A Label Attention Model for ICD Coding from Clinical Text

ICD coding is a process of assigning the International Classification of Disease diagnosis codes to clinical/medical notes documented by health professionals (e.g. clinicians). This process requires significant human resources, and thus is costly and prone to error. To handle the problem, machine learning has been utilized for automatic ICD coding. Previous state-of-the-art models were based on convolutional neural networks, using a single/several fixed window sizes. However, the lengths and interdependence between text fragments related to ICD codes in clinical text vary significantly, leading to the difficulty of deciding what the best window sizes are. In this paper, we propose a new label attention model for automatic ICD coding, which can handle both the various lengths and the interdependence of the ICD code related text fragments. Furthermore, as the majority of ICD codes are not frequently used, leading to the extremely imbalanced data issue, we additionally propose a hierarchical joint learning mechanism extending our label attention model to handle the issue, using the hierarchical relationships among the codes. Our label attention model achieves new state-of-the-art results on three benchmark MIMIC datasets, and the joint learning mechanism helps improve the performances for infrequent codes.Comment: In Proceedings of IJCAI 2020 (Main Track

The Current Status of Historical Preservation Law in Regularory Takings Jurisprudence: Has the Lucas Missile Dismantled Preservation Programs?

This paper describes our NIHRIO system for SemEval-2018 Task 3 "Irony detection in English tweets". We propose to use a simple neural network architecture of Multilayer Perceptron with various types of input features including: lexical, syntactic, semantic and polarity features.  Our system achieves very high performance in both subtasks of binary and multi-class irony detection in tweets. In particular, we rank at fifth in terms of the accuracy metric and the F1 metric. Our code is available at: https://github.com/NIHRIO/IronyDetectionInTwitte

A STUDY OF INDONESIA’S STOCK MARKET: HOW PREDICTABLE IS IT?

Using monthly data from January 1995 to December 2017, this paper tests whetherIndonesian stock index returns are predictable. In particular, we use eight macrovariables to predict the Indonesian composite and six sectoral index returns using thefeasible generalized least squares estimator. Our results suggest that the Indonesianstock index returns are predictable. However, the predictability depends not only onthe macro predictor used but also on the indexes examined. Second, we find that themost popular predictor is the exchange rate, followed by the interest rate. Finally, ourmain findings hold for a number of robustness tests.Using monthly data from January 1995 to December 2017, this paper tests whetherIndonesian stock index returns are predictable. In particular, we use eight macrovariables to predict the Indonesian composite and six sectoral index returns using thefeasible generalized least squares estimator. Our results suggest that the Indonesianstock index returns are predictable. However, the predictability depends not only onthe macro predictor used but also on the indexes examined. Second, we find that themost popular predictor is the exchange rate, followed by the interest rate. Finally, ourmain findings hold for a number of robustness tests

Factors Affecting Business Angels Investment in Vietnam

The paper aims at investigating and comparing the factors determining investment decisions by business angels (BAs) from the viewpoints of BAs and startups in Vietnam based on a framework synthesized from a literature review and primary data from in-depth interviews conducted with 8 startups and 15 angel investors. The results show that the startups’ founder, working team, financial issues, product and market, and strategy related to exit and the roles of BAs are startup-related factors determining BAs’ investment in Vietnam. For BA-related factors, the BAs’ experience, investment objectives and preferences, and culture are key determinants. The novelty of the paper is to find out the gaps between the perspectives of BAs and startups, and the difference between Vietnamese and foreign BAs’ viewpoints. The finding is that BAs, more strictly than startups, assess their business plan, financial state, product, market, and targeted consumers. Startups neglect the exit strategy and role of BAs in invested startups. In addition, foreign and domestic BAs have different opinions on startups’ market scale, and expectation of profits and BAs’ roles in startups. The paper ends by providing some implications for Vietnamese startups to attract more angel investment, focusing on improving the quality of human resources, developing a profitable, honest, and realistic business plan, and setting up a long-run vision towards the global market. Doi: 10.28991/ESJ-2023-07-02-07 Full Text: PD

The behaviour of U.S. public debt and deficits during the global financial crisis

In this paper we test the sustainability of U.S. public debt for the period 1916-2012 by analyzing how the primary surplus to gross domestic product (GDP) responds to changes in the debt to GDP ratio in a time-varying parameter model. Further, we determine the stationarity property of the debt/GDP ratio while accommodating possible breaks in the data caused by wars and economic crisis under both the null and alternative hypotheses of an endogenous unit root test. The results show that the U.S. public debt was sustainable until 2005 when the primary surplus to GDP reacted negatively to the debt/income ratio. This is further exacerbated during the global financial crisis when primary surpluses continued to fall with increased debt, thus jeopardizing the sustainability of fiscal policy. While the stationarity test shows that the U.S. fiscal debt/GDP ratio is sustainable, it fails to highlight the risk that its debt policy has been becoming unsustainable in recent years