Advancements in wearable sensor technologies and the digitization of medical records have contributed to the unprecedented ubiquity of biomedical time series data. Data-driven models have tremendous potential to assist clinical diagnosis and improve patient care by improving long-term monitoring capabilities, facilitating early disease detection and intervention, as well as promoting personalized healthcare delivery. However, accessing extensively labeled datasets to train data-hungry deep learning models encounters many barriers, such as long-tail distribution of rare diseases, cost of annotation, privacy and security concerns, data-sharing regulations, and ethical considerations. An emerging approach to overcome the scarcity of labeled data is to augment AI methods with human-like capabilities to leverage past experiences to learn new tasks with limited examples, called few-shot learning. This survey provides a comprehensive review and comparison of few-shot learning methods for biomedical time series applications. The clinical benefits and limitations of such methods are discussed in relation to traditional data-driven approaches. This paper aims to provide insights into the current landscape of few-shot learning for biomedical time series and its implications for future research and applications.
Deep neural language models have set new breakthroughs in many tasks of Natural Language Processing (NLP). Recent work has shown that deep transformer language models (pretrained on large amounts of texts) can achieve high levels of task-specific few-shot performance comparable to state-of-the-art models. However, the ability of these large language models in few-shot transfer learning has not yet been explored in the biomedical domain. We investigated the performance of two powerful transformer language models, i.e. GPT-3 and BioBERT, in few-shot settings on various biomedical NLP tasks. The …
Emory University（埃默里大学）；Georgia Institute of Technology（佐治亚理工学院）
Objective: Few-shot learning (FSL) methods require small numbers of labeled instances for training. As many medical topics have limited annotated textual data in practical settings, FSL-based natural language processing (NLP) methods hold substantial promise. We aimed to conduct a systematic review to explore the state of FSL methods for medical NLP. Materials and Methods: We searched for articles published between January 2016 and August 2021 using PubMed/Medline, Embase, ACL Anthology, and IEEE Xplore Digital Library. To identify the latest relevant methods, we also searched other sources su…
Recent few-shot learning works focus on training a model with prior meta-knowledge to fast adapt to new tasks with unseen classes and samples. However, conventional time-series classification algorithms fail to tackle the few-shot scenario. Existing few-shot learning methods are proposed to tackle image or text data, and most of them are neural-based models that lack interpretability. This paper proposes an interpretable neural-based framework, namely \textit{Dual Prototypical Shapelet Networks (DPSN)} for few-shot time-series classification, which not only trains a neural network-based model …
NTT Communication Science Laboratories（NTT通信科学实验室）；NTT Software Innovation Center（NTT软件创新中心）
Time-series forecasting is important for many applications. Forecasting models are usually trained using time-series data in a specific target task. However, sufficient data in the target task might be unavailable, which leads to performance degradation. In this paper, we propose a few-shot learning method that forecasts a future value of a time-series in a target task given a few time-series in the target task. Our model is trained using time-series data in multiple training tasks that are different from target tasks. Our model uses a few time-series to build a forecasting function based on a…
Aston University（阿斯顿大学）；University of Birmingham（伯明翰大学）；Massachusetts Institute of Technology（麻省理工学院）
We propose a robust framework for interpretable, few-shot analysis of non-stationary sequential data based on flexible graphical models to express the structured distribution of sequential events, using prototype radial basis function (RBF) neural network emissions. A motivational link is demonstrated between prototypical neural network architectures for few-shot learning and the proposed RBF network infinite hidden Markov model (RBF-iHMM). We show that RBF networks can be efficiently specified via prototypes allowing us to express complex nonstationary patterns, while hidden Markov models are…
Deakin University（迪肯大学）；University of South Carolina（南卡罗来纳大学）
Automatic analysis of biomedical time series such as electroencephalogram (EEG) and electrocardiographic (ECG) signals has attracted great interest in the community of biomedical engineering due to its important applications in medicine. In this work, a simple yet effective bag-of-words representation that is able to capture both local and global structure similarity information is proposed for biomedical time series representation. In particular, similar to the bag-of-words model used in text document domain, the proposed method treats a time series as a text document and extracts local segme…
A Zero-shot and Few-shot Study of Instruction-Finetuned Large Language Models Applied to Clinical and Biomedical Tasks
LIA, Avignon Université（阿维尼翁大学 LIA 实验室）；Zenidoc（Zenidoc 公司）；Nantes Université（南特大学）；École Centrale Nantes（南特中央理工学院）；CNRS（法国国家科学研究中心）；LS2N, UMR 6004（南特数字科学实验室）
LLMs are not Zero-Shot Reasoners for Biomedical Information Extraction
ASUS Intelligent Cloud Services (AICS)（ASUS智能云服务）；Imperial College London（伦敦帝国学院）；University of Manchester（曼彻斯特大学）；National University of Singapore（新加坡国立大学）
Multi-Scale and Multi-Modal Contrastive Learning Network for Biomedical Time Series
Shanghai Jiao Tong University（上海交通大学）；Key Laboratory for Thin Film and Microfabrication of Ministry of Education（教育部薄膜与微细加工重点实验室）；Guangdong JiuZhi Technology Co., Ltd（广东九智科技有限公司）；Shenzhen University（深圳大学）
BioFormer: Rethinking Cross-Subject Generalization via Spectral Structural Alignment in Biomedical Time-Series
College of Computer Science, Nankai University, Tianjin, China（南开大学计算机科学学院）；College of Cyber Science, Tianjin Key Laboratory of Interventional Brain-Computer Interface（天津介入脑机接口与智能康复重点实验室）；Intelligent Rehabilitation, Key Lab of Data（智能康复，数据实验室）；Intelligent System Security, Frontiers Science Center for New Organic Matter, Nankai University, Tianjin, China（智能系统安全，新有机物前沿科学中心，南开大学，天津，中国）
Forecasting-based Biomedical Time-series Data Synthesis for Open Data and Robust AI
School of Electrical Engineering, KAIST, Daejeon, Republic of Korea（韩国成均馆大学电气工程学院）；Department of Applied AI, Hansung University, Seoul, Republic of Korea（汉城市立大学应用人工智能系）
Benchmarking large language models for biomedical natural language processing applications and recommendations
Yale University（耶鲁大学）；National Institutes of Health（美国国立卫生研究院）；University of Texas Health Science at Houston（休斯顿德克萨斯大学健康科学中心）；University of Minnesota（明尼苏达大学）
Information Extraction (IE) from text refers to the task of extracting structured knowledge from unstructured text. The task typically consists of a series of sub-tasks such as Named Entity Recognition and Relation Extraction. Sourcing entity and relation type specific training data is a major bottleneck in domains with limited resources such as biomedicine. In this work we present a slot filling approach to the task of biomedical IE, effectively replacing the need for entity and relation-specific training data, allowing us to deal with zero-shot settings. We follow the recently proposed parad…
Institute of Medical Technology, Health Science Center of Peking University（北京大学医学部医学技术研究院）；National Institute of Health Data Science, Peking University（北京大学国家健康医疗大数据研究院）
Few-shot Time Series Classification (few-shot TSC) is a challenging problem in time series analysis. It is more difficult to classify when time series of the same class are not completely consistent in spectral domain or time series of different classes are partly consistent in spectral domain. To address this problem, we propose a novel method named Spectral Propagation Graph Network (SPGN) to explicitly model and propagate the spectrum-wise relations between different time series with graph network. To the best of our knowledge, SPGN is the first to utilize spectral comparisons in different …
Nowadays a diverse range of physiological data can be captured continuously for various applications in particular wellbeing and healthcare. Such data require efficient methods for classification and analysis. Deep learning algorithms have shown remarkable potential regarding such analyses, however, the use of these algorithms on low-power wearable devices is challenged by resource constraints such as area and power consumption. Most of the available on-chip deep learning processors contain complex and dense hardware architectures in order to achieve the highest possible throughput. Such a tre…
Tabular LLMs for Interpretable Few-Shot Alzheimer's Disease Prediction with Multimodal Biomedical Data
University of Pennsylvania（宾夕法尼亚大学）；Stony Brook University（石溪大学）；University of North Carolina at Chapel Hill（北卡罗来纳大学教堂山分校）；Medical University of South Carolina（南卡罗来纳医科大学）；Cedars-Sinai Medical Center（西格拉姆山医疗中心）
A recent advancement in the domain of biomedical Entity Linking is the development of powerful two-stage algorithms, an initial candidate retrieval stage that generates a shortlist of entities for each mention, followed by a candidate ranking stage. However, the effectiveness of both stages are inextricably dependent on computationally expensive components. Specifically, in candidate retrieval via dense representation retrieval it is important to have hard negative samples, which require repeated forward passes and nearest neighbour searches across the entire entity label set throughout traini…
Enabling Few-Shot Alzheimer's Disease Diagnosis on Biomarker Data with Tabular LLMs
University of Pennsylvania（宾夕法尼亚大学）；United States Naval Academy（美国海军学院）；University of North Carolina at Chapel Hill（北卡罗来纳大学教堂山分校）；Cedars Sinai Medical Center（西好莱坞 Cedars Sinai 医院）
A Novel Data Augmentation Strategy for Robust Deep Learning Classification of Biomedical Time-Series Data: Application to ECG and EEG Analysis
Computer Science Department, College of Science, University of Zakho（扎赫大学科学学院计算机科学系）；Department of Computer Science and Information Technology, The American University of Kurdistan（库尔德斯坦美国大学计算机科学与信息技术系）；PRIME Lab, Scientific Research Center, University of Zakho（扎赫大学科学研究中心PRIME实验室）
Learning complex time series forecasting models usually requires a large amount of data, as each model is trained from scratch for each task/data set. Leveraging learning experience with similar datasets is a well-established technique for classification problems called few-shot classification. However, existing approaches cannot be applied to time-series forecasting because i) multivariate time-series datasets have different channels and ii) forecasting is principally different from classification. In this paper we formalize the problem of few-shot forecasting of time-series with heterogeneou…
University of Oxford（牛津大学）；Mind Foundry Ltd.（思维铸造有限公司）
Recurrent neural networks (RNNs) are state-of-the-art in several sequential learning tasks, but they often require considerable amounts of data to generalise well. For many time series forecasting (TSF) tasks, only a few dozens of observations may be available at training time, which restricts use of this class of models. We propose a novel RNN-based model that directly addresses this problem by learning a shared feature embedding over the space of many quantised time series. We show how this enables our RNN framework to accurately and reliably forecast unseen time series, even when there is l…
Autoregressive models for biomedical signal processing
St Vincent’s Hospital（圣文森特医院）；The University of Melbourne（墨尔本大学）；Federation University Australia（澳大利亚联邦大学）；University of Melbourne（墨尔本大学）；Graeme Clark Institute（格雷姆·克拉克研究所）
One of the main problems with biomedical signals is the limited amount of patient-specific data and the significant amount of time needed to record the sufficient number of samples needed for diagnostic and treatment purposes. In this study, we present a framework to simultaneously generate and classify biomedical time series based on a modified Adversarial Autoencoder (AAE) algorithm and one-dimensional convolutions. Our work is based on breathing time series, with specific motivation to capture breathing motion during radiotherapy lung cancer treatments. First, we explore the potential in us…
Prediction and Causality of functional MRI and synthetic signal using a Zero-Shot Time-Series Foundation Model
AGH University of Krakow, Poland（克拉科夫AGH大学）；Institut de Neurosciences de la Timone UMR 7289, Aix Marseille Université, CNRS, 13005, Marseille, France（里莫内神经科学研究所）
