The ability to perform accurate prognosis of patients is crucial for proactive clinical decision making, informed resource management and personalised care. Existing outcome prediction models suffer from a low recall of infrequent positive outcomes. We present a highly-scalable and robust machine learning framework to automatically predict adversity represented by mortality and ICU admission from time-series vital signs and laboratory results obtained within the first 24 hours of hospital admission. The stacked platform comprises two components: a) an unsupervised LSTM Autoencoder that learns an optimal representation of the time-series, using it to differentiate the less frequent patterns which conclude with an adverse event from the majority patterns that do not, and b) a gradient boosting model, which relies on the constructed representation to refine prediction, incorporating static features of demographics, admission details and clinical summaries. The model is used to assess a patient's risk of adversity over time and provides visual justifications of its prediction based on the patient's static features and dynamic signals. Results of three case studies for predicting mortality and ICU admission show that the model outperforms all existing outcome prediction models, achieving PR-AUC of 0.891 (95$%$ CI: 0.878 - 0.969) in predicting mortality in ICU and general ward settings and 0.908 (95$%$ CI: 0.870-0.935) in predicting ICU admission.
Optimizing YOLOv5s Object Detection through Knowledge Distillation algorithm
University of Chicago（芝加哥大学）；University of Michigan（密歇根大学）；Johns Hopkins University（约翰斯·霍普金斯大学）；Shanghai Jiao Tong University（上海交通大学）；Tulane University（杜兰大学）；Northeastern University（东北大学）
Cross-Modal Representational Knowledge Distillation for Enhanced Spike-Informed LFP Modeling
Ming Hsieh Department of Electrical and Computer Engineering（明希斯电气与计算机工程系）；Thomas Lord Department of Computer Science（托马斯·劳德计算机科学系）；Alfred E. Mann Department of Biomedical Engineering（阿尔弗雷德·E·曼生物医学工程系）；Neuroscience Graduate Program University of Southern California（神经科学研究生项目美国南加州大学）
Scalable Clinical Data Infrastructure and Comparative ML Evaluation for Hospitalisation Risk Prediction in Elderly Patients with Multiple Long-Term Conditions using CPRD
School of Information, University of Sheffield（谢菲尔德大学信息学院）；School of Medicine, Faculty of Medicine and Health, University of Leeds（利兹大学医学与健康学院医学院）；Division of Informatics, University of Manchester（曼彻斯特大学信息学系）；Academic Unit for Ageing & Stroke Research, University of Leeds（利兹大学衰老与中风研究学术单元）；Institute of Population Health, University of Liverpool（利物浦大学人口健康研究所）
Deep Medicine, University of Oxford（牛津大学Deep Medicine（深度医学））；University of Oxford（牛津大学）
Recent evidence shows that deep learning models trained on electronic health records from millions of patients can deliver substantially more accurate predictions of risk compared to their statistical counterparts. While this provides an important opportunity for improving clinical decision-making, the lack of interpretability is a major barrier to the incorporation of these black-box models in routine care, limiting their trustworthiness and preventing further hypothesis-testing investigations. In this study, we propose two methods, namely, model distillation and variable selection, to untang…
Institute of Advanced Technology, Westlake Institute for Advanced Study（西湖高等研究院先进技术研究院）
Objective. Deep neural networks (DNNs) have shown unprecedented success in various brain-machine interface applications such as epileptic seizure prediction. However, existing approaches typically train models in a patient-specific fashion due to the highly personalized characteristics of epileptic signals. Therefore, only a limited number of labeled recordings from each subject can be used for training. As a consequence, current DNN based methods demonstrate poor generalization ability to some extent due to the insufficiency of training data. On the other hand, patient-independent models atte…
Electronic health records (EHR) are increasingly being used for constructing disease risk prediction models. Feature engineering in EHR data however is challenging due to their highly dimensional and heterogeneous nature. Low-dimensional representations of EHR data can potentially mitigate these challenges. In this paper, we use global vectors (GloVe) to learn word embeddings for diagnoses and procedures recorded using 13 million ontology terms across 2.7 million hospitalisations in national UK EHR. We demonstrate the utility of these embeddings by evaluating their performance in identifying p…
University of Southern California（南加州大学）；Children’s Hospital Los Angeles（洛杉矶儿童医院）
Exponential growth in Electronic Healthcare Records (EHR) has resulted in new opportunities and urgent needs for discovery of meaningful data-driven representations and patterns of diseases in Computational Phenotyping research. Deep Learning models have shown superior performance for robust prediction in computational phenotyping tasks, but suffer from the issue of model interpretability which is crucial for clinicians involved in decision-making. In this paper, we introduce a novel knowledge-distillation approach called Interpretable Mimic Learning, to learn interpretable phenotype features …
In forensic applications, it is very common that only small naturalistic datasets consisting of short utterances in complex or unknown acoustic environments are available. In this study, we propose a pipeline solution to improve speaker verification on a small actual forensic field dataset. By leveraging large-scale out-of-domain datasets, a knowledge distillation based objective function is proposed for teacher-student learning, which is applied for short utterance forensic speaker verification. The objective function collectively considers speaker classification loss, Kullback-Leibler diverg…
JointDistill: Adaptive Multi-Task Distillation for Joint Depth Estimation and Scene Segmentation
Northwestern Polytechnical University（北华理工大学）；Hong Kong University of Science and Technology (Guangzhou)（香港理工大学（广州））；National University of Singapore（新加坡国立大学）；Harbin Engineering University（哈尔滨工程大学）
End-to-End task-oriented dialogue systems generate responses based on dialog history and an accompanying knowledge base (KB). Inferring those KB entities that are most relevant for an utterance is crucial for response generation. Existing state of the art scales to large KBs by softly filtering over irrelevant KB information. In this paper, we propose a novel filtering technique that consists of (1) a pairwise similarity based filter that identifies relevant information by respecting the n-ary structure in a KB record. and, (2) an auxiliary loss that helps in separating contextually unrelated …
Peking University（北京大学）；Lancaster University（兰卡斯特大学）；Peking University Third Hospital（北京大学第三医院）
Due to the characteristics of COVID-19, the epidemic develops rapidly and overwhelms health service systems worldwide. Many patients suffer from systemic life-threatening problems and need to be carefully monitored in ICUs. Thus the intelligent prognosis is in an urgent need to assist physicians to take an early intervention, prevent the adverse outcome, and optimize the medical resource allocation. However, in the early stage of the epidemic outbreak, the data available for analysis is limited due to the lack of effective diagnostic mechanisms, rarity of the cases, and privacy concerns. In th…
Automated Batch Distillation Process Simulation for a Large Hybrid Dataset for Deep Anomaly Detection
Fraunhofer Institute for Industrial Mathematics (ITWM)（弗劳恩霍夫工业数学研究所）；Laboratory of Engineering Thermodynamics (LTD)（工程热力学实验室）；RPTU Kaiserslautern（科隆大学（卡塞尔拉特恩分校））
Foresight-England: Development of a National-Scale Generative AI Model of Electronic Health Records for Medical Event Prediction across the COVID-19 Pandemic
University College London（伦敦大学学院）；King’s College London（伦敦国王学院）；University College London Hospitals National Institute for Health Research Biomedical Research Centre（伦敦大学学院医院国家卫生研究院生物医学研究中心）；Interdisciplinary Transformation University（跨学科转型大学）；British Heart Foundation Data Science Centre（英国心脏基金会数据科学中心）；Health Data Research UK（英国健康数据研究中心）；The University of Edinburgh（爱丁堡大学）；University of Cambridge（剑桥大学）；Victor Phillip Dahdaleh Heart and Lung Research Institute, University of Cambridge（剑桥大学维克多·菲利普·达德赫心肺研究所）；British Heart Foundation Centre of Research Excellence, University of Cambridge（剑桥大学英国心脏基金会卓越研究中心）
In realistic speech enhancement settings for end-user devices, we often encounter only a few speakers and noise types that tend to reoccur in the specific acoustic environment. We propose a novel personalized speech enhancement method to adapt a compact denoising model to the test-time specificity. Our goal in this test-time adaptation is to utilize no clean speech target of the test speaker, thus fulfilling the requirement for zero-shot learning. To complement the lack of clean utterance, we employ the knowledge distillation framework. Instead of the missing clean utterance target, we distill…
Batch Distillation Data for Developing Machine Learning Anomaly Detection Methods
Laboratory of Engineering Thermodynamics, RPTU Kaiserslautern（工程热力学实验室，RPTU凯泽斯劳滕）；Technical University of Munich, Campus Straubing for Biotechnology and Sustainability, Laboratory for Chemical Process Engineering（慕尼黑工业大学，施特劳宾生物技术与可持续发展校区，化学过程工程实验室）；Department of Computer Science, RPTU Kaiserslautern（计算机科学系，RPTU凯泽斯劳滕）
QualityFM: a Multimodal Physiological Signal Foundation Model with Self-Distillation for Signal Quality Challenges in Critically Ill Patients
Department of Electronics, Information and Bioengineering, Politecnico di Milano（电子、信息与生物工程系，米兰理工学院）；State Key Laboratory of Industrial Control Technology, Zhejiang University（工业控制技术国家重点实验室，浙江大学）
Equitable Length of Stay Prediction for Patients with Learning Disabilities and Multiple Long-term Conditions Using Machine Learning
Loughborough University（拉夫堡大学）；Swansea University Medical School（斯旺西大学医学院）；University of Leicester（莱斯特大学）；Leicestershire Partnership NHS Trust（莱斯特郡合作 NHS 信托机构）
The University of Texas Health Science Center at Houston（德克萨斯大学休斯顿健康科学中心）；Harvard Medical School（哈佛医学院）；Weill Cornell Medicine, Cornell University（康奈尔大学威尔康奈尔医学院）
Patient representation learning refers to learning a dense mathematical representation of a patient that encodes meaningful information from Electronic Health Records (EHRs). This is generally performed using advanced deep learning methods. This study presents a systematic review of this field and provides both qualitative and quantitative analyses from a methodological perspective. We identified studies developing patient representations from EHRs with deep learning methods from MEDLINE, EMBASE, Scopus, the Association for Computing Machinery (ACM) Digital Library, and Institute of Electrical…
Towards Collaborative Fairness in Federated Learning Under Imbalanced Covariate Shift
The Pennsylvania State University（宾夕法尼亚州立大学）；State University of New York at Albany（纽约州立大学阿尔巴尼分校）；University of Minnesota Twin Cities（明尼苏达大学双城分校）；Dalian University of Technology（大连理工大学）
