Generating and Comparing Knowledge Graphs of Medical Processes Using pMineR

Roberto Gatta; Mauro Vallati; Jacopo Lenkowicz; Eric Rojas; Andrea Damiani; Lucia Sacchi; Berardino De Bari; Arianna Dagliati; Carlos Fernandez-Llatas; Matteo Montesi; Antonio Marchetti; Maurizio Castellano; Vincenzo Valentini

doi:10.1145/3148011.3154464

Generating and Comparing Knowledge Graphs of Medical Processes Using pMineR

Roberto Gatta, Mauro Vallati, Jacopo Lenkowicz, Eric Rojas, Andrea Damiani, Lucia Sacchi, Berardino De Bari, Arianna Dagliati, Carlos Fernandez-Llatas, Matteo Montesi, Antonio Marchetti, Maurizio Castellano, Vincenzo Valentini

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

19 Citations (Scopus)

Abstract

Process mining focuses on extracting knowledge, under the form of models, from data generated and stored in information systems. The analysis of generated models can provide useful insights to domain experts. In addition, models of processes can be used to test if a considered process complies with some given specifications. For these reasons, process mining is gaining significant importance in the healthcare domain, where the complexity and flexibility of processes makes extremely hard to evaluate and assess how patients have been treated.
In this paper we describe how pMineR, an R library designed and developed for performing process mining in the medical domain, is currently exploited in Hospitals for supporting domain experts in the analysis of the extracted knowledge models. In its current release, pMineR can encode extracted processes under the form of directed graphs, which are easy to interpret and understand by experts of the domain. It also provides graphical comparison between different processes, allows to model the adherence to a given clinical guidelines and to estimate performance and the workload of the available resources in healthcare

Original language	English
Title of host publication	Proceedings of the Ninth Conference on Knowledge Capture (K-CAP), (Austin, TX, 4-6 December 2017)
Publisher	Association for Computing Machinery (ACM)
Number of pages	4
ISBN (Print)	9781450355537
DOIs	https://doi.org/10.1145/3148011.3154464
Publication status	Published - 4 Dec 2017
Event	9th International Conference on Knowledge Capture - Hilton Garden Inn Convention Center, Austin, United States Duration: 4 Dec 2017 → 6 Dec 2017 Conference number: 9 https://k-cap2017.org/ (Link to Conference Website)

Conference

Conference	9th International Conference on Knowledge Capture
Abbreviated title	K-CAP 2017
Country/Territory	United States
City	Austin
Period	4/12/17 → 6/12/17
Internet address	https://k-cap2017.org/ (Link to Conference Website)

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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Gatta, R., Vallati, M., Lenkowicz, J., Rojas, E., Damiani, A., Sacchi, L., De Bari, B., Dagliati, A., Fernandez-Llatas, C., Montesi, M., Marchetti, A., Castellano, M., & Valentini, V. (2017). Generating and Comparing Knowledge Graphs of Medical Processes Using pMineR. In Proceedings of the Ninth Conference on Knowledge Capture (K-CAP), (Austin, TX, 4-6 December 2017) [36] Association for Computing Machinery (ACM). https://doi.org/10.1145/3148011.3154464

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title = "Generating and Comparing Knowledge Graphs of Medical Processes Using pMineR",

abstract = "Process mining focuses on extracting knowledge, under the form of models, from data generated and stored in information systems. The analysis of generated models can provide useful insights to domain experts. In addition, models of processes can be used to test if a considered process complies with some given specifications. For these reasons, process mining is gaining significant importance in the healthcare domain, where the complexity and flexibility of processes makes extremely hard to evaluate and assess how patients have been treated.In this paper we describe how pMineR, an R library designed and developed for performing process mining in the medical domain, is currently exploited in Hospitals for supporting domain experts in the analysis of the extracted knowledge models. In its current release, pMineR can encode extracted processes under the form of directed graphs, which are easy to interpret and understand by experts of the domain. It also provides graphical comparison between different processes, allows to model the adherence to a given clinical guidelines and to estimate performance and the workload of the available resources in healthcare",

keywords = "Process mining, knowledge graph extraction, Health informatics",

author = "Roberto Gatta and Mauro Vallati and Jacopo Lenkowicz and Eric Rojas and Andrea Damiani and Lucia Sacchi and {De Bari}, Berardino and Arianna Dagliati and Carlos Fernandez-Llatas and Matteo Montesi and Antonio Marchetti and Maurizio Castellano and Vincenzo Valentini",

year = "2017",

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doi = "10.1145/3148011.3154464",

language = "English",

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booktitle = "Proceedings of the Ninth Conference on Knowledge Capture (K-CAP), (Austin, TX, 4-6 December 2017)",

publisher = "Association for Computing Machinery (ACM)",

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note = "9th International Conference on Knowledge Capture, K-CAP 2017 ; Conference date: 04-12-2017 Through 06-12-2017",

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Gatta, R, Vallati, M, Lenkowicz, J, Rojas, E, Damiani, A, Sacchi, L, De Bari, B, Dagliati, A, Fernandez-Llatas, C, Montesi, M, Marchetti, A, Castellano, M & Valentini, V 2017, Generating and Comparing Knowledge Graphs of Medical Processes Using pMineR. in Proceedings of the Ninth Conference on Knowledge Capture (K-CAP), (Austin, TX, 4-6 December 2017)., 36, Association for Computing Machinery (ACM), 9th International Conference on Knowledge Capture, Austin, Texas, United States, 4/12/17. https://doi.org/10.1145/3148011.3154464

Generating and Comparing Knowledge Graphs of Medical Processes Using pMineR. / Gatta, Roberto; Vallati, Mauro; Lenkowicz, Jacopo et al.

Proceedings of the Ninth Conference on Knowledge Capture (K-CAP), (Austin, TX, 4-6 December 2017). Association for Computing Machinery (ACM), 2017. 36.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Generating and Comparing Knowledge Graphs of Medical Processes Using pMineR

AU - Gatta, Roberto

AU - Vallati, Mauro

AU - Lenkowicz, Jacopo

AU - Rojas, Eric

AU - Damiani, Andrea

AU - Sacchi, Lucia

AU - De Bari, Berardino

AU - Dagliati, Arianna

AU - Fernandez-Llatas, Carlos

AU - Montesi, Matteo

AU - Marchetti, Antonio

AU - Castellano, Maurizio

AU - Valentini, Vincenzo

N1 - Conference code: 9

PY - 2017/12/4

Y1 - 2017/12/4

N2 - Process mining focuses on extracting knowledge, under the form of models, from data generated and stored in information systems. The analysis of generated models can provide useful insights to domain experts. In addition, models of processes can be used to test if a considered process complies with some given specifications. For these reasons, process mining is gaining significant importance in the healthcare domain, where the complexity and flexibility of processes makes extremely hard to evaluate and assess how patients have been treated.In this paper we describe how pMineR, an R library designed and developed for performing process mining in the medical domain, is currently exploited in Hospitals for supporting domain experts in the analysis of the extracted knowledge models. In its current release, pMineR can encode extracted processes under the form of directed graphs, which are easy to interpret and understand by experts of the domain. It also provides graphical comparison between different processes, allows to model the adherence to a given clinical guidelines and to estimate performance and the workload of the available resources in healthcare

AB - Process mining focuses on extracting knowledge, under the form of models, from data generated and stored in information systems. The analysis of generated models can provide useful insights to domain experts. In addition, models of processes can be used to test if a considered process complies with some given specifications. For these reasons, process mining is gaining significant importance in the healthcare domain, where the complexity and flexibility of processes makes extremely hard to evaluate and assess how patients have been treated.In this paper we describe how pMineR, an R library designed and developed for performing process mining in the medical domain, is currently exploited in Hospitals for supporting domain experts in the analysis of the extracted knowledge models. In its current release, pMineR can encode extracted processes under the form of directed graphs, which are easy to interpret and understand by experts of the domain. It also provides graphical comparison between different processes, allows to model the adherence to a given clinical guidelines and to estimate performance and the workload of the available resources in healthcare

KW - Process mining

KW - knowledge graph extraction

KW - Health informatics

U2 - 10.1145/3148011.3154464

DO - 10.1145/3148011.3154464

M3 - Conference contribution

SN - 9781450355537

BT - Proceedings of the Ninth Conference on Knowledge Capture (K-CAP), (Austin, TX, 4-6 December 2017)

PB - Association for Computing Machinery (ACM)

T2 - 9th International Conference on Knowledge Capture

Y2 - 4 December 2017 through 6 December 2017

ER -

Generating and Comparing Knowledge Graphs of Medical Processes Using pMineR

Abstract

Conference

UN SDGs

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