Using topological data analysis and pseudo time series to infer temporal phenotypes from electronic health records

AbstractIn developmental biology as well as in other biological systems, emerging structure and organization can be captured using time-series data of protein locations. In analyzing this time-dependent data, it is a common challenge not only to determine whether topological features emerge, but also to identify the timing of their formation. For instance, in most cells, actin filaments interact with myosin motor proteins and organize into polymer networks and higher-order structures. Ring channels are examples of such structures that maintain constant diameters over time and play key roles in processes such as cell division, development, and wound healing. Given the limitations in studying interactions of actin with myosin in vivo, we generate time-series data of protein polymer interactions in cells using complex agent-based models. Since the data has a filamentous structure, we propose sampling along the actin filaments and analyzing the topological structure of the resulting point cloud at each time. Building on existing tools from persistent homology, we develop a topological data analysis (TDA) method that assesses effective ring generation in this dynamic data. This method connects topological features through time in a path that corresponds to emergence of organization in the data. In this work, we also propose methods for assessing whether the topological features of interest are significant and thus whether they contribute to the formation of an emerging hole (ring channel) in the simulated protein interactions. In particular, we use the MEDYAN simulation platform to show that this technique can distinguish between the actin cytoskeleton organization resulting from distinct motor protein binding parameters.

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Time series classification via topological data analysis

Expert Systems with Applications ◽

10.1016/j.eswa.2021.115326 ◽

2021 ◽

pp. 115326

Author(s):

Alperen Karan ◽

Atabey Kaygun

Keyword(s):

Time Series ◽

Data Analysis ◽

Topological Data Analysis ◽

Time Series Classification ◽

Topological Data

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Multimodal Data Analysis and Visualization to Study the Usage of Electronic Health Records

Proceedings of the ICTs for improving Patients Rehabilitation Research Techniques ◽

10.4108/icst.pervasivehealth.2013.252025 ◽

2013 ◽

Cited By ~ 1

Author(s):

Nadir Weibel ◽

Shazia Ashfaq ◽

Alan Calvitti ◽

James Hollan ◽

Zia Agha

Keyword(s):

Data Analysis ◽

Electronic Health Records ◽

Health Records ◽

Multimodal Data ◽

Electronic Health ◽

Multimodal Data Analysis

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Inferring Temporal Phenotypes with Topological Data Analysis and Pseudo Time-Series

Artificial Intelligence in Medicine - Lecture Notes in Computer Science ◽

10.1007/978-3-030-21642-9_50 ◽

2019 ◽

pp. 399-409 ◽

Cited By ~ 1

Author(s):

Arianna Dagliati ◽

Nophar Geifman ◽

Niels Peek ◽

John H. Holmes ◽

Lucia Sacchi ◽

...

Keyword(s):

Time Series ◽

Data Analysis ◽

Topological Data Analysis ◽

Topological Data

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Topological data analysis of financial time series: Landscapes of crashes

Physica A Statistical Mechanics and its Applications ◽

10.1016/j.physa.2017.09.028 ◽

2018 ◽

Vol 491 ◽

pp. 820-834 ◽

Cited By ~ 33

Author(s):

Marian Gidea ◽

Yuri Katz

Keyword(s):

Time Series ◽

Data Analysis ◽

Financial Time Series ◽

Topological Data Analysis ◽

Financial Time ◽

Topological Data

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Sexually Transmitted Infections, the Epidemic That Persists After the COVID-19 Pandemic: a Time-series Analysis of the Primary Care Electronic Health Records Covering About 5 Million People in Catalonia

10.21203/rs.3.rs-871742/v1 ◽

2021 ◽

Author(s):

Carolina Guiriguet ◽

Mireia Alberny ◽

Ermengol Coma ◽

Carme Roca ◽

Francesc Fina ◽

...

Keyword(s):

Primary Care ◽

Time Series ◽

Electronic Health Records ◽

Sexually Transmitted Infections ◽

Control Measures ◽

Health Records ◽

Future Evolution ◽

Sexually Transmitted ◽

Electronic Health ◽

The Impact

Abstract Background: The COVID-19 pandemic and related control measures have affected the diagnosis of other diseases, including sexually transmitted infections (STI). Our aim is to analyse the impact of the COVID-19 pandemic on the incidence of STI diagnosed in primary care.Methods: Time-series study of STI, using data from primary care electronic health records in Catalonia (Spain) from January 2016 to March 2021. We obtained the monthly expected incidence of STI using a temporary regression, where the response variable was the incidence of STI from 2016 to 2019 and the adjustment variables were the trend and seasonality of the time series. Excess or reduction of STI were defined as the number of observed minus the expected cases, globally and stratified by age, sexe, type of STI and socioeconomic status.Results: Between March 2020 and March 2021 we observed a reduction of 20.2% (95% CI: 13.0% to 25.8%) on STI diagnoses compared to the expected. This reduction was greater during the lockdown period (-39%), in women (-26.5%), in people aged under 60 years (up to -22.4% in people aged 30-59 years), less deprived areas (-24%) and some types of STI, specially chlamydia (-32%), gonorrhea (-30.7%) and HIV (-21.5%). Conversely, syphilis and non-specific STI were those with lesser reductions with -3.6% and -7.2%, respectively,Conclusions: The COVID-19 pandemic has impacted on STI incidence, reducing the number of diagnoses performed in primary care and raising concerns about future evolution of STI trends. Those STI that are less symptomatic or diagnosed through screening will deserve special attention regarding potential diagnostic delays.

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