scholarly journals Using patient biomarker time series to determine mortality risk in hospitalised COVID-19 patients: a comparative analysis across two New York hospitals

2021 ◽  
Author(s):  
Ben Lambert ◽  
Isaac J. Stopard ◽  
Amir Momeni-Boroujeni ◽  
Rachelle Mendoza ◽  
Alejandro Zuretti
Keyword(s):  
New York ◽  
Time Series ◽  
Mortality Risk ◽  
Time Series Data ◽  
Medical Center ◽  
New York State ◽  
Prognostic Models ◽  
Series Data ◽  
Clinical Measurements ◽  
The Individual

AbstractA large range of prognostic models for determining the risk of COVID-19 patient mortality exist, but these typically restrict the set of biomarkers considered to measurements available at patient admission. Additionally, many of these models are trained and tested on patient cohorts from a single hospital, raising questions about the generalisability of results. We used a Bayesian Markov model to analyse time series data of biomarker measurements taken throughout the duration of a COVID-19 patient’s hospitalisation for n = 1540 patients from two hospitals in New York: State University of New York (SUNY) Downstate Health Sciences University and Maimonides Medical Center. In doing so, we quantified the mortality risk associated with both static (e.g. demographic and patient history variables) and dynamic factors (e.g. changes in biomarkers) throughout hospitalisation. By using our model to make predictions across the hospitals, we assessed how predictive factors generalised between the two cohorts. The individual dynamics of the measurements and their associated mortality risk were remarkably consistent across the hospitals. The model accuracy in predicting patient outcome (death or discharge) was 72.3% (predicting SUNY; posterior median accuracy) and 71.4% (predicting Maimonides) respectively. Model sensitivity was higher for detecting patients who would go on to be discharged (79.2%) versus those who died (61.0%). Our results indicate the utility of including dynamic clinical measurements when assessing patient mortality risk but also highlight the difficulty of identifying high risk patients.

Producers' Response to Changes in Prices and Marketing Policies: A Case Study of Sugarcane and Paddy in Eastern U.P. by S. C. Gupta and A. Majid. New York: Asia Publishing House, 1965.

1968 ◽  
Vol 8 (2) ◽  
pp. 308-309
Author(s):  
Mohammad Irshad Khan
Keyword(s):  
New York ◽  
Time Series ◽  
Time Series Data ◽  
Publishing House ◽  
Economic Incentives ◽  
The Other ◽  
Series Data ◽  
Poor Countries ◽  
Northern India

It is alleged that the agricultural output in poor countries responds very little to movements in prices and costs because of subsistence-oriented produc¬tion and self-produced inputs. The work of Gupta and Majid is concerned with the empirical verification of the responsiveness of farmers to prices and marketing policies in a backward region. The authors' analysis of the respon¬siveness of farmers to economic incentives is based on two sets of data (concern¬ing sugarcane, cash crop, and paddy, subsistence crop) collected from the district of Deoria in Eastern U.P. (Utter Pradesh) a chronically foodgrain deficit region in northern India. In one set, they have aggregate time-series data at district level and, in the other, they have obtained data from a survey of five villages selected from 170 villages around Padrauna town in Deoria.

scholarly journals Bernoulli Time Series Modelling with Application to Accommodation Tourism Demand

2021 ◽  
Vol 5 (1) ◽  
pp. 17
Author(s):  
Miguel Ángel Ruiz Reina
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Prognostic Models ◽  
Series Data ◽  
Economic Agents ◽  
Time Series Modelling ◽  
Simulation And Training ◽  
Uncertainty Method ◽  
And Training ◽  
Hotel Accommodation

In this research, a new uncertainty method has been developed and applied to forecasting the hotel accommodation market. The simulation and training of Time Series data are from January 2001 to December 2018 in the Spanish case. The Log-log BeTSUF method estimated by GMM-HAC-Newey-West is considered as a contribution for measuring uncertainty vs. other prognostic models in the literature. The results of our model present better indicators of the RMSE and Ratio Theil’s for the predictive evaluation period of twelve months. Furthermore, the straightforward interpretation of the model and the high descriptive capacity of the model allow economic agents to make efficient decisions.

scholarly journals ANALISIS DAMPAK KEPEMILIKAN SURAT BERHARGA OLEH BPD TERHADAP PERTUMBUHAN EKONOMI DAERAH

2018 ◽  
Vol 14 (1) ◽  
pp. 32-47
Author(s):  
Khairur dan Telisa Aulia Falian Raziqiin ◽  
Telisa Aulia Falian
Keyword(s):  
Economic Growth ◽  
New York ◽  
Time Series ◽  
Regional Development ◽  
Time Series Data ◽  
Financial Institution ◽  
Positive Influence ◽  
Regional Economic Growth ◽  
Series Data ◽  
Regional Economic

Local government-owned banks (BPD), was established in order to help accelerate the development of the area where the BPD located. The expected goals of this study are: To measure the effect of the placement of funds by BPD on regional economic growth, to measure investment lending by BPD to regional economic growth. Population was all the existing Regional Development Bank in Indonesia. Based on data from Bank Indonesia, the number of regional development banks perDesember 2013 as many as 26 banks. The type of data that will be used in this research is time series data (time series) from January 2009 until December 2013 The model that will be used in this research is the use of panel data. Results of research on Analysis of Impact of Ownership of Securities by BPD Against Regional Development, government capital spending, credit productive, ownership of securities by BPD positive effect on GDP, and significantly affect GDP, labor force have a positive influence on the GDP, but the effect was not significant workforce to GDP.Badan Pusat Statistik. Berbagai tahun. Data Realisasi APBD. Badan PusatStatistik, Jakarta. Bank Indonesia. Berbagai tahun. Laporan Publikasi Bank Umum. Bank Indonesia,Jakarta. Budiono. (2001). Ekonomi Moneter Edisi 3. Yogyakarta : BPFE Djojosubroto, Dono Iskandar. (2004). Koordinasi Kebijakan Fiskal dan Moneter di Indonesia Pasca Undang – undang Bank Indonesia 1999. Jakarta : Kompas Dornbusch, Rudiger, Stanley Fischer, Richard Startz. (2004). Makroekonomi. (Yusuf Wibisono, Roy Indra Mirazudin, terjemahan). Jakarta :MediaGlobal Edukasi. Gujarati, Damodar. (1997). Ekonometrika Dasar. (Sumarno Zein, terjemahan).Jakarta : Erlangga. Gultom, Lukdir. (2013). Tantangan Meningkatkan Efisiensi dan Efektifitas BPD sebagai Regional Champion Dalam Pengembangan Usaha Mikro, Kecil dan Menengah di Indonesia, Makalah SESPIBI Angkatan XXXI (Tidak Dipublikasikan). Bank Indonesia. Husnan, Suad. (2003). Dasar – dasar Teori Portofolio dan Analisis Sekuritas.Yogyakarta : UPP AMP YKPN. Kasmir. (2002). Dasar – Dasar Perbankan. Jakarta : PT. Raja Grafindo Persada. Kuncoro, Mudrajad. (2001) Metode Kuantitatif : Teori dan Aplikasi untuk Bisnis dan Ekonomi. Yogyakarta : AMP YKPN. Latumaerissa dan Julius R. (1999). Mengenal Aspek-aspek Operasi Bank Umum. Jakarta : Bumi Aksara. Lipsey, Richard G, et al. (1997). Pengantar Makro Ekonomi. ( Jaka Wasana danKibrandoko, terjemahan). Jakarta :Binarupa Aksara. Mankiw, Gregory. (2000). Macroeconomics Theory. New York : Worth PublisherInc. Nachrowi, Nachrowi D., Hardius Usman. (2006). Pendekatan Populer dan Praktis EKONOMETRIKA untuk Analisis Ekonomi dan Keuangan.Jakarta : Lembaga Penerbit FEUI. Rahmany, A. Fuad. (2004). Era Baru Kebijakan Fiskal : Pemikiran, Konsep dan Implementasi. Jakarta : Penerbit Buku Kompas, hal. 445 – 462. Rivai, Veithzal, Andria Permata Veithzal, Ferry N. Idroes. (2007). Bank and Financial Institution Management : Conventional & Sharia System, Jakarta : RajaGrafindo Persada. Sunarsip. (2008). Relasi Bank Pembangunan Daerah dan Perekonomian Daerah, dimuat dalam Republika, Rabu, 9 Januari 2008. Rubrik Pareto hal.16 Sunarsip. (2011). Transformasi BPD. Dimuat Infobank Edisi Januari 2011. Republik Indonesia, Kementrian Keuangan (2010), Potensi Bank Pembangunan Daerah Sebagai Pendiri Dana Pensiun Lembaga Keuangan,Tim Studi Potensi Bank Pembangunan Daerah Sebagai Pendiri Dana Pensiun. Jakarta.Waluyanto, Rahmat. (2004). Era Baru Kebijakan Fiskal : Pemikiran, Konsep dan Implementasi. Jakarta : Penerbit Buku Kompas, hal. 463 – 508. Wuryandari, Gantiah. (2013). Mengusung Bank Pembangunan Daerah (BPD) Sebagai Bank Fokus Sektor Strategis Dalam Mendukung Pembangunan Nasional, Makalah SESPIBI Angkatan XXXI (Tidak Dipublikasikan). Bank Indonesia.

scholarly journals Fractal Complexity of Daily Physical Activity Patterns Differs With Age Over the Life Span and Is Associated With Mortality in Older Adults

2018 ◽  
Vol 74 (9) ◽  
pp. 1461-1467 ◽  
Author(s):  
David A Raichlen ◽  
Yann C Klimentidis ◽  
Chiu-Hsieh Hsu ◽  
Gene E Alexander
Keyword(s):  
Physical Activity ◽  
Older Adults ◽  
Time Series ◽  
Time Scales ◽  
Mortality Risk ◽  
Time Series Data ◽  
Series Data ◽  
Fluctuation Analysis ◽  
Wide Range ◽  
Wearable Accelerometers

Abstract Background Accelerometers are included in a wide range of devices that monitor and track physical activity for health-related applications. However, the clinical utility of the information embedded in their rich time-series data has been greatly understudied and has yet to be fully realized. Here, we examine the potential for fractal complexity of actigraphy data to serve as a clinical biomarker for mortality risk. Methods We use detrended fluctuation analysis (DFA) to analyze actigraphy data from the National Health and Nutrition Examination Survey (NHANES; n = 11,694). The DFA method measures fractal complexity (signal self-affinity across time-scales) as correlations between the amplitude of signal fluctuations in time-series data across a range of time-scales. The slope, α, relating the fluctuation amplitudes to the time-scales over which they were measured describes the complexity of the signal. Results Fractal complexity of physical activity (α) decreased significantly with age (p = 1.29E−6) and was lower in women compared with men (p = 1.79E−4). Higher levels of moderate-to-vigorous physical activity in older adults and in women were associated with greater fractal complexity. In adults aged 50–79 years, lower fractal complexity of activity (α) was associated with greater mortality (hazard ratio = 0.64; 95% confidence interval = 0.49–0.82) after adjusting for age, exercise engagement, chronic diseases, and other covariates associated with mortality. Conclusions Wearable accelerometers can provide a noninvasive biomarker of physiological aging and mortality risk after adjusting for other factors strongly associated with mortality. Thus, this fractal analysis of accelerometer signals provides a novel clinical application for wearable accelerometers, advancing efforts for remote monitoring of physiological health by clinicians.

scholarly journals Peramalan Data Runtun Waktu menggunakan Model Hybrid Time Series Regression – Autoregressive Integrated Moving Average

2021 ◽  
Vol 2 (1) ◽  
Author(s):  
Melisa Arumsari ◽  
◽  
Andrea Dani ◽  
Keyword(s):  
Time Series ◽  
Hybrid Method ◽  
Time Series Data ◽  
Moving Average ◽  
Series Data ◽  
Time Series Regression ◽  
Accurate Forecast ◽  
Using Data ◽  
The Individual ◽  
Time Series Data Analysis

Forecasting is a method used to estimate or predict a value in the future using data from the past. With the development of methods in time series data analysis, a hybrid method was developed in which a combination of several models was carried out in order to produce a more accurate forecast. The purpose of this study was to determine whether the TSR-ARIMA hybrid method has a better level of accuracy than the individual TSR method so that more accurate forecasting results are obtained. The data in this study are monthly data on the number of passengers on American airlines for the period January 1949 to December 1960. Based on the analysis, the TSR-ARIMA hybrid method produces a MAPE of 3,061% and the TSR method produces an MAPE of 7,902%.

scholarly journals Learning to Predict in-hospital mortality risk in intensive care unit with attention-based Temporal convolution network

2020 ◽  
Author(s):  
Yu-wen Chen ◽  
Yu-jie Li ◽  
Zhi-yong Yang ◽  
Kun-hua Zhong ◽  
Li-ge Zhang ◽  
...  
Keyword(s):  
Time Series ◽  
Mortality Risk ◽  
Time Series Data ◽  
Risk Model ◽  
Series Data ◽  
Dynamic Prediction ◽  
Physiological Variables ◽  
Prediction Of Mortality ◽  
Low Sensitivity ◽  
Mimic Iii

Abstract Background: Dynamic prediction of patients’ mortality risk in ICU with time series data is limited due to the high dimensionality, uncertainty with sampling intervals, and other issues. New deep learning method, temporal convolution network (TCN), makes it possible to deal with complex clinical time series data in ICU. We aimed to develop and validate it to predict mortality risk using time series data from MIMIC III dataset. Methods: 21139 records of ICU stays were analyzed and in total 17 physiological variables from the MIMIC III dataset were used to predict mortality risk. Then we compared the model performances of attention-based TCN with traditional artificial intelligence (AI) method. Results: Area Under Receiver Operating Characteristic (AUCROC) and Area Under Precision-Recall curve (AUC-PR) of attention-based TCN for predicting the mortality risk 48h after ICU admission were 0.837(0.824 -0.850) and 0.454. The sensitivity and specificity of attention-based TCN were 67.1% and 82.6%, compared to the traditional AI method yield low sensitivity (<50%). Conclusions: Attention-based TCN model achieved better performance in prediction of mortality risk with time series data than traditional AI methods and conventional score-based models. Attention-based TCN mortality risk model has the potential for helping decision-making in critical patients.

scholarly journals Adaptive Synapse Arrangement in Cortical Learning Algorithm

2021 ◽  
Vol 25 (4) ◽  
pp. 450-466
Author(s):  
Takeru Aoki ◽  
◽  
Keiki Takadama ◽  
Hiroyuki Sato
Keyword(s):  
New York ◽  
Time Series ◽  
Prediction Accuracy ◽  
Input Data ◽  
Time Series Data ◽  
Learning Algorithm ◽  
Prediction Method ◽  
Series Data ◽  
Load Prediction ◽  
Data Prediction

The cortical learning algorithm (CLA) is a time-series data prediction method that is designed based on the human neocortex. The CLA has multiple columns that are associated with the input data bits by synapses. The input data is then converted into an internal column representation based on the synapse relation. Because the synapse relation between the columns and input data bits is fixed during the entire prediction process in the conventional CLA, it cannot adapt to input data biases. Consequently, columns not used for internal representations arise, resulting in a low prediction accuracy in the conventional CLA. To improve the prediction accuracy of the CLA, we propose a CLA that self-adaptively arranges the column synapses according to the input data tendencies and verify its effectiveness with several artificial time-series data and real-world electricity load prediction data from New York City. Experimental results show that the proposed CLA achieves higher prediction accuracy than the conventional CLA and LSTMs with different network optimization algorithms by arranging column synapses according to the input data tendency.

scholarly journals Dynamic predict in-hospital mortality risk in intensive care unit with a new deep learning of artificial intelligence

2020 ◽  
Author(s):  
Yu-wen Chen ◽  
Yu-jie Li ◽  
Zhi-yong Yang ◽  
Kun-hua Zhong ◽  
Li-ge Zhang ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Time Series ◽  
Deep Learning ◽  
Mortality Risk ◽  
Time Series Data ◽  
Risk Model ◽  
Series Data ◽  
Physiological Variables ◽  
Prediction Of Mortality ◽  
Mimic Iii

Abstract Background Dynamic prediction of patients’ mortality risk in ICU with time series data is limited due to the high dimensionality, uncertainty with sampling intervals, and other issues. New deep learning method, temporal convolution network (TCN), makes it possible to deal with complex clinical time series data in ICU. We aimed to develop and validate it to predict mortality risk using time series data from MIMIC III dataset. Methods Finally, 21139 records of ICU stays were analyzed and in total 17 physiological variables from the MIMIC III dataset were used to predict mortality risk. Then we compared the model performances of attention-based TCN with traditional artificial intelligence (AI) method. Results The Area Under Receiver Operating Characteristic (AUCROC) and Area Under Precision-Recall curve (AUC-PR) of attention-based TCN for predicting the mortality risk 48 h after ICU admission were 0.837(0.824–0.850) and 0.454. The sensitivity and specificity of attention-based TCN were 67.1% and 82.6%, compared to the traditional AI method yield low sensitivity (< 50%). Conclusions Attention-based TCN model achieved better performance in prediction of mortality risk with time series data than traditional AI methods and conventional score-based models. Attention-based TCN mortality risk model has the potential for helping decision-making in critical patients.

scholarly journals Model-based forecasting for Canadian COVID-19 data

PLoS ONE ◽  
2021 ◽  
Vol 16 (1) ◽  
pp. e0244536
Author(s):  
Li-Pang Chen ◽  
Qihuang Zhang ◽  
Grace Y. Yi ◽  
Wenqing He
Keyword(s):  
New York ◽  
Time Series Data ◽  
New York State ◽  
Smooth Transition ◽  
World Health ◽  
Series Data ◽  
Short Term ◽  
Model Based ◽  
Star Models ◽  
The Impact

Background Since March 11, 2020 when the World Health Organization (WHO) declared the COVID-19 pandemic, the number of infected cases, the number of deaths, and the number of affected countries have climbed rapidly. To understand the impact of COVID-19 on public health, many studies have been conducted for various countries. To complement the available work, in this article we examine Canadian COVID-19 data for the period of March 18, 2020 to August 16, 2020 with the aim to forecast the dynamic trend in a short term. Method We focus our attention on Canadian data and analyze the four provinces, Ontario, Alberta, British Columbia, and Quebec, which have the most severe situations in Canada. To build predictive models and conduct prediction, we employ three models, smooth transition autoregressive (STAR) models, neural network (NN) models, and susceptible-infected-removed (SIR) models, to fit time series data of confirmed cases in the four provinces separately. In comparison, we also analyze the data of daily infections in two states of USA, Texas and New York state, for the period of March 18, 2020 to August 16, 2020. We emphasize that different models make different assumptions which are basically difficult to validate. Yet invoking different models allows us to examine the data from different angles, thus, helping reveal the underlying trajectory of the development of COVID-19 in Canada. Finding The examinations of the data dated from March 18, 2020 to August 11, 2020 show that the STAR, NN, and SIR models may output different results, though the differences are small in some cases. Prediction over a short term period incurs smaller prediction variability than over a long term period, as expected. The NN method tends to outperform other two methods. All the methods forecast an upward trend in all the four Canadian provinces for the period of August 12, 2020 to August 23, 2020, though the degree varies from method to method. This research offers model-based insights into the pandemic evolvement in Canada.

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