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Related Concept Videos

Survival Tree01:19

Survival Tree

Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
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Kaplan-Meier Approach01:24

Kaplan-Meier Approach

The Kaplan-Meier estimator is a non-parametric method used to estimate the survival function from time-to-event data. In medical research, it is frequently employed to measure the proportion of patients surviving for a certain period after treatment. This estimator is fundamental in analyzing time-to-event data, making it indispensable in clinical trials, epidemiological studies, and reliability engineering. By estimating survival probabilities, researchers can evaluate treatment effectiveness,...
Cluster Sampling Method01:20

Cluster Sampling Method

Appropriate sampling methods ensure that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
To choose a cluster sample, divide the population into clusters (groups) and then randomly select some of the clusters. All the members from these clusters are in the cluster sample. For example, if you randomly sample four departments from your...
Truncation in Survival Analysis01:09

Truncation in Survival Analysis

Truncation in survival analysis refers to the exclusion of individuals or events from the dataset based on specific criteria related to the time of the event. This exclusion can happen in two primary forms: left truncation and right truncation.
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Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

Pharmacokinetic models are mathematical constructs that represent and predict the time course of drug concentrations in the body, providing meaningful pharmacokinetic parameters. These models are categorized into compartment, physiological, and distributed parameter models.
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Model-Independent Approaches for Pharmacokinetic Data: Noncompartmental Analysis00:59

Model-Independent Approaches for Pharmacokinetic Data: Noncompartmental Analysis

Noncompartmental analyses offer an alternative method for describing drug pharmacokinetics without relying on a specific compartmental model. In this approach, the drug's pharmacokinetics are assumed to be linear, with the terminal phase log-linear. This assumption allows for simplified analysis and interpretation of the drug's behavior in the body.
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A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
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A Novel Approach for Handling Missing Data in Multivariate Time Series Clustering: Case Study on Predicting Delayed

Mihir Momaya1, Gyorgy Simon2, Sergio Duarte1

  • 1University of Florida, Gainesville, FL.

Studies in Health Technology and Informatics
|May 23, 2026
PubMed
Summary
This summary is machine-generated.

We introduce the Phasing and List-Splitting (PALS) technique for handling missing electronic health record (EHR) data, particularly for multivariate time series (MTS) clustering. PALS effectively addresses missing not at random data, improving imputation accuracy and clustering quality.

Keywords:
ClusteringDelayed Graft FunctionKidney TransplantsMissing EHR DataMultivariate Time Series DataUnsupervised Machine Learning

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Basics of Multivariate Analysis in Neuroimaging Data
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Basics of Multivariate Analysis in Neuroimaging Data

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Basics of Multivariate Analysis in Neuroimaging Data
06:35

Basics of Multivariate Analysis in Neuroimaging Data

Published on: July 24, 2010

Area of Science:

  • Data Science
  • Biostatistics
  • Health Informatics

Background:

  • Missing data is prevalent in electronic health record (EHR) data, often exhibiting Missing Not at Random (MNAR) patterns.
  • Existing imputation methods, including deep learning approaches, struggle with MNAR data and irregularly spaced multivariate time series (MTS).
  • Accurate handling of missing EHR data is crucial for reliable patient trajectory analysis and clustering.

Purpose of the Study:

  • To propose and evaluate the Phasing and List-Splitting (PALS) technique for handling irregularly spaced missing data in MTS.
  • To address the limitations of current imputation methods for MNAR data in EHR datasets.
  • To assess PALS's effectiveness in multivariate time series clustering applications, specifically for patient trajectory grouping.

Main Methods:

  • Developed the Phasing and List-Splitting (PALS) technique, prioritizing minimal imputation while preserving temporal alignment.
  • Compared PALS against existing imputation methods for handling missing MTS data.
  • Evaluated imputation accuracy and the quality of MTS clustering using patient trajectory data from kidney transplant recipients with delayed graft function.

Main Results:

  • PALS demonstrated superior performance in handling irregularly spaced missing data compared to existing methods.
  • The technique achieved high imputation accuracy, even with Missing Not at Random data patterns.
  • Clustering of patient trajectories using PALS-imputed data resulted in improved group quality, reflecting observable trajectory patterns.

Conclusions:

  • The Phasing and List-Splitting (PALS) technique offers a novel and effective solution for imputing missing data in multivariate time series, especially within the context of electronic health records.
  • PALS is particularly advantageous for applications like multivariate time series clustering where temporal alignment and handling of Missing Not at Random data are critical.
  • The method shows promise for improving the analysis of patient trajectories and enabling more accurate patient stratification in clinical research.