A third of adults in America use the Internet to diagnose medical concerns, and online symptom checkers are increasingly part of this process. These tools are powered by diagnosis models similar to clinical decision support systems, with the primary difference being the coverage of symptoms and diagnoses. To be useful to patients and physicians, these models must have high accuracy while covering a meaningful space of symptoms and diagnoses. To the best of our knowledge, this paper is the first in studying the trade-off between the coverage of the model and its performance for diagnosis. To this end, we learn diagnosis models with different coverage from EHR data. We find a 1\% drop in top-3 accuracy for every 10 diseases added to the coverage. We also observe that complexity for these models does not affect performance, with linear models performing as well as neural networks.
Istanbul Medipol University（伊斯坦布尔梅迪波尔大学）；SRTM University Nanded（SRTM大学楠德分校）
Using machine learning algorithms for the rapid diagnosis and detection of the COVID-19 pandemic and isolating the patients from crowded environments are very important to controlling the epidemic. This study aims to develop a point-of-care testing (POCT) system that can detect COVID-19 by detecting volatile organic compounds (VOCs) in a patient's exhaled breath using the Gradient Boosted Trees Learner Algorithm. 294 breath samples were collected from 142 patients at Istanbul Medipol Mega Hospital between December 2020 and March 2021. 84 cases out of 142 resulted in negatives, and 58 cases res…
Tarbiat Modares University (TMU)（塔比阿特·莫达雷斯大学（TMU））；Hospital Management Research Center (HMRC), Iran University of Medical Sciences (IUMS)（伊朗医科大学医院管理研究中心（HMRC））；Louisiana Tech University（路易斯安那理工大学）；Arak University of Medical Science（阿拉克医科大学）
Background: Mycobacterium Tuberculosis (TB) is an infectious bacterial disease presenting similar symptoms to pneumonia; therefore, differentiating between TB and pneumonia is challenging. Therefore, the main aim of this study is proposing an automatic method for differential diagnosis of TB from Pneumonia. Methods: In this study, a two-step decision support system named TPIS is proposed for differential diagnosis of TB from pneumonia based on stacked ensemble classifiers. The first step of our proposed model aims at early diagnosis based on low-cost features including demographic characterist…
Recent developments in medical imaging with Deep Learning presents evidence of automated diagnosis and prognosis. It can also be a complement to currently available diagnosis methods. Deep Learning can be leveraged for diagnosis, severity prediction, intubation support prediction and many similar tasks. We present prediction of intubation support requirement for patients from the Chest X-ray using Deep representation learning. We release our source code publicly at https://github.com/aniketmaurya/covid-research.
Khoury College of Computer Sciences, Northeastern University（东北大学科里计算机科学学院）；Brigham and Women’s Hospital（布莱根妇女医院）
Electronic Health Records (EHRs) provide vital contextual information to radiologists and other physicians when making a diagnosis. Unfortunately, because a given patient's record may contain hundreds of notes and reports, identifying relevant information within these in the short time typically allotted to a case is very difficult. We propose and evaluate models that extract relevant text snippets from patient records to provide a rough case summary intended to aid physicians considering one or more diagnoses. This is hard because direct supervision (i.e., physician annotations of snippets re…
COVID-19 diagnosis by routine blood tests using machine learning
Smart Blood Analytics Swiss SA（智能血液分析瑞士公司）；Faculty of Computer and Information Science, University of Ljubljana（卢布尔雅那大学计算机与信息科学学院）；Faculty of Chemistry and Chemical Technology, University of Ljubljana（卢布尔雅那大学化学与化学技术学院）；University Medical Centre Ljubljana（卢布尔雅那大学医学中心）
Centre for Big Data Research in Health, University of New South Wales（新南威尔士大学健康大数据研究中心）
Deep neural networks are increasingly being used for computer-aided diagnosis, but erroneous diagnoses can be extremely costly for patients. We propose a learning to defer with uncertainty (LDU) algorithm which identifies patients for whom diagnostic uncertainty is high and defers them for evaluation by human experts. LDU was evaluated on the diagnosis of myocardial infarction (using discharge summaries), the diagnosis of any comorbidities (using structured data), and the diagnosis of pleural effusion and pneumothorax (using chest x-rays), and compared with 'learning to defer without uncertain…
The University of Melbourne（墨尔本大学）；United Arab Emirates University（阿联酋大学）；National Water and Energy Center, United Arab Emirates University（阿联酋大学国家水与能源中心）
The outbreak of the COVID-19 pandemic revealed the criticality of timely intervention in a situation exacerbated by a shortage in medical staff and equipment. Pain-level screening is the initial step toward identifying the severity of patient conditions. Automatic recognition of state and feelings help in identifying patient symptoms to take immediate adequate action and providing a patient-centric medical plan tailored to a patient's state. In this paper, we propose a framework for pain-level detection for deployment in the United Arab Emirates and assess its performance using the most used a…
Arison School of Business, Interdisciplinary Center（阿里森商学院，跨学科中心）
A key step in medical diagnosis is giving the patient a universally recognized label (e.g. Appendicitis) which essentially assigns the patient to a class(es) of patients with similar body failures. However, two patients having the same disease label(s) with high probability may still have differences in their feature manifestation patterns implying differences in the required treatments. Additionally, in many cases, the labels of the primary diagnoses leave some findings unexplained. Medical diagnosis is only partially about probability calculations for label X or Y. Diagnosis is not complete …
Xiamen University（厦门大学）；The University of Sydney（悉尼大学）；Tsinghua University（清华大学）
Artificial intelligence methods have been increasingly turning into a potentially powerful tool in the diagnosis and management of diseases. In this study, we utilized logistic regression (LR), decision tree (DT), gradient boosted decision tree (GBDT), support vector machine (SVM), and multilayer perceptron (MLP) as machine learning models to rapidly diagnose the mycoplasma pneumoniae pneumonia (MPP) in children patients. The classification task was carried out after applying the preprocessing procedure to the MPP dataset. The most efficient results are obtained by GBDT. It provides the best p…
We show how to learn low-dimensional representations (embeddings) of patient visits from the corresponding electronic health record (EHR) where International Classification of Diseases (ICD) diagnosis codes are removed. We expect that these embeddings will be useful for the construction of predictive statistical models anticipated to drive personalized medicine and improve healthcare quality. These embeddings are learned using a deep neural network trained to predict ICD diagnosis categories. We show that our embeddings capture relevant clinical informations and can be used directly as input t…
Mila-Quebec AI Institute（魁北克人工智能研究所Mila）；Dialogue Health Technologies Inc.（Dialogue健康技术公司）
There has been a rapidly growing interest in Automatic Symptom Detection (ASD) and Automatic Diagnosis (AD) systems in the machine learning research literature, aiming to assist doctors in telemedicine services. These systems are designed to interact with patients, collect evidence about their symptoms and relevant antecedents, and possibly make predictions about the underlying diseases. Doctors would review the interactions, including the evidence and the predictions, collect if necessary additional information from patients, before deciding on next steps. Despite recent progress in this area…
Ben-Gurion University of the Negev（内盖夫本-古里安大学）；Soroka University Medical Center（索罗卡大学医学中心）
Sepsis is a condition caused by the body's overwhelming and life-threatening response to infection, which can lead to tissue damage, organ failure, and finally death. Common signs and symptoms include fever, increased heart rate, increased breathing rate, and confusion. Sepsis is difficult to predict, diagnose, and treat. Patients who develop sepsis have an increased risk of complications and death and face higher health care costs and longer hospitalization. Today, sepsis is one of the leading causes of mortality among populations in intensive care units (ICUs). In this paper, we look at the …
Mila-Quebec AI Institute（魁北克人工智能研究所Mila）；Dialogue Health Technologies Inc.（Dialogue健康科技公司）
The automation of the medical evidence acquisition and diagnosis process has recently attracted increasing attention in order to reduce the workload of doctors and democratize access to medical care. However, most works proposed in the machine learning literature focus solely on improving the prediction accuracy of a patient's pathology. We argue that this objective is insufficient to ensure doctors' acceptability of such systems. In their initial interaction with patients, doctors do not only focus on identifying the pathology a patient is suffering from; they instead generate a differential …
Hyperbolic Graph Representation Learning for Differential Diagnosis on Biomedical Knowledge Graphs
Università degli Studi di Milano（米兰大学）；Università della Svizzera italiana (USI)（瑞士意大利语大学）；Politecnico di Milano（米兰理工大学）；Aalto University（阿尔托大学）；FGV（杰图利奥·瓦加斯基金会）
Currently, strokes are the leading cause of adult disability in the United States. Traditional treatment and rehabilitation options such as physical therapy and tissue plasminogen activator are limited in their effectiveness and ability to restore mobility and function to the patient. As a result, there exists an opportunity to greatly improve the treatment for strokes. Machine learning, specifically techniques that utilize Brain-Computer Interfaces (BCIs) to help the patient either restore neurologic pathways or effectively communicate with an electronic prosthetic, show promising results whe…
Institute of AI for Health, Helmholtz Munich, Germany International School of Medicine, Istanbul Medipol University, T\"urkiye Munich Leukemia Laboratory, Germany Department of Medicine III, Ludwig-Maximilian-University Hospital, Germany Department of Physics, University of Munich, Germany Munich Center for Machine Learning (MCML), Germany DKTK, German Cancer Consortium, Germany
Designing a Deep Learning-Driven Resource-Efficient Diagnostic System for Metastatic Breast Cancer: Reducing Long Delays of Clinical Diagnosis and Improving Patient Survival in Developing Countries
University of Maryland, Baltimore County（马里兰大学巴尔的摩县分校）；Meta Platforms Inc.（元平台公司）
University of Wisconsin - Madison（威斯康星大学麦迪逊分校）；Marshfield Clinic Research Institute（马什菲尔德诊所研究所）；Stanford University（斯坦福大学）
The widespread digitization of patient data via electronic health records (EHRs) has created an unprecedented opportunity to use machine learning algorithms to better predict disease risk at the patient level. Although predictive models have previously been constructed for a few important diseases, such as breast cancer and myocardial infarction, we currently know very little about how accurately the risk for most diseases or events can be predicted, and how far in advance. Machine learning algorithms use training data rather than preprogrammed rules to make predictions and are well suited for…
HIST-AID: Leveraging Historical Patient Reports for Enhanced Multi-Modal Automatic Diagnosis
Courant Institute of Mathematical Sciences, New York University（纽约大学柯朗数学科学研究所）；New York University Grossman School of Medicine（纽约大学格罗斯曼医学院）；Center of Data Science, New York University（纽约大学数据科学中心）；Prescient Design, Genentech（基因泰克公司Prescient Design部门）；CIFAR LMB（加拿大高级研究院学习算法与机器智能实验室）
Bipolar disorder is a mental health disorder that causes mood swings that range from depression to mania. Diagnosis of bipolar disorder is usually done based on patient interviews, and reports obtained from the caregivers of the patients. Subsequently, the diagnosis depends on the experience of the expert, and it is possible to have confusions of the disorder with other mental disorders. Automated processes in the diagnosis of bipolar disorder can help providing quantitative indicators, and allow easier observations of the patients for longer periods. Furthermore, the need for remote treatment…
This paper presents a novel lightweight COVID-19 diagnosis framework using CT scans. Our system utilises a novel two-stage approach to generate robust and efficient diagnoses across heterogeneous patient level inputs. We use a powerful backbone network as a feature extractor to capture discriminative slice-level features. These features are aggregated by a lightweight network to obtain a patient level diagnosis. The aggregation network is carefully designed to have a small number of trainable parameters while also possessing sufficient capacity to generalise to diverse variations within differ…
OpenAPMax: Abnormal Patterns-based Model for Real-World Alzheimer's Disease Diagnosis
Guangxi Normal University（广西师范大学）；Institute of Computing Technology, Chinese Academy of Sciences（中国科学院计算技术研究所）；Capital Medical University（首都医科大学）；Guilin Medical University（桂林医学院）
Augmentation of disease diagnosis and decision-making in healthcare with machine learning algorithms is gaining much impetus in recent years. In particular, in the current epidemiological situation caused by COVID-19 pandemic, swift and accurate prediction of disease diagnosis with machine learning algorithms could facilitate identification and care of vulnerable clusters of population, such as those having multi-morbidity conditions. In order to build a useful disease diagnosis prediction system, advancement in both data representation and development of machine learning architectures are imp…
Babylon Health（巴比伦健康）；University College London（伦敦大学学院）
Machine learning promises to revolutionize clinical decision making and diagnosis. In medical diagnosis a doctor aims to explain a patient's symptoms by determining the diseases \emph{causing} them. However, existing diagnostic algorithms are purely associative, identifying diseases that are strongly correlated with a patients symptoms and medical history. We show that this inability to disentangle correlation from causation can result in sub-optimal or dangerous diagnoses. To overcome this, we reformulate diagnosis as a counterfactual inference task and derive new counterfactual diagnostic al…
AI-powered skin spectral imaging enables instant sepsis diagnosis and outcome prediction in critically ill patients
German Cancer Research Center (DKFZ)（德国癌症研究中心）；Helmholtz Information and Data Science School for Health（亥姆霍兹健康信息与数据科学学院）；Heidelberg University（海德堡大学）；National Center for Tumor Diseases (NCT)（国家肿瘤疾病中心）；Heidelberg University Hospital（海德堡大学医院）；University Medical Center Mannheim（曼海姆大学医学中心）；DKFZ Hector Cancer Institute（DKFZ赫克托癌症研究所）
Realism in Action: Anomaly-Aware Diagnosis of Brain Tumors from Medical Images Using YOLOv8 and DeiT
Department of CS \& IT Institute for Advanced Studies in Basic Sciences (IASBS) Zanjan, Iran；Department of Computer Engineering University of Zanjan (ZNU) Zanjan, Iran
In medical diagnosis, physicians predict the state of a patient by checking measurements (features) obtained from a sequence of tests, e.g., blood test, urine test, followed by invasive tests. As tests are often costly, one would like to obtain only those features (tests) that can establish the presence or absence of the state conclusively. Another aspect of medical diagnosis is that we are often faced with unsupervised prediction tasks as the true state of the patients may not be known. Motivated by such medical diagnosis problems, we consider a {\it Cost-Sensitive Medical Diagnosis} (CSMD) p…
Medical automatic diagnosis aims to imitate human doctors in real-world diagnostic processes and to achieve accurate diagnoses by interacting with the patients. The task is formulated as a sequential decision-making problem with a series of symptom inquiring steps and the final diagnosis. Recent research has studied incorporating reinforcement learning for symptom inquiring and classification techniques for disease diagnosis, respectively. However, studies on efficiently and effectively combining the two procedures are still lacking. To address this issue, we devise an adaptive mechanism to al…
Globally, there is a substantial unmet need to diagnose various diseases effectively. The complexity of the different disease mechanisms and underlying symptoms of the patient population presents massive challenges to developing the early diagnosis tool and effective treatment. Machine Learning (ML), an area of Artificial Intelligence (AI), enables researchers, physicians, and patients to solve some of these issues. Based on relevant research, this review explains how Machine Learning (ML) and Deep Learning (DL) are being used to help in the early identification of numerous diseases. To begin,…
Vrije Universiteit（阿姆斯特丹自由大学）；Amsterdam UMC（阿姆斯特丹大学医学中心）；Amsterdam Neuroscience（阿姆斯特丹神经科学研究所）；Erasmus MC（伊拉斯姆斯医学中心）；University College London（伦敦大学学院）；TU Delft（代尔夫特理工大学）
Computer-aided methods have shown added value for diagnosing and predicting brain disorders and can thus support decision making in clinical care and treatment planning. This chapter will provide insight into the type of methods, their working, their input data - such as cognitive tests, imaging and genetic data - and the types of output they provide. We will focus on specific use cases for diagnosis, i.e. estimating the current 'condition' of the patient, such as early detection and diagnosis of dementia, differential diagnosis of brain tumours, and decision making in stroke. Regarding predic…
Pancreatic cancer is one of the leading causes of cancer-related death. Accurate detection, segmentation, and differential diagnosis of the full taxonomy of pancreatic lesions, i.e., normal, seven major types of lesions, and other lesions, is critical to aid the clinical decision-making of patient management and treatment. However, existing works focus on segmentation and classification for very specific lesion types (PDAC) or groups. Moreover, none of the previous work considers using lesion prevalence-related non-imaging patient information to assist the differential diagnosis. To this end, …
Knowledge Graph Sparsification for GNN-based Rare Disease Diagnosis
Department of Computer Science, Technical University of Munich（技术大学慕尼黑计算机科学系）；Munich Center for Machine Learning (MCML), Munich, Germany（慕尼黑机器学习中心（MCML））；Johns Hopkins University, Baltimore, Maryland, USA（约翰霍普金斯大学）
Erzincan Binali Yıldırım University（埃尔津詹比纳利耶尔德勒姆大学）；Petrozavodsk State University（彼得罗扎沃茨克国立大学）
Since February 2020, the world has been engaged in an intense struggle with the COVID-19 dis-ease, and health systems have come under tragic pressure as the disease turned into a pandemic. The aim of this study is to obtain the most effective routine blood values (RBV) in the diagnosis and prognosis of COVID-19 using a backward feature elimination algorithm for the LogNNet reservoir neural network. The first dataset in the study consists of a total of 5296 patients with the same number of negative and positive COVID-19 tests. The LogNNet-model achieved the accuracy rate of 99.5% in the diagnos…
The University of Sydney（悉尼大学）；Renmin University of China（中国人民大学）；Beijing Medicinovo Technology Co.,Ltd.（北京美迪诺沃科技有限公司）
One way to extract patterns from clinical records is to consider each patient record as a bag with various number of instances in the form of symptoms. Medical diagnosis is to discover informative ones first and then map them to one or more diseases. In many cases, patients are represented as vectors in some feature space and a classifier is applied after to generate diagnosis results. However, in many real-world cases, data is often of low-quality due to a variety of reasons, such as data consistency, integrity, completeness, accuracy, etc. In this paper, we propose a novel approach, attentio…
University of Alberta（阿尔伯塔大学）；Canadian VIGOUR Centre（加拿大VIGOUR中心）；Alberta Machine Intelligence Institute（阿尔伯塔机器智能研究所）
Electrocardiogram (ECG) abnormalities are linked to cardiovascular diseases, but may also occur in other non-cardiovascular conditions such as mental, neurological, metabolic and infectious conditions. However, most of the recent success of deep learning (DL) based diagnostic predictions in selected patient cohorts have been limited to a small set of cardiac diseases. In this study, we use a population-based dataset of >250,000 patients with >1000 medical conditions and >2 million ECGs to identify a wide range of diseases that could be accurately diagnosed from the patient's first in-hospital …
Sberbank Artificial Intelligence Laboratory（俄罗斯联邦储蓄银行人工智能实验室）；New York University（纽约大学）
In this paper we study the problem of predicting clinical diagnoses from textual Electronic Health Records (EHR) data. We show the importance of this problem in medical community and present comprehensive historical review of the problem and proposed methods. As the main scientific contributions we present a modification of Bidirectional Encoder Representations from Transformers (BERT) model for sequence classification that implements a novel way of Fully-Connected (FC) layer composition and a BERT model pretrained only on domain data. To empirically validate our model, we use a large-scale Ru…
Schizophrenia is a complex psychiatric disorder involving changes in thought patterns, perception, mood, and behavior. The diagnosis of schizophrenia is challenging and requires that patients show two or more positive symptoms for at least one month. Delays in identifying this debilitating disorder can impede a patient ability to receive much needed treatment. Advances in neuroimaging and machine learning algorithms can facilitate the diagnosis of schizophrenia and help clinicians to provide an accurate diagnosis of the disease. This paper presents a methodology for analyzing spectral images o…
Low grade endometrial stromal sarcoma (LGESS) is rare form of cancer, accounting for about 0.2% of all uterine cancer cases. Approximately 75% of LGESS patients are initially misdiagnosed with leiomyoma, which is a type of benign tumor, also known as fibroids. In this research, uterine tissue biopsy images of potential LGESS patients are preprocessed using segmentation and staining normalization algorithms. A variety of classic machine learning and leading deep learning models are then applied to classify tissue images as either benign or cancerous. For the classic techniques considered, the h…
Transformer representation learning is necessary for dynamic multi-modal physiological data on small-cohort patients
Nanjing University（南京大学）；The Affiliated Drum Tower Hospital of Nanjing University Medical School（南京大学医学院附属鼓楼医院）；National Laboratory of Solid State Microstructure（固体微结构物理国家重点实验室）
本文提出结合 Transformer 表征与传统机器学习的 POD 预测框架，利用多模态生理数据构建首个两类患者数据集，证明多模态 Transformer 表征可提升术后 1–3 天谵妄诊断性能。
Baylor College of Medicine（贝勒医学院）；Michael E. DeBakey VA Medical Center（迈克尔·E·德贝基退伍军人事务医疗中心）
The Early diagnosis and treatment of depression is essential for effective treatment. Depression, while being one of the most common mental illnesses, is still poorly understood in both research and clinical practice. Among different treatments, drug prescription is widely used, however the drug treatment is not effective for many patients. In this work, we propose a method for major depressive disorder (MDD) diagnosis as well as a method for predicting the drug response in patient with MDD using EEG signals. Method: We employ transformers, which are modified recursive neural networks with nov…
Shanghai Maritime University（上海海事大学）；University of Technology Sydney（悉尼科技大学）
More than 90% of the Parkinson Disease (PD) patients suffer from vocal disorders. Speech impairment is already indicator of PD. This study focuses on PD diagnosis through voiceprint features. In this paper, a method based on Deep Neural Network (DNN) recognition and classification combined with Mini-Batch Gradient Descent (MBGD) is proposed to distinguish PD patients from healthy people using voiceprint features. In order to exact the voiceprint features from patients, Weighted Mel Frequency Cepstrum Coefficients (WMFCC) is applied. The proposed method is tested on experimental data obtained b…
The COVID-19 pandemic has magnified an already existing trend of people looking for healthcare solutions online. One class of solutions are symptom checkers, which have become very popular in the context of COVID-19. Traditional symptom checkers, however, are based on manually curated expert systems that are inflexible and hard to modify, especially in a quickly changing situation like the one we are facing today. That is why all COVID-19 existing solutions are manual symptom checkers that can only estimate the probability of this disease and cannot contemplate alternative hypothesis or come u…
Understanding the temporal dynamics of COVID-19 patient phenotypes is necessary to derive fine-grained resolution of pathophysiology. Here we use state-of-the-art deep neural networks over an institution-wide machine intelligence platform for the augmented curation of 15.8 million clinical notes from 30,494 patients subjected to COVID-19 PCR diagnostic testing. By contrasting the Electronic Health Record (EHR)-derived clinical phenotypes of COVID-19-positive (COVIDpos, n=635) versus COVID-19-negative (COVIDneg, n=29,859) patients over each day of the week preceding the PCR testing date, we ide…
Causal prediction models for medication safety monitoring: The diagnosis of vancomycin-induced acute kidney injury
Amsterdam UMC（阿姆斯特丹大学医学中心）；University of Amsterdam（阿姆斯特丹大学）；Amsterdam Public Health（阿姆斯特丹公共卫生研究所）；Institute for Logic, Language and Computation（逻辑、语言与计算研究所）；Pacmed（Pacmed公司）
The Hebrew University of Jerusalem（耶路撒冷希伯来大学）；Hadassah Hebrew University Medical Center（哈达萨希伯来大学医学中心）；Sheba Medical Center（舍巴医学中心）；Tel Aviv University（特拉维夫大学）
Early diagnosis of sacroiliitis may lead to preventive treatment which can significantly improve the patient's quality of life in the long run. Oftentimes, a CT scan of the lower back or abdomen is acquired for suspected back pain. However, since the differences between a healthy and an inflamed sacroiliac joint in the early stages are subtle, the condition may be missed. We have developed a new automatic algorithm for the diagnosis and grading of sacroiliitis CT scans as incidental findings, for patients who underwent CT scanning as part of their lower back pain workout. The method is based o…
Tencent Jarvis Lab（腾讯Jarvis实验室）；Institute of Computing Technology Chinese Academy of Sciences（中国科学院计算技术研究所）；University of Chinese Academy of Sciences（中国科学院大学）；The First Affiliated Hospital of Anhui Medical University（安徽医科大学第一附属医院）；University of Science and Technology of China（中国科学技术大学）
Fast screening and diagnosis are critical in COVID-19 patient treatment. In addition to the gold standard RT-PCR, radiological imaging like X-ray and CT also works as an important means in patient screening and follow-up. However, due to the excessive number of patients, writing reports becomes a heavy burden for radiologists. To reduce the workload of radiologists, we propose DeltaNet to generate medical reports automatically. Different from typical image captioning approaches that generate reports with an encoder and a decoder, DeltaNet applies a conditional generation process. In particular…
HPOQuest: A Rare-Disease Diagnostic Agent Using Active Phenotype Acquisition
Technical University of Munich（慕尼黑工业大学）；Helmholtz Center Munich（亥姆霍兹慕尼黑中心）；Munich Center for Machine Learning (MCML)（慕尼黑机器学习中心）；Munich Data Science Institute (MDSI)（慕尼黑数据科学研究所）；Aalto University（阿尔托大学）
