed MCL can be adapted to a variety of popular contrastive learning backbones and boost their performance by learning features that could not be gained from standard contrastive learning procedures.
The utilization of large language models (LLMs) in the Healthcare domain has generated both excitement and concern due to their ability to effectively respond to freetext queries with certain professional knowledge. This survey outlines the capabilities of the currently developed LLMs for Healthcare and explicates their development process, with the aim of providing an overview of the development roadmap from traditional Pretrained Language Models (PLMs) to LLMs. Specifically, we first explore the potential of LLMs to enhance the efficiency and effectiveness of various Healthcare applications highlighting both the strengths and limitations. Secondly, we conduct a comparison between the previous PLMs and the latest LLMs, as well as comparing various LLMs with each other. Then we summarize related Healthcare training data, training methods, optimization strategies, and usage. Finally, the unique concerns associated with deploying LLMs in Healthcare settings are investigated, particularly regarding fairness, accountability, transparency and ethics. Our survey provide a comprehensive investigation from perspectives of both computer science and Healthcare specialty. Besides the discussion about Healthcare concerns, we supports the computer science community by compiling a collection of open source resources, such as accessible datasets, the latest methodologies, code implementations, and evaluation benchmarks in the Github. Summarily, we contend that a significant paradigm shift is underway, transitioning from PLMs to LLMs. This shift encompasses a move from discriminative AI approaches to generative AI approaches, as well as a shift from model-centered methodologies to datacentered methodologies.
In recent years, estimating the duration of medical intervention based on electronic health records (EHRs) has gained significant attention in the filed of clinical decision support. However, current models largely focus on structured data, leaving out information from the unstructured clinical free-text data. To address this, we present a novel language-enhanced transformer-based framework, which projects all relevant clinical data modalities (continuous, categorical, binary, and free-text features) into a harmonized language latent space using a pre-trained sentence encoder with the help of medical prompts. The proposed method enables the integration of information from different modalities within the cell transformer encoder and leads to more accurate duration estimation for medical intervention. Our experimental results on both US-based (length of stay in ICU estimation) and Asian (surgical duration prediction) medical datasets demonstrate the effectiveness of our proposed framework, which outperforms tailored baseline approaches and exhibits robustness to data corruption in EHRs.
Not all positive pairs are beneficial to time series contrastive learning. In this paper, we study two types of bad positive pairs that impair the quality of time series representation learned through contrastive learning ($i.e.$, noisy positive pair and faulty positive pair). We show that, with the presence of noisy positive pairs, the model tends to simply learn the pattern of noise (Noisy Alignment). Meanwhile, when faulty positive pairs arise, the model spends considerable efforts aligning non-representative patterns (Faulty Alignment). To address this problem, we propose a Dynamic Bad Pair Mining (DBPM) algorithm, which reliably identifies and suppresses bad positive pairs in time series contrastive learning. DBPM utilizes a memory module to track the training behavior of each positive pair along training process. This allows us to identify potential bad positive pairs at each epoch based on their historical training behaviors. The identified bad pairs are then down-weighted using a transformation module. Our experimental results show that DBPM effectively mitigates the negative impacts of bad pairs, and can be easily used as a plug-in to boost performance of state-of-the-art methods. Codes will be made publicly available.
Learning information-rich and generalizable representations effectively from unlabeled multivariate cardiac signals to identify abnormal heart rhythms (cardiac arrhythmias) is valuable in real-world clinical settings but often challenging due to its complex temporal dynamics. Cardiac arrhythmias can vary significantly in temporal patterns even for the same patient ($i.e.$, intra subject difference). Meanwhile, the same type of cardiac arrhythmia can show different temporal patterns among different patients due to different cardiac structures ($i.e.$, inter subject difference). In this paper, we address the challenges by proposing an Intra-inter Subject self-supervised Learning (ISL) model that is customized for multivariate cardiac signals. Our proposed ISL model integrates medical knowledge into self-supervision to effectively learn from intra-inter subject differences. In intra subject self-supervision, ISL model first extracts heartbeat-level features from each subject using a channel-wise attentional CNN-RNN encoder. Then a stationarity test module is employed to capture the temporal dependencies between heartbeats. In inter subject self-supervision, we design a set of data augmentations according to the clinical characteristics of cardiac signals and perform contrastive learning among subjects to learn distinctive representations for various types of patients. Extensive experiments on three real-world datasets were conducted. In a semi-supervised transfer learning scenario, our pre-trained ISL model leads about 10% improvement over supervised training when only 1% labeled data is available, suggesting strong generalizability and robustness of the model.
Cardiovascular disease is a major threat to health and one of the primary causes of death globally. The 12-lead ECG is a cheap and commonly accessible tool to identify cardiac abnormalities. Early and accurate diagnosis will allow early treatment and intervention to prevent severe complications of cardiovascular disease. In the PhysioNet/Computing in Cardiology Challenge 2020, our objective is to develop an algorithm that automatically identifies 27 ECG abnormalities from 12-lead ECG recordings.