Accurate and reliable prediction of hospital admission location is important due to resource-constraints and space availability in a clinical setting, particularly when dealing with patients who come from the emergency department. In this work we propose a student-teacher network via reinforcement learning to deal with this specific problem. A representation of the weights of the student network is treated as the state and is fed as an input to the teacher network. The teacher network's action is to select the most appropriate batch of data to train the student network on from a training set sorted according to entropy. By validating on three datasets, not only do we show that our approach outperforms state-of-the-art methods on tabular data and performs competitively on image recognition, but also that novel curricula are learned by the teacher network. We demonstrate experimentally that the teacher network can actively learn about the student network and guide it to achieve better performance than if trained alone.
CLR-voyance: Reinforcing Open-Ended Reasoning for Inpatient Clinical Decision Support with Outcome-Aware Rubrics
ASUS Intelligent Cloud Services (AICS)（ASUS智能云服务（AICS））；Rehabilitation Research Institute of Singapore, Nanyang Technological University（新加坡康复研究院，南洋理工大学）；Department of Family Medicine, Taipei Veterans General Hospital（台北荣民总医院家庭医学部）；School of Medicine, National Yang Ming Chiao Tung University（国家阳明交通大学医学院）；Yong Loo Lin School of Medicine, National University of Singapore（新加坡国立大学 Yong Loo Lin 医学院）
Identifying patients who will be discharged within 24 hours can improve hospital resource management and quality of care. We studied this problem using eight years of Electronic Health Records (EHR) data from Stanford Hospital. We fit models to predict 24 hour discharge across the entire inpatient population. The best performing models achieved an area under the receiver-operator characteristic curve (AUROC) of 0.85 and an AUPRC of 0.53 on a held out test set. This model was also well calibrated. Finally, we analyzed the utility of this model in a decision theoretic framework to identify regio…
Tsinghua University（清华大学）；Ping An Health Technology（平安健康科技）
Representation learning (RL) plays an important role in extracting proper representations from complex medical data for various analyzing tasks, such as patient grouping, clinical endpoint prediction and medication recommendation. Medical data can be divided into two typical categories, outpatient and inpatient, that have different data characteristics. However, few of existing RL methods are specially designed for inpatients data, which have strong temporal relations and consistent diagnosis. In addition, for unordered medical activity set, existing medical RL methods utilize a simple pooling…
University of Wisconsin-Milwaukee（威斯康星大学密尔沃基分校）；Aurora Research Institute（奥罗拉研究所）；Aurora Health Care（奥罗拉医疗保健）；Milwaukee Kidney Associates（密尔沃基肾脏协会）；Allina Health System（阿利纳医疗系统）
Acute kidney injury (AKI) commonly occurs in hospitalized patients and can lead to serious medical complications. In order to optimally predict AKI before it develops at any time during a hospital stay, we present a novel framework in which AKI is continually predicted automatically from EHR data over the entire hospital stay instead of at only one particular time. The continual model predicts AKI every time a patients AKI-relevant variable changes in the EHR. Thus the model is not only independent of a particular time for making predictions, but it can also leverage the latest values of all t…
Large amounts of electronic medical records collected by hospitals across the developed world offer unprecedented possibilities for knowledge discovery using computer based data mining and machine learning. Notwithstanding significant research efforts, the use of this data in the prediction of disease development has largely been disappointing. In this paper we examine in detail a recently proposed method which has in preliminary experiments demonstrated highly promising results on real-world data. We scrutinize the authors' claims that the proposed model is scalable and investigate whether th…
Hellenic Open University（希腊开放大学）；Sismanogleio General Hospital（西斯马诺格利奥综合医院）；Thoracic Diseases General Hospital Sotiria（索提里亚胸科疾病综合医院）；National and Kapodistrian University of Athens（雅典国家与卡波迪斯特里安大学）
Introduction: One of the most important tasks in the Emergency Department (ED) is to promptly identify the patients who will benefit from hospital admission. Machine Learning (ML) techniques show promise as diagnostic aids in healthcare. Material and methods: We investigated the following features seeking to investigate their performance in predicting hospital admission: serum levels of Urea, Creatinine, Lactate Dehydrogenase, Creatine Kinase, C-Reactive Protein, Complete Blood Count with differential, Activated Partial Thromboplastin Time, D Dimer, International Normalized Ratio, age, gender,…
University of Florida（佛罗里达大学）；Sepsis and Critical Illness Research Center（脓毒症与危重病研究中心）
Background: During the early stages of hospital admission, clinicians must use limited information to make diagnostic and treatment decisions as patient acuity evolves. However, it is common that the time series vital sign information from patients to be both sparse and irregularly collected, which poses a significant challenge for machine / deep learning techniques to analyze and facilitate the clinicians to improve the human health outcome. To deal with this problem, We propose a novel deep interpolation network to extract latent representations from sparse and irregularly sampled time-serie…
Universitat Politècnica de València（瓦伦西亚理工大学）；Instituto de Investigación Sanitaria La Fe（拉费健康研究所）；Universitat de València（瓦伦西亚大学）；Consejo Superior de Investigaciones Científicas（西班牙国家研究委员会）
Background: Palliative care is referred to a set of programs for patients that suffer life-limiting illnesses. These programs aim to guarantee a minimum level of quality of life (QoL) for the last stage of life. They are currently based on clinical evaluation of risk of one-year mortality. Objectives: The main objective of this work is to develop and validate machine-learning based models to predict the exitus of a patient within the next year using data gathered at hospital admission. Methods: Five machine learning techniques were applied in our study to develop machine-learning predictive mo…
Febrile neutropenia (FN) has been associated with high mortality, especially among adults with cancer. Understanding the patient and provider level heterogeneity in FN hospital admissions has potential to inform personalized interventions focused on increasing survival of individuals with FN. We leverage machine learning techniques to disentangling the complex interactions among multi domain risk factors in a population with FN. Data from the Healthcare Cost and Utilization Project (HCUP) National Inpatient Sample and Nationwide Inpatient Sample (NIS) were used to build machine learning based …
University of Toronto（多伦多大学）；Emory University（埃默里大学）；Georgia Institute of Technology（佐治亚理工学院）；University of Arizona（亚利桑那大学）
30-day hospital readmission is a long standing medical problem that affects patients' morbidity and mortality and costs billions of dollars annually. Recently, machine learning models have been created to predict risk of inpatient readmission for patients with specific diseases, however no model exists to predict this risk across all patients. We developed a bi-directional Long Short Term Memory (LSTM) Network that is able to use readily available insurance data (inpatient visits, outpatient visits, and drug prescriptions) to predict 30 day re-admission for any admitted patient, regardless of …
Northeast Yucai Foreign Language School（东北育才外国语学校）；Northeast Yucai School（东北育才学校）；Shenyang Pharmaceutical University（沈阳药科大学）
ICU readmission is associated with longer hospitalization, mortality and adverse outcomes. An early recognition of ICU re-admission can help prevent patients from worse situation and lower treatment cost. As the abundance of Electronics Health Records (EHR), it is popular to design clinical decision tools with machine learning technique manipulating on healthcare large scale data. We designed data-driven predictive models to estimate the risk of ICU readmission. The discharge summary of each hospital admission was carefully represented by natural language processing techniques. Unified Medical…
Explainable Admission-Level Predictive Modeling for Prolonged Hospital Stay in Elderly Populations: Challenges in Low- and Middle-Income Countries
Biophysics of Tropical Diseases Max Planck Tandem Group, University of Antioquia (UdeA)（热带疾病生物物理学马克斯·普朗克双人组，安蒂奥基亚大学）；Grupo de Física Atómica y Molecular, Instituto de Física, University of Antioquia (UdeA)（原子与分子物理组，物理研究所，安蒂奥基亚大学）；Graduate School and Research, Arcada University of Applied Sciences（应用科学大学研究生院）；VTT Technical Research Centre of Finland Ltd（芬兰技术研究中心）；Department of Internal Medicine, School of Medicine, University of Antioquia（内科学系，医学院，安蒂奥基亚大学）；Hospital Alma Mater de Antioquia, University of Antioquia（安蒂奥基亚大学阿尔玛 Mater 医院）
King’s College London（伦敦国王学院）；University College London（伦敦大学学院）
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 …
University of California, Los Angeles（加利福尼亚大学洛杉矶分校）
Critically ill patients in regular wards are vulnerable to unanticipated clinical dete- rioration which requires timely transfer to the intensive care unit (ICU). To allow for risk scoring and patient monitoring in such a setting, we develop a novel Semi- Markov Switching Linear Gaussian Model (SSLGM) for the inpatients' physiol- ogy. The model captures the patients' latent clinical states and their corresponding observable lab tests and vital signs. We present an efficient unsupervised learn- ing algorithm that capitalizes on the informatively censored data in the electronic health records (E…
Instance Generation for Patient-to-room Assignment and Admission Scheduling Based on Real Hospital Data
Chair of Combinatorial Optimization, RWTH Aachen University（组合优化研究所，亚琛工业大学）；Chair of Algorithms and Complexity, RWTH Aachen University（算法与复杂性研究所，亚琛工业大学）
DarwinAI Corp.（DarwinAI公司）；University of Waterloo（滑铁卢大学）；Waterloo Artificial Intelligence Institute（滑铁卢人工智能研究所）
The COVID-19 pandemic continues to have a devastating global impact, and has placed a tremendous burden on struggling healthcare systems around the world. Given the limited resources, accurate patient triaging and care planning is critical in the fight against COVID-19, and one crucial task within care planning is determining if a patient should be admitted to a hospital's intensive care unit (ICU). Motivated by the need for transparent and trustworthy ICU admission clinical decision support, we introduce COVID-Net Clinical ICU, a neural network for ICU admission prediction based on patient cl…
Predicting 30-Day Hospital Readmission in Medicare Patients: Insights from an LSTM Deep Learning Model
Georgia Institute of Technology（佐治亚理工学院）；University of Miami（迈阿密大学）；University of California（加利福尼亚大学）；Boston University（波士顿大学）；University of Pennsylvania（宾夕法尼亚大学）
University of Rochester Medical Center（罗切斯特大学医学中心）；University of Rochester（罗切斯特大学）；Goergen Institute for Data Science（戈尔根数据科学研究所）
Acute Kidney Injury (AKI), a sudden decline in kidney function, is associated with increased mortality, morbidity, length of stay, and hospital cost. Since AKI is sometimes preventable, there is great interest in prediction. Most existing studies consider all patients and therefore restrict to features available in the first hours of hospitalization. Here, the focus is instead on rehospitalized patients, a cohort in which rich longitudinal features from prior hospitalizations can be analyzed. Our objective is to provide a risk score directly at hospital re-entry. Gradient boosting, penalized l…
Validating Emergency Department Admission Predictions Based on Local Data Through MIMIC-IV
Hellenic Open University（希腊开放大学）；Sismanogleio General Hospital（西斯马诺格利奥综合医院）；National and Kapodistrian University of Athens（雅典国立卡波迪斯特里亚大学）；Amalia Fleming General Hospital（阿玛利亚·弗莱明综合医院）
University of Zurich（苏黎世大学）；University Hospital of Zurich（苏黎世大学医院）；Yale University（耶鲁大学）；Lister Hill National Center for Biomedical Communications（利斯特希尔国家生物医学通信中心）；National Library of Medicine（美国国家医学图书馆）；Yale School of Medicine（耶鲁医学院）
Heart failure (HF) is one of the leading causes of hospital admissions in the US. Readmission within 30 days after a HF hospitalization is both a recognized indicator for disease progression and a source of considerable financial burden to the healthcare system. Consequently, the identification of patients at risk for readmission is a key step in improving disease management and patient outcome. In this work, we used a large administrative claims dataset to (1)explore the systematic application of neural network-based models versus logistic regression for predicting 30 days all-cause readmissi…
Explainable AI for Infection Prevention and Control: Modeling CPE Acquisition and Patient Outcomes in an Irish Hospital with Transformers
School of Computing（计算学院）；Dublin City University（都柏林城市大学）；ADAPT Centre（ADAPT中心）；University College Dublin（都柏林大学）；St James’s Hospital（圣詹姆斯医院）；Health Service Executive (HSE)（健康服务执行局）；Trinity College Dublin（三一学院）
Early Prediction of In-Hospital ICU Mortality Using Innovative First-Day Data: A Review
Affiliated Hospital of Guangdong Medical University（广东医科大学附属医院）；Maastricht University Medical Center（马斯特里赫特大学医学中心）；GROW School for Oncology and Reproduction, Maastricht University Medical Center（马斯特里赫特大学医学中心肿瘤与生殖GROW学院）；Institute of Nephrology, Affiliated Hospital of Guangdong Medical University（广东医科大学附属医院肾脏病研究所）；Guangdong Provincial Key Laboratory of Autophagy and Major Chronic Non-communicable Diseases（广东省自噬与重大慢性非传染性疾病重点实验室）；Beijing University of Technology（北京工业大学）；China Agricultural University（中国农业大学）；JTB Technology Corp.（JTB科技股份有限公司）；Guangdong Medical University（广东医科大学）；Chinese PLA General Hospital（中国人民解放军总医院）；Shengjing Hospital of China Medical University（中国医科大学附属盛京医院）
Survival modeling using deep learning, machine learning and statistical methods: A comparative analysis for predicting mortality after hospital admission
Duke-NUS Medical School（杜克-新加坡国立大学医学院）；Sorbonne University（索邦大学）；Stanford University（斯坦福大学）；Singapore General Hospital（新加坡中央医院）；National University of Singapore（新加坡国立大学）
University of Illinois at Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；City University of Hong Kong（香港城市大学）
Emergency Department (ED) crowding is a worldwide issue that affects the efficiency of hospital management and the quality of patient care. This occurs when the request for an admit ward-bed to receive a patient is delayed until an admission decision is made by a doctor. To reduce the overcrowding and waiting time of ED, we build a classifier to predict the disposition of patients using manually-typed nurse notes collected during triage, thereby allowing hospital staff to begin necessary preparation beforehand. However, these triage notes involve high dimensional, noisy, and also sparse text d…
Predicting the Stay Length of Patients in Hospitals using Convolutional Gated Recurrent Deep Learning Model
Canberra Health Services（堪培拉卫生服务局）；Canberra Hospital（堪培拉医院）；Torrens University Australia（澳大利亚托伦斯大学）；The Australian National University（澳大利亚国立大学）；Australian National University（澳大利亚国立大学）
Recent years have seen an increased focus into the tasks of predicting hospital inpatient risk of deterioration and trajectory evolution due to the availability of electronic patient data. A common approach to these problems involves clustering patients time-series information such as vital sign observations) to determine dissimilar subgroups of the patient population. Most clustering methods assume time-invariance of vital-signs and are unable to provide interpretability in clusters that is clinically relevant, for instance, event or outcome information. In this work, we evaluate three differ…
University of Alberta（阿尔伯塔大学）；Amirkabir University of Technology（阿米尔卡比尔理工大学）
The pandemic COVID-19 disease has had a dramatic impact on almost all countries around the world so that many hospitals have been overwhelmed with Covid-19 cases. As medical resources are limited, deciding on the proper allocation of these resources is a very crucial issue. Besides, uncertainty is a major factor that can affect decisions, especially in medical fields. To cope with this issue, we use fuzzy logic (FL) as one of the most suitable methods in modeling systems with high uncertainty and complexity. We intend to make use of the advantages of FL in decisions on cases that need to treat…
Social Determinants of Health Prediction for ICD-9 Code with Reasoning Models
University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；University of Illinois Chicago（伊利诺伊大学芝加哥分校）；University of Illinois Chicago Peoria（伊利诺伊大学芝加哥分校皮奥里亚分校）
Development and external validation of a multimodal artificial intelligence mortality prediction model of critically ill patients using multicenter data
University of California, San Diego Health（加州大学圣地亚哥分校健康中心）；University of California, San Diego（加州大学圣地亚哥分校）
Wright State University（莱特州立大学）；Kno.e.sis Research Center（Kno.e.sis研究中心）
Early hospital mortality prediction is critical as intensivists strive to make efficient medical decisions about the severely ill patients staying in intensive care units. As a result, various methods have been developed to address this problem based on clinical records. However, some of the laboratory test results are time-consuming and need to be processed. In this paper, we propose a novel method to predict mortality using features extracted from the heart signals of patients within the first hour of ICU admission. In order to predict the risk, quantitative features have been computed based…
Growing Data；BioMedical Informatics center, Faculty of Medicine, Chiang Mai University（清迈大学医学院生物医学信息中心）；Chiang Mai University（清迈大学）；Biomedical Engineering Institute, Chiang Mai University（清迈大学生物医学工程研究所）；Maharaj Nakhon Chiang Mai hospital（玛哈拉·那空清迈医院）
The translation of medical diagnosis to clinical coding has wide range of applications in billing, aetiology analysis, and auditing. Currently, coding is a manual effort while the automation of such task is not straight forward. Among the challenges are the messy and noisy clinical records, case complexities, along with the huge ICD10 code space. Previous work mainly relied on discharge notes for prediction and was applied to a very limited data scale. We propose an ensemble model incorporating multiple clinical data sources for accurate code predictions. We further propose an assessment mecha…
Growing Data；BioMedical Informatics center, Faculty of Medicine, Chiang Mai University（清迈大学医学院生物医学信息中心）；Faculty of Medicine, Chiang Mai University（清迈大学医学院）；Faculty of Engineering, Chiang Mai University（清迈大学工程学院）；Biomedical Engineering Institute, Chiang Mai University（清迈大学生物医学工程研究所）；Maharaj Nakhon Chiang Mai hospital（玛哈拉吉那空清迈医院）；IT department of Maharaj Nakhon Chiang Mai hospital（玛哈拉吉那空清迈医院信息技术部）
Clinical coding is an administrative process that involves the translation of diagnostic data from episodes of care into a standard code format such as ICD10. It has many critical applications such as billing and aetiology research. The automation of clinical coding is very challenging due to data sparsity, low interoperability of digital health systems, complexity of real-life diagnosis coupled with the huge size of ICD10 code space. Related work suffer from low applicability due to reliance on many data sources, inefficient modelling and less generalizable solutions. We propose a novel colla…
Data-Driven Machine Learning Approaches for Predicting In-Hospital Sepsis Mortality
University of Southern California（南加州大学）；California State Polytechnic University, Pomona（加州州立理工大学波莫纳分校）；California State University, Long Beach（加州州立大学长滩分校）
At-Admission Prediction of Mortality and Pulmonary Embolism in COVID-19 Patients Using Statistical and Machine Learning Methods: An International Cohort Study
University of Oxford（牛津大学）；Institute for Clinical Research, National Institutes of Health (NIH), Malaysia（马来西亚国立卫生研究院临床研究所）；Amsterdam University Medical Center（阿姆斯特丹大学医学中心）
University of Texas Health Science Center at Houston（德克萨斯大学休斯顿健康科学中心）；McGovern Medical School of The University of Texas Health Science Center at Houston（德克萨斯大学休斯顿健康科学中心麦戈文医学院）
We propose a robust in-time predictor for in-hospital COVID-19 patient's probability of requiring mechanical ventilation. A challenge in the risk prediction for COVID-19 patients lies in the great variability and irregular sampling of patient's vitals and labs observed in the clinical setting. Existing methods have strong limitations in handling time-dependent features' complex dynamics, either oversimplifying temporal data with summary statistics that lose information or over-engineering features that lead to less robust outcomes. We propose a novel in-time risk trajectory predictive model to…
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）
MANDARIN: Mixture-of-Experts Framework for Dynamic Delirium and Coma Prediction in ICU Patients: Development and Validation of an Acute Brain Dysfunction Prediction Model
University of Florida（佛罗里达大学）；Stanford University（斯坦福大学）
