Patient similarity assessment (PSA) is pivotal to evidence-based and personalized medicine, enabled by analyzing the increasingly available electronic health records (EHRs). However, machine learning approaches for PSA has to deal with inherent data deficiencies of EHRs, namely missing values, noise, and small sample sizes. In this work, an end-to-end discriminative learning framework, called SparGE, is proposed to address these data challenges of EHR for PSA. SparGE measures similarity by jointly sparse coding and graph embedding. First, we use low-rank constrained sparse coding to identify and calculate weight for similar patients, while denoising against missing values. Then, graph embedding on sparse representations is adopted to measure the similarity between patient pairs via preserving local relationships defined by distances. Finally, a global cost function is constructed to optimize related parameters. Experimental results on two private and public real-world healthcare datasets, namely SingHEART and MIMIC-III, show that the proposed SparGE significantly outperforms other machine learning patient similarity methods.
A medical coding language model trained on clinical narratives from a population-wide cohort of 1.8 million patients
University of Copenhagen（哥本哈根大学）；Corti（Corti公司）；Novo Nordisk Foundation Center for Protein Research（诺和诺德基金会蛋白质研究中心）；Danish Research Institute for Suicide Prevention（丹麦自杀预防研究所）；Mental Health Centre Copenhagen（哥本哈根精神卫生中心）
This paper aims to provide an approach for automatic coding of physician-patient communication transcripts to improve patient-centered communication (PCC). PCC is a central part of high-quality health care. To improve PCC, dialogues between physicians and patients have been recorded and tagged with predefined codes. Trained human coders have manually coded the transcripts. Since it entails huge labor costs and poses possible human errors, automatic coding methods should be considered for efficiency and effectiveness. We adopted three machine learning algorithms (Naïve Bayes, Random Forest, and…
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…
An Agentic AI System for Multi-Framework Communication Coding
Duke School of Medicine（杜克大学医学院）；Duke-NUS Medical School（杜克-新加坡国立大学医学院）；MedStar Health National Center for Human Factors in Healthcare（MedStar Health国家医疗人因中心）；University of Pennsylvania（宾夕法尼亚大学）；Duke Cancer Institute（杜克癌症研究所）；Duke University（杜克大学）
Sepsis Prediction Using Graph Convolutional Networks over Patient-Feature-Value Triplets
College of Computer and Information Science（计算机与信息科学学院）；State Key Laboratory of Public Big Data（公共大数据国家重点实验室）；Department of Anesthesiology（麻醉科）；Key Laboratory of Perioperative Multi-organ Protection（围术期多器官保护关键实验室）
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 …
Inferring Actual Treatment Pathways from Patient Records
Australian Artificial Intelligence Institute（澳大利亚人工智能研究所）；University of Technology Sydney（悉尼科技大学）；The Children’s Cancer Research Unit（儿童癌症研究组）；The Children’s Hospital at Westmead（韦斯特米德儿童医院）；Ontario Tech University（安大略理工大学）
Missing values challenge data analysis because many supervised and unsupervised learning methods cannot be applied directly to incomplete data. Matrix completion based on low-rank assumptions are very powerful solution for dealing with missing values. However, existing methods do not consider the case of informative missing values which are widely encountered in practice. This paper proposes matrix completion methods to recover Missing Not At Random (MNAR) data. Our first contribution is to suggest a model-based estimation strategy by modelling the missing mechanism distribution. An EM algorit…
De-Identification of French Unstructured Clinical Notes for Machine Learning Tasks
Femto-ST Institute（菲姆托-ST研究所）；Université de Franche-Comté（弗朗什-孔泰大学）；CNRS（法国国家科学研究中心）；Université Technologique de Belfort-Montbéliard（贝尔福-蒙贝利亚尔技术大学）；Nord Franche-Comté Hospital（北弗朗什-孔泰医院）
Chongqing Radio and TV University（重庆广播电视大学）；Chongqing University（重庆大学）
Labeled speech data from patients with Parkinsons disease (PD) are scarce, and the statistical distributions of training and test data differ significantly in the existing datasets. To solve these problems, dimensional reduction and sample augmentation must be considered. In this paper, a novel PD classification algorithm based on sparse kernel transfer learning combined with a parallel optimization of samples and features is proposed. Sparse transfer learning is used to extract effective structural information of PD speech features from public datasets as source domain data, and the fast ADDM…
Institute for Biomedical Informatics（生物医学信息学研究所）；University of Pennsylvania（宾夕法尼亚大学）
Motivation: Many researchers with domain expertise are unable to easily apply machine learning to their bioinformatics data due to a lack of machine learning and/or coding expertise. Methods that have been proposed thus far to automate machine learning mostly require programming experience as well as expert knowledge to tune and apply the algorithms correctly. Here, we study a method of automating biomedical data science using a web-based platform that uses AI to recommend model choices and conduct experiments. We have two goals in mind: first, to make it easy to construct sophisticated models…
A Model-Driven Engineering Approach to AI-Powered Healthcare Platforms
Cairo University（开罗大学）；Scientific Academy for Service Technology e.V. (ServTech)（服务技术科学协会（ServTech））；Egypt University of Informatics（埃及信息学大学）；University of New South Wales（新南威尔士大学）
