Early prediction of patients at risk of clinical deterioration can help physicians intervene and alter their clinical course towards better outcomes. In addition to the accuracy requirement, early warning systems must make the predictions early enough to give physicians enough time to intervene. Interpretability is also one of the challenges when building such systems since being able to justify the reasoning behind model decisions is desirable in clinical practice. In this work, we built an interpretable model for the early prediction of various adverse clinical events indicative of clinical deterioration. The model is evaluated on two datasets and four clinical events. The first dataset is collected in a predominantly COVID-19 positive population at Stony Brook Hospital. The second dataset is the MIMIC III dataset. The model was trained to provide early warning scores for ventilation, ICU transfer, and mortality prediction tasks on the Stony Brook Hospital dataset and to predict mortality and the need for vasopressors on the MIMIC III dataset. Our model first separates each feature into multiple ranges and then uses logistic regression with lasso penalization to select the subset of ranges for each feature. The model training is completely automated and doesn't require expert knowledge like other early warning scores. We compare our model to the Modified Early Warning Score (MEWS) and quick SOFA (qSOFA), commonly used in hospitals. We show that our model outperforms these models in the area under the receiver operating characteristic curve (AUROC) while having a similar or better median detection time on all clinical events, even when using fewer features. Unlike MEWS and qSOFA, our model can be entirely automated without requiring any manually recorded features. We also show that discretization improves model performance by comparing our model to a baseline logistic regression model.
Clinically Interpretable Sepsis Early Warning via LLM-Guided Simulation of Temporal Physiological Dynamics
School of Electrical and Information Engineering, Tianjin University（天津大学电气与信息工程学院）；Department of Information Engineering and Computer Science, University of Trento（特伦托大学信息工程与计算机科学系）；Department of Orthopedics, Affiliated Kunshan Hospital of Jiangsu University（江苏大学附属昆山医院骨科部）；Tianjin University Chest Hospital（天津大学胸科医院）；Haihe Hospital, Tianjin University（天津大学海河医院）；Key Laboratory of Education Blockchain and Intelligent Technology, Ministry of Education, Guangxi Normal University（广西师范大学教育区块链与智能技术重点实验室）
Robô Laura；University of São Paulo（圣保罗大学）；Pontifical Catholic University of Parana（巴拉那天主教大学）；Federal University of Parana（巴拉那联邦大学）
Early recognition of clinical deterioration (CD) has vital importance in patients' survival from exacerbation or death. Electronic health records (EHRs) data have been widely employed in Early Warning Scores (EWS) to measure CD risk in hospitalized patients. Recently, EHRs data have been utilized in Machine Learning (ML) models to predict mortality and CD. The ML models have shown superior performance in CD prediction compared to EWS. Since EHRs data are structured and tabular, conventional ML models are generally applied to them, and less effort is put into evaluating the artificial neural ne…
Early Warning of Intraoperative Adverse Events via Transformer-Driven Multi-Label Learning
Chengdu Institute of Computer Application, Chinese Academy of Sciences（中国科学院成都计算机应用研究所）；University of Chinese Academy of Sciences（中国科学院大学）；China Zhenhua Research Institute Co., Ltd.（中国振华研究院有限公司）
Early Warning Index for Patient Deteriorations in Hospitals
Sloan School of Management, Massachusetts Institute of Technology（麻省理工学院斯隆管理学院）；Operations and Information Management, University of Wisconsin Madison（威斯康星大学麦迪逊分校运营与信息管理系）；Technology Management and Innovation, New York University（纽约大学技术管理与创新系）；Hartford HealthCare（哈特福德医疗集团）；Holistic Hospital Optimization（综合医院优化）
Indian Institute of Technology Madras（印度理工学院马德拉斯分校）；Robert Bosch Centre for Data Science and AI（罗伯特·博世数据科学与人工智能中心）
Automated medical coding is a process of codifying clinical notes to appropriate diagnosis and procedure codes automatically from the standard taxonomies such as ICD (International Classification of Diseases) and CPT (Current Procedure Terminology). The manual coding process involves the identification of entities from the clinical notes followed by querying a commercial or non-commercial medical codes Information Retrieval (IR) system that follows the Centre for Medicare and Medicaid Services (CMS) guidelines. We propose to automate this manual process by automatically constructing a query fo…
Laura Health（劳拉健康）；PUCRS（巴西天主教大学里约热内卢分校（注：PUCRS常见全称为Pontifícia Universidade Católica do Rio Grande do Sul，即南里约热内卢天主教大学，此处保留缩写并给出常见译名））；University of Évora（埃武拉大学）
Early recognition of clinical deterioration is one of the main steps for reducing inpatient morbidity and mortality. The challenging task of clinical deterioration identification in hospitals lies in the intense daily routines of healthcare practitioners, in the unconnected patient data stored in the Electronic Health Records (EHRs) and in the usage of low accuracy scores. Since hospital wards are given less attention compared to the Intensive Care Unit, ICU, we hypothesized that when a platform is connected to a stream of EHR, there would be a drastic improvement in dangerous situations aware…
PatTree: a novel approach for automated creation of multimodal, graph-based patient representations for medical classification tasks
Institute for Biomedical Informatics, University of Cologne, Medical Faculty（科隆大学医学院生物医学信息学研究所）；University Hospital Cologne（科隆大学医院）；Department of Data Science and Artificial Intelligence, Fraunhofer Institute for Applied Information Technology（弗劳恩霍夫应用信息技术研究所数据科学与人工智能系）
Qatar University（卡塔尔大学）；University of Dhaka（达卡大学）；College of Medicine, QU Health, Qatar University（卡塔尔大学QU Health医学院）；Universiti Kebangsaan Malaysia（马来西亚国民大学）
COVID-19 pandemic has created an extreme pressure on the global healthcare services. Fast, reliable and early clinical assessment of the severity of the disease can help in allocating and prioritizing resources to reduce mortality. In order to study the important blood biomarkers for predicting disease mortality, a retrospective study was conducted on 375 COVID-19 positive patients admitted to Tongji Hospital (China) from January 10 to February 18, 2020. Demographic and clinical characteristics, and patient outcomes were investigated using machine learning tools to identify key biomarkers to p…
We developed an explainable artificial intelligence (AI) early warning score (xAI-EWS) system for early detection of acute critical illness. While maintaining a high predictive performance, our system explains to the clinician on which relevant electronic health records (EHRs) data the prediction is grounded. Acute critical illness is often preceded by deterioration of routinely measured clinical parameters, e.g., blood pressure and heart rate. Early clinical prediction is typically based on manually calculated screening metrics that simply weigh these parameters, such as Early Warning Scores …
Early diagnosis of dementia, particularly in the prodromal stage (i.e., mild cognitive impairment, or MCI), has become a research and clinical priority but remains challenging. Automated analysis of the drawing process has been studied as a promising means for screening prodromal and clinical dementia, providing multifaceted information encompassing features, such as drawing speed, pen posture, writing pressure, and pauses. We examined the feasibility of using these features not only for detecting prodromal and clinical dementia but also for predicting the severity of cognitive impairments ass…
Benchmarking Automated Clinical Language Simplification: Dataset, Algorithm, and Evaluation
College of Information Sciences and Technology, Pennsylvania State University（宾夕法尼亚州立大学信息科学与技术学院）；School of Software Technology, Dalian University of Technology（大连理工大学软件学院）；Relativity（相对论公司）
AutoMedic: An Automated Evaluation Framework for Clinical Conversational Agents with Medical Dataset Grounding
Yonsei University College of Medicine（延世大学医学院）；Yonsei Institute for Digital Health（延世大学数字健康研究所）；Yonsei University（延世大学）；Institute of Behavioral Sciences in Medicine（医学行为科学研究所）
Automated SNOMED CT Concept Annotation in Clinical Text Using Bi-GRU Neural Networks
Informatics and Analytics, University of North Carolina Greensboro（信息学与分析，北卡罗来纳大学格林斯堡分校）；Department of Computer Science, University of Nebraska Omaha（计算机科学系，内布拉斯加大学奥马哈分校）
Nankai University（南开大学）；Tianjin Huanhu Hospital（天津市环湖医院）；the University of Aizu（会津大学）；Tianjin Medical University（天津医科大学）；College of Electronic Information and Optical Engineering, Nankai University（南开大学电子信息与光学工程学院）；School of Computer Science and Engineering, the University of Aizu（会津大学计算机科学与工程学院）；School of Basic Medical Sciences, Tianjin Medical University（天津医科大学基础医学院）；Department of Medical Imaging, Tianjin Huanhu Hospital（天津市环湖医院医学影像科）；Key Laboratory for Cerebral Artery and Neural Degeneration of Tianjin（天津市脑血管及神经变性重点实验室）；Tianjin Key Laboratory of Optoelectronic Sensor and Sensing Network Technology（天津市光电传感器与传感网络技术重点实验室）
The patient with ischemic stroke can benefit most from the earliest possible definitive diagnosis. While the high quality medical resources are quite scarce across the globe, an automated diagnostic tool is expected in analyzing the magnetic resonance (MR) images to provide reference in clinical diagnosis. In this paper, we propose a deep learning method to automatically segment ischemic stroke lesions from multi-modal MR images. By using atrous convolution and global convolution network, our proposed residual-structured fully convolutional network (Res-FCN) is able to capture features from la…
University of California, Los Angeles（加利福尼亚大学洛杉矶分校）；Alan Turing Institute（艾伦·图灵研究所）
Clinical prognostic models derived from largescale healthcare data can inform critical diagnostic and therapeutic decisions. To enable off-theshelf usage of machine learning (ML) in prognostic research, we developed AUTOPROGNOSIS: a system for automating the design of predictive modeling pipelines tailored for clinical prognosis. AUTOPROGNOSIS optimizes ensembles of pipeline configurations efficiently using a novel batched Bayesian optimization (BO) algorithm that learns a low-dimensional decomposition of the pipelines high-dimensional hyperparameter space in concurrence with the BO procedure.…
Skin cancer is one of the most common types of cancer around the world. For this reason, over the past years, different approaches have been proposed to assist detect it. Nonetheless, most of them are based only on dermoscopy images and do not take into account the patient clinical information. In this work, first, we present a new dataset that contains clinical images, acquired from smartphones, and patient clinical information of the skin lesions. Next, we introduce a straightforward approach to combine the clinical data and the images using different well-known deep learning models. These m…
The present study proposes a new approach to automated screening of Clinically Significant Macular Edema (CSME) and addresses two major challenges associated with such screenings, i.e., exudate segmentation and imbalanced datasets. The proposed approach replaces the conventional exudate segmentation based feature extraction by combining a pre-trained deep neural network with meta-heuristic feature selection. A feature space over-sampling technique is being used to overcome the effects of skewed datasets and the screening is accomplished by a k-NN based classifier. The role of each data-process…
Radiotherapy inverse planning often requires planners to modify parameters in the treatment planning system's objective function to produce clinically acceptable plans. Due to the manual steps in this process, plan quality can vary depending on the planning time available and the planner's skills. This study investigates two hyperparameter-tuning methods for automated inverse planning. Because this framework does not train a model on previously-optimized plans, it can be readily adapted to practice pattern changes, and plan quality is not limited by that of a training cohort. We selected 10 pa…
University of Edinburgh（爱丁堡大学）；Health Data Research UK（英国健康数据研究所）；University College London（伦敦大学学院）
Diagnostic or procedural coding of clinical notes aims to derive a coded summary of disease-related information about patients. Such coding is usually done manually in hospitals but could potentially be automated to improve the efficiency and accuracy of medical coding. Recent studies on deep learning for automated medical coding achieved promising performances. However, the explainability of these models is usually poor, preventing them to be used confidently in supporting clinical practice. Another limitation is that these models mostly assume independence among labels, ignoring the complex …
National Institute of Technology Karnataka Surathkal（卡纳塔克邦国家技术学院苏拉特卡尔分校）
In clinical diagnosis, diagnostic images that are obtained from the scanning devices serve as preliminary evidence for further investigation in the process of delivering quality healthcare. However, often the medical image may contain fault artifacts, introduced due to noise, blur and faulty equipment. The reason for this may be the low-quality or older scanning devices, the test environment or technicians lack of training etc; however, the net result is that the process of fast and reliable diagnosis is hampered. Resolving these issues automatically can have a significant positive impact in a…
University Hospital, University of Zurich（苏黎世大学医院）；Leiden University Medical Center（莱顿大学医学中心）；University of Groningen, University Medical Center Groningen（格罗宁根大学医学中心）；Erasmus MC Cancer Institute, University Medical Center Rotterdam（鹿特丹大学医学中心伊拉斯谟MC癌症研究所）；University Medical Center Utrecht（乌得勒支大学医学中心）；Maastricht Radiation Oncology Clinic（马斯特里赫特放射肿瘤诊所）；Medisch Spectrum Twente（特文特医疗集团）；Radiotherapiegroep（放射治疗集团）
Randomized controlled trials (RCTs) are considered as the gold standard for testing causal hypotheses in the clinical domain. However, the investigation of prognostic variables of patient outcome in a hypothesized cause-effect route is not feasible using standard statistical methods. Here, we propose a new automated causal inference method (AutoCI) built upon the invariant causal prediction (ICP) framework for the causal re-interpretation of clinical trial data. Compared to existing methods, we show that the proposed AutoCI allows to efficiently determine the causal variables with a clear diff…
A Hybrid Multi-Factor Network with Dynamic Sequence Modeling for Early Warning of Intraoperative Hypotension
State Key Laboratory of Cognitive Intelligence, University of Science and Technology of China（认知智能国家重点实验室，中国科学技术大学）；Intelligent Interconnected Systems Laboratory of Anhui Province, Hefei University of Technology（安徽省智能互联系统实验室，合肥工业大学）
Codification of free-text clinical narratives have long been recognised to be beneficial for secondary uses such as funding, insurance claim processing and research. The current scenario of assigning codes is a manual process which is very expensive, time-consuming and error prone. In recent years, many researchers have studied the use of Natural Language Processing (NLP), related Machine Learning (ML) and Deep Learning (DL) methods and techniques to resolve the problem of manual coding of clinical narratives and to assist human coders to assign clinical codes more accurately and efficiently. …
Clinically Calibrated Machine Learning Benchmarks for Large-Scale Multi-Disorder EEG Classification
Department of Electronics；Electrical Communication Engineering Indian Institute of Technology, Kharagpur West Bengal, India；Department of Computer Science；Engineering Indian Institute of Technology, Kharagpur West Bengal, India；Subir Chowdhury School of Quality；Reliability Indian Institute of Technology, Kharagpur West Bengal, India
To be effective, state of the art machine learning technology needs large amounts of annotated data. There are numerous compelling applications in healthcare that can benefit from high performance automated decision support systems provided by deep learning technology, but they lack the comprehensive data resources required to apply sophisticated machine learning models. Further, for economic reasons, it is very difficult to justify the creation of large annotated corpora for these applications. Hence, automated annotation techniques become increasingly important. In this study, we investigate…
Graz University of Technology（格拉茨技术大学）；Stanford University（斯坦福大学）；Computer Algorithms for Médicine (Café) Laboratory（医学计算机算法（Café）实验室）；University Medicine Essen（埃森大学医学院）
Objective: Surveillance imaging of chronic aortic diseases, such as dissections, relies on obtaining and comparing cross-sectional diameter measurements at predefined aortic landmarks, over time. Due to a lack of robust tools, the orientation of the cross-sectional planes is defined manually by highly trained operators. We show how manual annotations routinely collected in a clinic can be efficiently used to ease this task, despite the presence of a non-negligible interoperator variability in the measurements. Impact: Ill-posed but repetitive imaging tasks can be eased or automated by leveragi…
An AI tool for automated analysis of large-scale unstructured clinical cine CMR databases
King’s College London（伦敦国王学院）；Guy’s and St Thomas’ NHS Foundation Trust（盖伊和圣托马斯NHS基金会信托）；Duke University（杜克大学）；Barts Health NHS Trust（巴特健康NHS基金会信托）；Alan Turing Institute（阿兰·图灵研究所）；University Medical Center Utrecht（乌得勒支大学医学中心）
ReLyfe - Medical Intelligence（ReLyfe 医疗智能公司）；Ecole Polytechnique（巴黎综合理工学院）；BeCareLink（BeCareLink 公司）
Structuring medical data in France remains a challenge mainly because of the lack of medical data due to privacy concerns and the lack of methods and approaches on processing the French language. One of these challenges is structuring drug-related information in French clinical documents. To our knowledge, over the last decade, there are less than five relevant papers that study French prescriptions. This paper proposes a new approach for extracting drug-related information from French clinical scanned documents while preserving patients' privacy. In addition, we deployed our method in a healt…
Large Language Models Streamline Automated Machine Learning for Clinical Studies
University Hospital Aachen（亚琛大学医院）；RWTH Aachen University（亚琛工业大学）；Technical University Dresden（德累斯顿工业大学）；National Center for Tumor Diseases (NCT)（国家肿瘤疾病中心 (NCT)）；University Hospital Heidelberg（海德堡大学医院）
University of Texas Southwestern Medical Center（德克萨斯大学西南医学中心）
Data cleaning consumes about 80% of the time spent on data analysis for clinical research projects. This is a much bigger problem in the era of big data and machine learning in the field of medicine where large volumes of data are being generated. We report an initial effort towards automated patient data cleaning using deep learning: the standardization of organ labeling in radiation therapy. Organs are often labeled inconsistently at different institutions (sometimes even within the same institution) and at different time periods, which poses a problem for clinical research, especially for m…
University of Toronto（多伦多大学）；The Hospital for Sick Children（病童医院）
Abbreviation disambiguation is important for automated clinical note processing due to the frequent use of abbreviations in clinical settings. Current models for automated abbreviation disambiguation are restricted by the scarcity and imbalance of labeled training data, decreasing their generalizability to orthogonal sources. In this work we propose a novel data augmentation technique that utilizes information from related medical concepts, which improves our model's ability to generalize. Furthermore, we show that incorporating the global context information within the whole medical note (in …
University of Sheffield（谢菲尔德大学）；Sheffield Teaching Hospitals NHS Foundation Trust（谢菲尔德教学医院NHS基金会信托）；INSIGNEO, Institute for In Silico Medicine, University of Sheffield（谢菲尔德大学INSIGNEO计算医学研究所）；NIHR Sheffield Biomedical Research Centre, University of Sheffield（谢菲尔德大学NIHR谢菲尔德生物医学研究中心）
Purpose: Lung disease assessment in precapillary pulmonary hypertension (PH) is essential for appropriate patient management. This study aims to develop an artificial intelligence (AI) deep learning model for lung texture classification in CT Pulmonary Angiography (CTPA), and evaluate its correlation with clinical assessment methods. Materials and Methods: In this retrospective study with external validation, 122 patients with pre-capillary PH were used to train (n=83), validate (n=17) and test (n=10 internal test, n=12 external test) a patch based DenseNet-121 classification model. "Normal", …
Comprehensive Evaluation of OCT-based Automated Segmentation of Retinal Layer, Fluid and Hyper-Reflective Foci: Impact on Clinical Assessment of Diabetic Retinopathy Severity
School of Engineering Science, Simon Fraser University（西蒙弗雷泽大学工程科学学院）；School of Medicine, Wake Forest University（威克森林大学医学院）；Sankara Nethralaya（萨卡拉眼科医院）；Department of Ophthalmology and Visual Sciences, The University of British Columbia（不列颠哥伦比亚大学眼科与视觉科学系）；School of Biomedical Engineering, The University of British Columbia（不列颠哥伦比亚大学生物医学工程学院）；Institute of Ophthalmology, University College London（伦敦大学学院眼科研究所）；Department of Medical Physics and Biomedical Engineering, University College London（伦敦大学学院医学物理与生物医学工程系）；Department of Computer Science, Memorial University of Newfoundland（新斯科舍纪念大学计算机科学系）
Reykjavik University（雷克雅未克大学）；Primary Health Care Service of the Capital Area（首都地区初级卫生保健服务中心）；University of Iceland（冰岛大学）；Development Centre for Primary Health Care in Iceland（冰岛初级卫生保健发展中心）
Clinical Text Notes (CTNs) contain physicians' reasoning process, written in an unstructured free text format, as they examine and interview patients. In recent years, several studies have been published that provide evidence for the utility of machine learning for predicting doctors' diagnoses from CTNs, a task known as ICD coding. Data annotation is time consuming, particularly when a degree of specialization is needed, as is the case for medical data. This paper presents a method of augmenting a sparsely annotated dataset of Icelandic CTNs with a machine-learned imputation in a semi-self-su…
University of Illinois at Chicago（伊利诺伊大学芝加哥分校）；University of Florida（佛罗里达大学）
Facial pain expression is an important modality for assessing pain, especially when the patient's verbal ability to communicate is impaired. The facial muscle-based action units (AUs), which are defined by the Facial Action Coding System (FACS), have been widely studied and are highly reliable as a method for detecting facial expressions (FE) including valid detection of pain. Unfortunately, FACS coding by humans is a very time-consuming task that makes its clinical use prohibitive. Significant progress on automated facial expression recognition (AFER) has led to its numerous successful applic…
Bridging AI and Clinical Practice: Integrating Automated Sleep Scoring Algorithm with Uncertainty-Guided Physician Review
University of Bern（伯尔尼大学）；University of Applied Sciences and Arts of Southern Switzerland（瑞士南部应用科学与艺术大学）；Inselspital, Bern University Hospital（伯尔尼大学医院）；Ohio Sleep Medicine Institute（俄亥俄睡眠医学研究所）
Aarhus University Hospital - Psychiatry（奥胡斯大学医院 - 精神病学部）；Aarhus University（奥胡斯大学）；University College London（伦敦大学学院）；University of Oxford（牛津大学）
Speech patterns have been identified as potential diagnostic markers for neuropsychiatric conditions. However, most studies only compare a single clinical group to healthy controls, whereas clinical practice often requires differentiating between multiple potential diagnoses (multiclass settings). To address this, we assembled a dataset of repeated recordings from 420 participants (67 with major depressive disorder, 106 with schizophrenia and 46 with autism, as well as matched controls), and tested the performance of a range of conventional machine learning models and advanced Transformer mode…
