The objective of this study is to develop a good risk model for classifying business delinquency by simultaneously exploring several machine learning based methods including regularization, hyper-parameter optimization, and model ensembling algorithms. The rationale under the analyses is firstly to obtain good base binary classifiers (include Logistic Regression ($LR$), K-Nearest Neighbors ($KNN$), Decision Tree ($DT$), and Artificial Neural Networks ($ANN$)) via regularization and appropriate settings of hyper-parameters. Then two model ensembling algorithms including bagging and boosting are performed on the good base classifiers for further model improvement. The models are evaluated using accuracy, Area Under the Receiver Operating Characteristic Curve (AUC of ROC), recall, and F1 score via repeating 10-fold cross-validation 10 times. The results show the optimal base classifiers along with the hyper-parameter settings are $LR$ without regularization, $KNN$ by using 9 nearest neighbors, $DT$ by setting the maximum level of the tree to be 7, and $ANN$ with three hidden layers. Bagging on $KNN$ with $K$ valued 9 is the optimal model we can get for risk classification as it reaches the average accuracy, AUC, recall, and F1 score valued 0.90, 0.93, 0.82, and 0.89, respectively.
One major obstacle that precludes the success of reinforcement learning in real-world applications is the lack of robustness, either to model uncertainties or external disturbances, of the trained policies. Robustness is critical when the policies are trained in simulations instead of real world environment. In this work, we propose a risk-aware algorithm to learn robust policies in order to bridge the gap between simulation training and real-world implementation. Our algorithm is based on recently discovered distributional RL framework. We incorporate CVaR risk measure in sample based distrib…
These are the proceedings of the 4th workshop on Machine Learning for the Developing World (ML4D), held as part of the Thirty-fourth Conference on Neural Information Processing Systems (NeurIPS) on Saturday, December 12th 2020.
Stanford University（斯坦福大学）；Institut du Cerveau - Paris Brain Institute - ICM（巴黎大脑研究所（ICM））
Risk prediction models using genetic data have seen increasing traction in genomics. However, most of the polygenic risk models were developed using data from participants with similar (mostly European) ancestry. This can lead to biases in the risk predictors resulting in poor generalization when applied to minority populations and admixed individuals such as African Americans. To address this bias, largely due to the prediction models being confounded by the underlying population structure, we propose a novel deep-learning framework that leverages data from diverse population and disentangles…
Objective: Machine learning algorithms are now widely used in predicting acute events for clinical applications. While most of such prediction applications are developed to predict the risk of a particular acute event at one hospital, few efforts have been made in extending the developed solutions to other events or to different hospitals. We provide a scalable solution to extend the process of clinical risk prediction model development of multiple diseases and their deployment in different Electronic Health Records (EHR) systems. Materials and Methods: We defined a generic process for clinica…
Université de Montréal（蒙特利尔大学）；Mila Québec Artificial Intelligence Institute（米拉魁北克人工智能研究所）；United Nations Global Pulse（联合国全球脉动）；Institute for Data Science, Durham University（杜伦大学数据科学研究所）；NYU Stern School of Business（纽约大学斯特恩商学院）；Global Coordination Mechanism on NCDs, WHO（世界卫生组织非传染性疾病全球协调机制）
The COVID-19 pandemic has been a major challenge to humanity, with 12.7 million confirmed cases as of July 13th, 2020 [1]. In previous work, we described how Artificial Intelligence can be used to tackle the pandemic with applications at the molecular, clinical, and societal scales [2]. In the present follow-up article, we review these three research directions, and assess the level of maturity and feasibility of the approaches used, as well as their potential for operationalization. We also summarize some commonly encountered risks and practical pitfalls, as well as guidelines and best practi…
More than 144 000 Australians were diagnosed with cancer in 2019. The majority will first present to their GP symptomatically, even for cancer for which screening programs exist. Diagnosing cancer in primary care is challenging due to the non-specific nature of cancer symptoms and its low prevalence. Understanding the epidemiology of cancer symptoms and patterns of presentation in patient's medical history from primary care data could be important to improve earlier detection and cancer outcomes. As past medical data about a patient can be incomplete, irregular or missing, this creates additio…
Developing A Fair Individualized Polysocial Risk Score (iPsRS) for Identifying Increased Social Risk of Hospitalizations in Patients with Type 2 Diabetes (T2D)
University of Florida（佛罗里达大学）；University of Florida College of Medicine（佛罗里达大学医学院）
University of Sheffield（谢菲尔德大学）；The Alan Turing Institute（艾伦·图灵研究所）
Gaining the ability to make informed decisions on operation and maintenance of structures provides motivation for the implementation of structural health monitoring (SHM) systems. However, descriptive labels for measured data corresponding to health-states of the monitored system are often unavailable. This issue limits the applicability of fully-supervised machine learning paradigms for the development of statistical classifiers to be used in decision-support in SHM systems. One approach to dealing with this problem is risk-based active learning. In such an approach, data-label querying is gu…
Fragility-aware Classification for Risk Characterization and Robust Learning
School of Management, Zhejiang University（浙江大学管理学院）；Department of Systems Engineering and Engineering Management, The Chinese University of Hong Kong（香港中文大学系统工程与工程管理系）；Department of Industrial Engineering and Decision Analytics, Hong Kong University of Science and Technology（香港科技大学工业工程与决策分析系）；Department of Marketing and Analytics, University College London（伦敦大学学院市场营销与分析系）
Reinforcement learning (RL) has achieved remarkable performance in numerous sequential decision making and control tasks. However, a common problem is that learned nearly optimal policy always overfits to the training environment and may not be extended to situations never encountered during training. For practical applications, the randomness of environment usually leads to some devastating events, which should be the focus of safety-critical systems such as autonomous driving. In this paper, we introduce the minimax formulation and distributional framework to improve the generalization abili…
Woven Planet United Kingdom Limited（Woven Planet英国有限公司）；Woven Planet North America, Inc.（Woven Planet北美公司）
The imitation learning of self-driving vehicle policies through behavioral cloning is often carried out in an open-loop fashion, ignoring the effect of actions to future states. Training such policies purely with Empirical Risk Minimization (ERM) can be detrimental to real-world performance, as it biases policy networks towards matching only open-loop behavior, showing poor results when evaluated in closed-loop. In this work, we develop an efficient and simple-to-implement principle called Closed-loop Weighted Empirical Risk Minimization (CW-ERM), in which a closed-loop evaluation procedure is…
What if? Causal Machine Learning in Supply Chain Risk Management
Poznań University of Economics and Business（波兹南经济与商业大学）；University of Huddersfield（哈德斯菲尔德大学）；University of Cambridge（剑桥大学）；Alan Turing Institute（阿兰·图灵研究所）
University of Johannesburg（约翰内斯堡大学）；Council for Scientific and Industrial Research (CSIR)（科学与工业研究理事会（CSIR））
The Ripper algorithm is designed to generate rule sets for large datasets with many features. However, it was shown that the algorithm struggles with classification performance in the presence of missing data. The algorithm struggles to classify instances when the quality of the data deteriorates as a result of increasing missing data. In this paper, a feature selection technique is used to help improve the classification performance of the Ripper model. Principal component analysis and evidence automatic relevance determination techniques are used to improve the performance. A comparison is d…
Improving Colorectal Cancer Screening and Risk Assessment through Predictive Modeling on Medical Images and Records
Geisel School of Medicine at Dartmouth（达特茅斯盖泽尔医学院）；Dartmouth-Hitchcock Medical Center（达特茅斯-希区柯克医学中心）；New Hampshire Colonoscopy Registry（新罕布什尔结肠镜检查登记处）；White River Junction VA Medical Center（怀特河交界处退伍军人事务医疗中心）；Dartmouth College（达特茅斯学院）
Improving Early Prediction of Type 2 Diabetes Mellitus with ECG-DiaNet: A Multimodal Neural Network Leveraging Electrocardiogram and Clinical Risk Factors
College of Science and Engineering, Hamad Bin Khalifa University（哈马德·本·哈利法大学科学与工程学院）
Amundi Asset Management（东方汇理资产管理公司）；Ecole des Ponts ParisTech（巴黎高科路桥学校）
This article explores the use of machine learning models to build a market generator. The underlying idea is to simulate artificial multi-dimensional financial time series, whose statistical properties are the same as those observed in the financial markets. In particular, these synthetic data must preserve the probability distribution of asset returns, the stochastic dependence between the different assets and the autocorrelation across time. The article proposes then a new approach for estimating the probability distribution of backtest statistics. The final objective is to develop a framewo…
It is broadly known that deep neural networks are susceptible to being fooled by adversarial examples with perturbations imperceptible by humans. Various defenses have been proposed to improve adversarial robustness, among which adversarial training methods are most effective. However, most of these methods treat the training samples independently and demand a tremendous amount of samples to train a robust network, while ignoring the latent structural information among these samples. In this work, we propose a novel Local Structure Preserving (LSP) regularization, which aims to preserve the lo…
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）
Improving Generalizability of Hip Fracture Risk Prediction via Domain Adaptation Across Multiple Cohorts
Michigan Technological University（密歇根理工大学）；Grand Valley State University（大峡谷州立大学）；Kennesaw State University（肯尼索州立大学）；University of California, Irvine（加州大学欧文分校）；UC Davis Health（加州大学戴维斯分校健康中心）；Geisel School of Medicine, Dartmouth College（达特茅斯学院盖泽尔医学院）；Tulane University（杜兰大学）
Improving Hospital Risk Prediction with Knowledge-Augmented Multimodal EHR Modeling
Department of Computer Science, University of Virginia（大学计算机科学系）；University of Virginia School of Medicine（弗吉尼亚大学医学院）；Virginia Polytechnic Institute and State University（弗吉尼亚理工学院和州立大学）；Biocomplexity Institute and Initiative, University of Virginia（大学生物复杂性研究所）；Division of Infectious Diseases & International Health, University of Virginia School of Medicine（大学感染性疾病与国际卫生分会）
Risk-aware Classification via Uncertainty Quantification
Amazon Alexa AI（亚马逊Alexa人工智能）；DEVCOM Army Research Laboratory（DEVCOM陆军研究实验室）；University College London（伦敦大学学院）；Fordham University（福特汉姆大学）；University of Brescia（布雷西亚大学）；Cardiff University（卡迪夫大学）
