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Extracting Integrated Features of Electronic Medical Records Big Data for Mortality and Phenotype Prediction

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Chinese Journal of Electronics 2024年第3期33卷 776-792页

作者： Fei LI Yiqiang CHEN Yang GU Yaowei WANG Institute of Computing Technology University of Chinese Academy of Sciences Pengcheng Laboratory

The key to synthesizing the features of electronic medical records(EMR) big data and using them for specific medical purposes,such as mortality and phenotype prediction,is to integrate the individual medical event and the overall multivariate time series feature extraction automatically,as well as to alleviate data imbalance *** paper provides a general feature extraction method to reduce manual intervention and automatically process large-scale *** processing uses two variational auto-encoders(VAEs) to automatically extract individual and global *** avoids the well-known posterior collapse problem of Transformer VAE through a uniquely designed "proportional and stabilizing" mechanism and forms a unique means to alleviate the data imbalance *** conducted experiments using ICU-STAY patients' data from the MIMIC-Ⅲ database and compared them with the mainstream EMR time series processing *** results show that the method extracts visible and comprehensive features,alleviates data imbalance problems and improves the accuracy in specific predicting tasks.

关键词： Databases time series analysis MIMICs Manuals Big Data Feature extraction Transformers

An introduction to multivariate probabilistic forecast evaluation

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Energy and AI 2021年第2期4卷 119-135页

作者： Mathias Blicher Bjerregard Jan Kloppenborg Møller Henrik Madsen Technical University of Denmark Department of Applied Mathematics and Computer Denmark

Probabilistic forecasting is becoming increasingly important for a wide range of applications,especially for en-ergy systems such as forecasting wind power production.A need for proper evaluation of probabilistic forecasts follows naturally with this,because evaluation is the key to improving the *** plenty of excellent reviews and research papers on probabilistic forecast evaluation already exist,we find that there is a need for an introduction with some practical *** particular,many forecast scenarios in energy systems are inher-ently multivariate,and while univariate evaluation methods are well understood and documented,only limited and scattered work has been done on their multivariate *** paper therefore contains a review of a selected set of probabilistic forecast evaluation methods,primarily scoring rules,as well as practical sections that explain how these methods can be calculated and *** three case studies featuring simple autore-gressive models,stochastic differential equations and real wind power data,we implement,apply and discuss the logarithmic score,the continuous ranked probability score and the variogram score for forecasting problems of varying ***,the advantages and disadvantages of the three scoring rules are highlighted,and this provides a significant step towards deciding on an evaluation method for a given multivariate forecast scenario including forecast scenarios relevant for energy systems.

关键词： Probabilistic forecast evaluation Multivariate scoring rules Wind power forecast Ensemble forecast time series analysis

Predicting cutaneous leishmaniasis using SARIMA and Markov switching models in Isfahan, Iran: A time-series study

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Asian Pacific Journal of Tropical Medicine 2021年第2期14卷 83-93页

作者： Vahid Rahmanian Saied Bokaie Aliakbar Haghdoost Mohsen Barouni Department of Food Hygiene and Quality Control Division of Epidemiology&ZoonosesFaculty of Veterinary MedicineUniversity of TehranTehranIran HIV/STI Surveillance Research Center and WHO Collaborating Center for HIV SurveillanceInstitute for Futures Studies in HealthKerman University of Medical SciencesKermanIran Health Services Management Research Center Institute for Futures Studies in HealthKerman University of Medical SciencesKermanIran

Objective:To determine the potential effect of environment variables on cutaneous leishmaniasis occurrence using time-series models and compare the predictive ability of seasonal autoregressive integrated moving average(SARIMA)models and Markov switching model(MSM).Methods:This descriptive study employed yearly and monthly data of 49364 parasitologically-confirmed cases of cutaneous leishmaniasis in Isfahan province,located in the center of Iran from January 2000 to December *** data were provided by the leishmaniasis national surveillance system,the meteorological organization of Isfahan province,and Iranian Space Agency for vegetation *** SARIMA and MSM models were implemented to examine the environmental factors of cutaneous leishmaniasis ***:The minimum relative humidity,maximum relative humidity,minimum wind speed,and maximum wind speed were significantly associated with cutaneous leishmaniasis epidemics in different lags(P<0.05).Comparing SARIMA and MSM,Akaikes information criterion(AIC),and mean absolute percentage error(MAPE)in MSM were much smaller than SARIMA models(MSM:AIC=0.95,MAPE=3.5%;SARIMA:AIC=158.93,MAPE:11.45%).Conclusions:SARIMA and MSM can be a useful tool for predicting cutaneous leishmaniasis in Isfahan *** cutaneous leishmaniasis falls into one of two states of epidemic and non-epidemic,the use of MSM(dynamic)is recommended,which can provide more information compared to models that use a single distribution for all observations(Box-Jenkins SARIMA model).

关键词： Leishmaniasis Climate factor time series analysis Forecasting Iran

Identification of non-linear autoregressive models with exogenous inputs for room air temperature modelling

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Energy and AI 2022年第3期9卷 78-87页

作者： Christian Ankerstjerne Thilker Peder Bacher Davide Cali Henrik Madsen Technical University of Denmark Department of Applied Mathematics and Computer ScienceAsmussens AlléBuilding 303BDK-2800 Kgs.LyngbyDenmark

This paper proposes non-linear autoregressive models with exogenous inputs to model the air temperature ineach room of a Danish school building connected to the local district heating network. To obtain satisfactorymodels, the authors find it necessary to estimate the solar radiation effect as a function of the time of the dayusing a B-spline basis expansion. Furthermore, this paper proposes a method for estimating the valve positionof the radiator thermostats in each room using modified Hermite polynomials to ensure monotonicity of theestimated curve. The non-linearities require a modification in the estimation procedure: Some parametersare estimated in an outer optimisation, while the usual regression parameters are estimated in an inneroptimisation. The models are able to simulate the temperature 24 h ahead with a root-mean-square-errorof the predictions between 0.25℃ and 0.6℃. The models seem to capture the solar radiation gain in away aligned with expectations. The estimated thermostatic valve functions also seem to capture the importantvariations of the individual room heat inputs.

关键词： time series analysis Non-linear models District heating Smart energy systems

The Motif Tracking Algorithm

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International Journal of Automation and computing 2008年第1期5卷 32-44页

作者： William Wilson Phil Birkin Uwe Aickelin School of Computer Science University of NottinghamNottingham NG8 1BBUK

The search for patterns or motifs in data represents a problem area of key interest to finance and economic researchers. In this paper, we introduce the motif tracking algorithm （MTA）, a novel immune inspired （IS） pattern identification tool that is able to identify unknown motifs of a non specified length which repeat within time series data. The power of the algorithm comes from the fact that it uses a small number of parameters with minimal assumptions regarding the data being examined or the underlying motifs. Our interest lies in applying the algorithm to financial time series data to identify unknown patterns that exist. The algorithm is tested using three separate data sets. Particular suitability to financial data is shown by applying it to oil price data. In all cases, the algorithm identifies the presence of a motif population in a fast and efficient manner due to the utilization of an intuitive symbolic representation. The resulting population of motifs is shown to have considerable potential value for other applications such as forecasting and algorithm seeding.

关键词： Motif detection repeating patterns time series analysis artificial immune systems immune memory

Feature selection for energy system modeling: Identification of relevant time series information

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Energy and AI 2021年第2期4卷 16-29页

作者： Inga M.Muller Chair of Renewable and Sustainable Energy Systems Technical University of MunichAccisstrasse 21Munich 80333Germany

Heuristic or clustering based time series aggregation methods are often used to reduce temporal complexity of energy system models by selecting representative ***,these methods potentially neglect relevant information of time series(e.g.,distribution parameters).To identify relevant time series parameters,feature selection algorithms can be *** present research contributes by(a)developing a new feature selection approach based on clustering,nested modeling and regression(CNR)which is designed for applications requiring high selectivity and using different data sets,(b)comparing and evaluating CNR with feature selection methods available from the literature(e.g.,LASSO)and(c)identifying relevant information of the time series applied in energy system models,in particular those of demand,photovoltaic and *** show that CNR achieves on average up to 101%lower mean absolute errors when methods are directly ***,CNR better identifies relevant information when the number of selected features is *** disadvantage of CNR,however,is its high computational effort.A potential remedy to counter this is the combination with another method(e.g.,as pre-feature selection).In terms of relevant information,energy systems including photovoltaic are mainly characterized by the correlation between demand and photovoltaic time series as well as the range and the 35%quantile of *** energy systems include wind power,the minimum and mean of wind as well as the correlation between demand and wind time series are relevant *** implications of these findings are discussed.

关键词： Energy system modeling Feature selection time series analysis Nested modeling Clustering Regression Intermittent renewable energies

Auto-Regressive Models of Non-Stationary time series with Finite Length

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Tsinghua Science and Technology 2005年第2期10卷 162-168页

作者：费万春白伦 Material Engineering College Soochow University Suzhou 215021 China Graduate School of Information Science and Technology the University of Tokyo Japan Material Engineering College Soochow University Suzhou 215021 China

To analyze and simulate non-stationary time series with finite length, the statistical characteris- tics and auto-regressive (AR) models of non-stationary time series with finite length are discussed and stud- ied. A new AR model called the time varying parameter AR model is proposed for solution of non-stationary time series with finite length. The auto-covariances of time series simulated by means of several AR models are analyzed. The result shows that the new AR model can be used to simulate and generate a new time series with the auto-covariance same as the original time series. The size curves of cocoon filaments re- garded as non-stationary time series with finite length are experimentally simulated. The simulation results are significantly better than those obtained so far, and illustrate the availability of the time varying parameter AR model. The results are useful for analyzing and simulating non-stationary time series with finite length.

关键词： time series analysis auto-covariance non-stationary auto-regressive model size curve of cocoon filament

Forecasting the number of zoonotic cutaneous leishmaniasis cases in south of Fars province, Iran using seasonal ARIMA time series method

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Asian Pacific Journal of Tropical Medicine 2017年第1期10卷 77-83页

作者： Mehdi Sharafi Haleh Ghaem Hamid Reza Tabatabaee Hossein Faramarzi Student Research Committee Shiraz University of Medical Sciences Research Center for Health Sciences Department of EpidemiologySchool of HealthShiraz University of Medical Sciences infectious diseases assistant professor of department of community medicine Medical School Shiraz University of Medical Sciences

Objective: To predict the trend of cutaneous leishmaniasis and assess the relationship between the disease trend and weather variables in south of Fars province using Seasonal Autoregressive Integrated Moving Average(SARIMA) model,Methods: The trend of cutaneous leishmaniasis was predicted using Mini tab software and SARIMA model,Besides,information about the disease and weather conditions was collected monthly based on time series design during January 2010 to March 2016,Moreover,various SARIMA models were assessed and the best one was selected,Then,the model's fitness was evaluated based on normality of the residuals' distribution,correspondence between the fitted and real amounts,and calculation of Akaike Information Criteria(AIC) and Bayesian Information Criteria(BIC),Results: The study results indicated that SARIMA model(4,1,4)(0,1,0)(12) in general and SARIMA model(4,1,4)(0,1,1)(12) in below and above 15 years age groups could appropriately predict the disease trend in the study area,Moreover,temperature with a three-month delay(lag3) increased the disease trend,rainfall with a four-month delay(lag4) decreased the disease trend,and rainfall with a nine-month delay(lag9) increased the disease trend,Conclusions: Based on the results,leishmaniasis follows a descending trend in the study area in case drought condition continues,SARIMA models can suitably measure the disease trend,and the disease follows a seasonal trend.

关键词： SARIMA model Zoonotic cutaneous leishmaniasis time series analysis

Simulation of groundwater table dynamics based on Feflow in the Minqin Basin,China

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Journal of Arid Land 2012年第2期4卷 123-131页

作者： Lan MA XiaoMei WEI AnMing BAO ShiFei WANG Key Laboratory of Water Sediment Sciences College of Water Sciences Beijing Normal University Beijing 100875 China College of Water Resources and Architectural Engineering Northwest Sci-Tech University of Agriculture and Forestry Yangling 712100 China State Key Laboratory of Desert and Oasis Ecology Xinjiang Institute of Ecology and Geography Chinese Academy of Sciences Urumqi 830011 China

As groundwater table declination is an important factor resulting in degradation of eco-environment in the Minqin Basin, China, it is significant to investigate and understand the groundwater table dynamics in this area. According to the physical and geographical conditions of the Minqin Basin, a hydrogeological conceptual model and a mathematical model were established, and the mathematical model was figured out by using Finite Element subsurface Flow system （Feflow）. Accurate hydrogeological parameters were acquired, and the spatio-temporal distribution dynamics of groundwater table for 1983-2001 were also simulated. The model performed well with a correlation coefficient of 0.977 and a mean error of 0.9768 m. The inflow and outflow of the groundwater system were predicted by time series analysis, and the groundwater table dynamics for 2011 were further acquired. Gen- erally the groundwater table in the Minqin Basin would continue to decline. The groundwater table would decline during spring and summer irrigation, while it would rise during autumn-winter irrigation. The groundwater depression cones would expand with the increase of center depths. Therefore, regulatory measures should be taken to prevent the declination of groundwater table and improve the eco-environment of this area.

关键词： Feflow time series analysis groundwater table dynamics simulation prediction