Incorporating symptom data in longitudinal disease trajectories for more detailed patient stratification

Research output: Contribution to journal › Journal article › Research › peer-review

Standard

Incorporating symptom data in longitudinal disease trajectories for more detailed patient stratification. / Lademann, Martin; Lademann, Mette; Boeck Jensen, Anders; Brunak, Søren.

In: International Journal of Medical Informatics, Vol. 129, 2019, p. 107-113.

Research output: Contribution to journal › Journal article › Research › peer-review

Harvard

Lademann, M, Lademann, M, Boeck Jensen, A & Brunak, S 2019, 'Incorporating symptom data in longitudinal disease trajectories for more detailed patient stratification', International Journal of Medical Informatics, vol. 129, pp. 107-113. https://doi.org/10.1016/j.ijmedinf.2019.06.003

APA

Lademann, M., Lademann, M., Boeck Jensen, A., & Brunak, S. (2019). Incorporating symptom data in longitudinal disease trajectories for more detailed patient stratification. International Journal of Medical Informatics, 129, 107-113. https://doi.org/10.1016/j.ijmedinf.2019.06.003

Vancouver

Lademann M, Lademann M, Boeck Jensen A, Brunak S. Incorporating symptom data in longitudinal disease trajectories for more detailed patient stratification. International Journal of Medical Informatics. 2019;129:107-113. https://doi.org/10.1016/j.ijmedinf.2019.06.003

Author

Lademann, Martin ; Lademann, Mette ; Boeck Jensen, Anders ; Brunak, Søren. / Incorporating symptom data in longitudinal disease trajectories for more detailed patient stratification. In: International Journal of Medical Informatics. 2019 ; Vol. 129. pp. 107-113.

Bibtex

@article{54d404d6a6b94e5ba54cb60366c99f97,
title = "Incorporating symptom data in longitudinal disease trajectories for more detailed patient stratification",
abstract = "OBJECTIVE: Use symptoms to stratify temporal disease trajectories.MATERIALS AND METHODS: We use data from the Danish National Patient Registry to stratify temporal disease pairs by the symptom distributions they associate to. The underlying data comprise of 6.6 million patients collectively assigned with 7.5 million symptoms from chapter XVIII in the WHO International Classification of Disease version 10 terminology.RESULTS: We stratify 33 disease pairs into 67 temporal disease-symptom-disease trajectories from three main diagnoses (two diabetes subtypes and COPD), where the symptom significantly changes the risk of developing the subsequent diseases. We combine these trajectories into three temporal disease networks, one for each main diagnosis. We confirm apparent relations between diseases and symptoms and discovered that multiple symptoms decrease the risk for diabetes progression.CONCLUSION: Symptoms can be used to stratify disease trajectories, and we suggest that this approach can be applied to temporal disease trajectories systematically using structured claims data. The method can be extended to also use text-mined symptoms from unstructured data in health records.",
author = "Martin Lademann and Mette Lademann and {Boeck Jensen}, Anders and S{\o}ren Brunak",
year = "2019",
doi = "10.1016/j.ijmedinf.2019.06.003",
language = "English",
volume = "129",
pages = "107--113",
journal = "International Journal of Medical Informatics",
issn = "1386-5056",
publisher = "Elsevier Ireland Ltd",

}

RIS

TY - JOUR

T1 - Incorporating symptom data in longitudinal disease trajectories for more detailed patient stratification

AU - Lademann, Martin

AU - Lademann, Mette

AU - Boeck Jensen, Anders

AU - Brunak, Søren

PY - 2019

Y1 - 2019

N2 - OBJECTIVE: Use symptoms to stratify temporal disease trajectories.MATERIALS AND METHODS: We use data from the Danish National Patient Registry to stratify temporal disease pairs by the symptom distributions they associate to. The underlying data comprise of 6.6 million patients collectively assigned with 7.5 million symptoms from chapter XVIII in the WHO International Classification of Disease version 10 terminology.RESULTS: We stratify 33 disease pairs into 67 temporal disease-symptom-disease trajectories from three main diagnoses (two diabetes subtypes and COPD), where the symptom significantly changes the risk of developing the subsequent diseases. We combine these trajectories into three temporal disease networks, one for each main diagnosis. We confirm apparent relations between diseases and symptoms and discovered that multiple symptoms decrease the risk for diabetes progression.CONCLUSION: Symptoms can be used to stratify disease trajectories, and we suggest that this approach can be applied to temporal disease trajectories systematically using structured claims data. The method can be extended to also use text-mined symptoms from unstructured data in health records.

AB - OBJECTIVE: Use symptoms to stratify temporal disease trajectories.MATERIALS AND METHODS: We use data from the Danish National Patient Registry to stratify temporal disease pairs by the symptom distributions they associate to. The underlying data comprise of 6.6 million patients collectively assigned with 7.5 million symptoms from chapter XVIII in the WHO International Classification of Disease version 10 terminology.RESULTS: We stratify 33 disease pairs into 67 temporal disease-symptom-disease trajectories from three main diagnoses (two diabetes subtypes and COPD), where the symptom significantly changes the risk of developing the subsequent diseases. We combine these trajectories into three temporal disease networks, one for each main diagnosis. We confirm apparent relations between diseases and symptoms and discovered that multiple symptoms decrease the risk for diabetes progression.CONCLUSION: Symptoms can be used to stratify disease trajectories, and we suggest that this approach can be applied to temporal disease trajectories systematically using structured claims data. The method can be extended to also use text-mined symptoms from unstructured data in health records.

U2 - 10.1016/j.ijmedinf.2019.06.003

DO - 10.1016/j.ijmedinf.2019.06.003

M3 - Journal article

C2 - 31445244

VL - 129

SP - 107

EP - 113

JO - International Journal of Medical Informatics

JF - International Journal of Medical Informatics

SN - 1386-5056

ER -

ID: 227086800