Extended vertical lists for temporal pattern mining from multivariate time series

Kocheturov, Anton; Momčilović, Petar; Pardalos, Pãnos M.

doi:10.1111/exsy.12448

Cited by 7 publications

(4 citation statements)

References 42 publications

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“…For both retrospective and prospective data sets, we developed extraction, transformation, and loading routines for converting native EHR formats to data standards, including the Observational Medical Outcomes Partnership common data model, RxNorm medication terminology from the National Library of Medicine, US Veterans Health Administration National Drug File reference terminology, and the Logical Observation Identifiers Names and Codes standards . For each patient’s medical record containing heterogeneous variables (eg, demographic characteristics and medical history, diagnoses and procedures, medications, laboratory results, and vital signs), we used several validated preprocessing algorithms for handling outliers, missing values, normalization, and resampling . We linked EHR data with US census data to ascertain social determinants of health and long-term mortality …”

Section: Methodsmentioning

confidence: 99%

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Performance of a Machine Learning Algorithm Using Electronic Health Record Data to Predict Postoperative Complications and Report on a Mobile Platform

et al. 2022

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IMPORTANCEPredicting postoperative complications has the potential to inform shared decisions regarding the appropriateness of surgical procedures, targeted risk-reduction strategies, and postoperative resource use. Realizing these advantages requires that accurate real-time predictions be integrated with clinical and digital workflows; artificial intelligence predictive analytic platforms using automated electronic health record (EHR) data inputs offer an intriguing possibility for achieving this, but there is a lack of high-level evidence from prospective studies supporting their use. OBJECTIVETo examine whether the MySurgeryRisk artificial intelligence system has stable predictive performance between development and prospective validation phases and whether it is feasible to provide automated outputs directly to surgeons' mobile devices. DESIGN, SETTING, AND PARTICIPANTSIn this prognostic study, the platform used automated EHR data inputs and machine learning algorithms to predict postoperative complications and provide predictions to surgeons, previously through a web portal and currently through a mobile device application. All patients 18 years or older who were admitted for any type of inpatient surgical

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Section: Methodsmentioning

confidence: 99%

“…resampling. 15,[21][22][23][24][25][26] We linked EHR data with US census data to ascertain social determinants of health and long-term mortality. 15,27…”

Section: Long-term Mortality Riskmentioning

confidence: 99%

Performance of a Machine Learning Algorithm Using Electronic Health Record Data to Predict Postoperative Complications and Report on a Mobile Platform

et al. 2022

Self Cite

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“…Recently, there are a lot of research applying deep learning to analyzing and predicting time series. Kocheturov et al [6] developed a new method for mining complex temporal patterns in multivariate time series. This study proposed the Extended Vertical List structure to track positions of the first state of the pattern inside records.…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Deep Learning Approach for PM2.5 Concentration Prediction in Smart Environmental Monitoring

Vo¹,

Vo²,

Bui³

et al. 2023

Intelligent Automation &Amp; Soft Computing

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Nowadays, air pollution is a big environmental problem in developing countries. In this problem, particulate matter 2.5 (PM2.5) in the air is an air pollutant. When its concentration in the air is high in developing countries like Vietnam, it will harm everyone's health. Accurate prediction of PM2.5 concentrations can help to make the correct decision in protecting the health of the citizen. This study develops a hybrid deep learning approach named PM25-CBL model for PM2.5 concentration prediction in Ho Chi Minh City, Vietnam. Firstly, this study analyzes the effects of variables on PM2.5 concentrations in Air Quality HCMC dataset. Only variables that affect the results will be selected for PM2.5 concentration prediction. Secondly, an efficient PM25-CBL model that integrates a convolutional neural network (CNN) and Bidirectional Long Short-Term Memory (Bi-LSTM) is developed. This model consists of three following modules: CNN, Bi-LSTM, and Fully connected modules. Finally, this study conducts the experiment to compare the performance of our approach and several state-of-the-art deep learning models for time series prediction such as LSTM, Bi-LSTM, the combination of CNN and LSTM (CNN-LSTM), and ARIMA. The empirical results confirm that PM25-CBL model outperforms other methods for Air Quality HCMC dataset in terms of several metrics including Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE).

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“…In other cases, new parallel algorithms have been proposed, for example using hybrid OpenMP-MPI [16], and parallel sequential pattern mining applied to so-called massive trajectory data [17]. For more examples of pattern mining algorithms and a list of platforms for which they have been adapted see [18][19][20][21][22].…”

Section: Introductionmentioning

confidence: 99%

Fast implementation of pattern mining algorithms with time stamp uncertainties and temporal constraints

Titarenko

Aivaliotis

et al. 2019

J Big Data

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Pattern mining is a powerful tool for analysing big datasets. Temporal datasets include time as an additional parameter. This leads to complexity in algorithmic formulation, and it can be challenging to process such data quickly and efficiently. In addition, errors or uncertainty can exist in the timestamps of data, for example in manually recorded health data. Sometimes we wish to find patterns only within a certain temporal range. In some cases real-time processing and decision-making may be desirable. All these issues increase algorithmic complexity, processing times and storage requirements. In addition, it may not be possible to store or process confidential data on public clusters or the cloud that can be accessed by many people. Hence it is desirable to optimise algorithms for standalone systems. In this paper we present an integrated approach which can be used to write efficient codes for pattern mining problems. The approach includes: (1) cleaning datasets with removal of infrequent events, (2) presenting a new scheme for time-series data storage, (3) exploiting the presence of prior information about a dataset when available, (4) utilising vectorisation and multicore parallelisation. We present two new algorithms, FARPAM (FAst Robust PAttern Mining) and FARPAMp (FARPAM with prior information about prior uncertainty, allowing faster searching). The algorithms are applicable to a wide range of temporal datasets. They implement a new formulation of the pattern searching function which reproduces and extends existing algorithms (such as SPAM and RobustSPAM), and allows for significantly faster calculation. The algorithms also include an option of temporal restrictions in patterns, which is available neither in SPAM nor in RobustSPAM. The searching algorithm is designed to be flexible for further possible extensions. The algorithms are coded in C++, and are highly optimised and parallelised for a modern standalone multicore workstation, thus avoiding security issues connected with transfers of confidential data onto clusters. FARPAM has been successfully tested on a publicly available weather dataset and on a confidential adult social care dataset, reproducing results obtained by previous algorithms in both cases. It has been profiled against the widely used SPAM algorithm (for sequential pattern mining) and RobustSPAM (developed for datasets with errors in time points). The algorithm outperforms SPAM by up to 20 times and RobustSPAM by up to 6000 times. In both cases the new algorithm has better scalability.

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Extended vertical lists for temporal pattern mining from multivariate time series

Cited by 7 publications

References 42 publications

Performance of a Machine Learning Algorithm Using Electronic Health Record Data to Predict Postoperative Complications and Report on a Mobile Platform

Performance of a Machine Learning Algorithm Using Electronic Health Record Data to Predict Postoperative Complications and Report on a Mobile Platform

A Hybrid Deep Learning Approach for PM2.5 Concentration Prediction in Smart Environmental Monitoring

Fast implementation of pattern mining algorithms with time stamp uncertainties and temporal constraints

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