Ändra sökning
RefereraExporteraLänk till posten
Permanent länk

Direktlänk
Referera
Referensformat
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Annat format
Fler format
Språk
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Annat språk
Fler språk
Utmatningsformat
  • html
  • text
  • asciidoc
  • rtf
Learning from heterogeneous temporal data from electronic health records
Stockholms universitet, Samhällsvetenskapliga fakulteten, Institutionen för data- och systemvetenskap.
Stockholms universitet, Samhällsvetenskapliga fakulteten, Institutionen för data- och systemvetenskap.
Stockholms universitet, Samhällsvetenskapliga fakulteten, Institutionen för data- och systemvetenskap.
Stockholms universitet, Samhällsvetenskapliga fakulteten, Institutionen för data- och systemvetenskap.
2017 (Engelska)Ingår i: Journal of Biomedical Informatics, ISSN 1532-0464, E-ISSN 1532-0480, Vol. 65, 105-119 s.Artikel i tidskrift (Refereegranskat) Published
Abstract [en]

Electronic health records contain large amounts of longitudinal data that are valuable for biomedical informatics research. The application of machine learning is a promising alternative to manual analysis of such data. However, the complex structure of the data, which includes clinical events that are unevenly distributed over time, poses a challenge for standard learning algorithms. Some approaches to modeling temporal data rely on extracting single values from time series; however, this leads to the loss of potentially valuable sequential information. How to better account for the temporality of clinical data, hence, remains an important research question. In this study, novel representations of temporal data in electronic health records are explored. These representations retain the sequential information, and are directly compatible with standard machine learning algorithms. The explored methods are based on symbolic sequence representations of time series data, which are utilized in a number of different ways. An empirical investigation, using 19 datasets comprising clinical measurements observed over time from a real database of electronic health records, shows that using a distance measure to random subsequences leads to substantial improvements in predictive performance compared to using the original sequences or clustering the sequences. Evidence is moreover provided on the quality of the symbolic sequence representation by comparing it to sequences that are generated using domain knowledge by clinical experts. The proposed method creates representations that better account for the temporality of clinical events, which is often key to prediction tasks in the biomedical domain.

Ort, förlag, år, upplaga, sidor
2017. Vol. 65, 105-119 s.
Nyckelord [en]
random subsequence, time series classification, electronic health records, data mining, machine learning
Nationell ämneskategori
Systemvetenskap, informationssystem och informatik
Forskningsämne
data- och systemvetenskap
Identifikatorer
URN: urn:nbn:se:su:diva-137481DOI: 10.1016/j.jbi.2016.11.006ISI: 000406235200008OAI: oai:DiVA.org:su-137481DiVA: diva2:1062756
Tillgänglig från: 2017-01-08 Skapad: 2017-01-08 Senast uppdaterad: 2017-08-21Bibliografiskt granskad
Ingår i avhandling
1. Learning Predictive Models from Electronic Health Records
Öppna denna publikation i ny flik eller fönster >>Learning Predictive Models from Electronic Health Records
2017 (Engelska)Doktorsavhandling, sammanläggning (Övrigt vetenskapligt)
Abstract [en]

The ongoing digitization of healthcare, which has been much accelerated by the widespread adoption of electronic health records, generates unprecedented amounts of clinical data in a readily computable form. This, in turn, affords great opportunities for making meaningful secondary use of clinical data in the endeavor to improve healthcare, as well as to support epidemiology and medical research. To that end, there is a need for techniques capable of effectively and efficiently analyzing large amounts of clinical data. While machine learning provides the necessary tools, learning effective predictive models from electronic health records comes with many challenges due to the complexity of the data. Electronic health records contain heterogeneous and longitudinal data that jointly provides a rich perspective of patient trajectories in the healthcare process. The diverse characteristics of the data need to be properly accounted for when learning predictive models from clinical data. However, how best to represent healthcare data for predictive modeling has been insufficiently studied. This thesis addresses several of the technical challenges involved in learning effective predictive models from electronic health records.

Methods are developed to address the challenges of (i) representing heterogeneous types of data, (ii) leveraging the concept hierarchy of clinical codes, and (iii) modeling the temporality of clinical events. The proposed methods are evaluated empirically in the context of detecting adverse drug events in electronic health records. Various representations of each type of data that account for its unique characteristics are investigated and it is shown that combining multiple representations yields improved predictive performance. It is also demonstrated how the information embedded in the concept hierarchy of clinical codes can be exploited, both for creating enriched feature spaces and for decomposing the predictive task. Moreover, incorporating temporal information leads to more effective predictive models by distinguishing between event occurrences in the patient history. Both single-point representations, using pre-assigned or learned temporal weights, and multivariate time series representations are shown to be more informative than representations in which temporality is ignored. Effective methods for representing heterogeneous and longitudinal data are key for enhancing and truly enabling meaningful secondary use of electronic health records through large-scale analysis of clinical data.

Ort, förlag, år, upplaga, sidor
Stockholm: Department of Computer and Systems Sciences, Stockholm University, 2017. 82 s.
Serie
Report Series / Department of Computer & Systems Sciences, ISSN 1101-8526 ; 17-001
Nyckelord
Data Science, Machine Learning, Predictive Modeling, Data Representation, Health Informatics, Electronic Health Records
Nationell ämneskategori
Datavetenskap (datalogi)
Forskningsämne
data- och systemvetenskap
Identifikatorer
urn:nbn:se:su:diva-137936 (URN)978-91-7649-682-4 (ISBN)978-91-7649-683-1 (ISBN)
Disputation
2017-03-02, Lilla hörsalen, NOD-huset, Borgarfjordsgatan 12, Kista, 13:00 (Engelska)
Opponent
Handledare
Tillgänglig från: 2017-02-07 Skapad: 2017-01-13 Senast uppdaterad: 2017-02-08Bibliografiskt granskad

Open Access i DiVA

Fulltext saknas

Övriga länkar

Förlagets fulltext

Sök vidare i DiVA

Av författaren/redaktören
Zhao, JingPapapetrou, PanagiotisAsker, LarsBoström, Henrik
Av organisationen
Institutionen för data- och systemvetenskap
I samma tidskrift
Journal of Biomedical Informatics
Systemvetenskap, informationssystem och informatik

Sök vidare utanför DiVA

GoogleGoogle Scholar

doi
urn-nbn

Altmetricpoäng

doi
urn-nbn
Totalt: 55 träffar
RefereraExporteraLänk till posten
Permanent länk

Direktlänk
Referera
Referensformat
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Annat format
Fler format
Språk
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Annat språk
Fler språk
Utmatningsformat
  • html
  • text
  • asciidoc
  • rtf