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@ARTICLE{Merkelbach:1005756,
      author       = {Merkelbach, Kilian and Schaper, Steffen and Diedrich,
                      Christian and Fritsch, Sebastian Johannes and Schuppert,
                      Andreas},
      title        = {{N}ovel architecture for gated recurrent unit autoencoder
                      trained on time series from electronic health records
                      enables detection of {ICU} patient subgroups},
      journal      = {Scientific reports},
      volume       = {13},
      number       = {1},
      issn         = {2045-2322},
      address      = {[London]},
      publisher    = {Macmillan Publishers Limited, part of Springer Nature},
      reportid     = {FZJ-2023-01610},
      pages        = {4053},
      year         = {2023},
      abstract     = {Electronic health records (EHRs) are used in hospitals to
                      store diagnoses, clinician notes, examinations, lab results,
                      and interventions for each patient. Grouping patients into
                      distinct subsets, for example, via clustering, may enable
                      the discovery of unknown disease patterns or comorbidities,
                      which could eventually lead to better treatment through
                      personalized medicine. Patient data derived from EHRs is
                      heterogeneous and temporally irregular. Therefore,
                      traditional machine learning methods like PCA are ill-suited
                      for analysis of EHR-derived patient data. We propose to
                      address these issues with a new methodology based on
                      training a gated recurrent unit (GRU) autoencoder directly
                      on health record data. Our method learns a low-dimensional
                      feature space by training on patient data time series, where
                      the time of each data point is expressed explicitly. We use
                      positional encodings for time, allowing our model to better
                      handle the temporal irregularity of the data. We apply our
                      method to data from the Medical Information Mart for
                      Intensive Care (MIMIC-III). Using our data-derived feature
                      space, we can cluster patients into groups representing
                      major classes of disease patterns. Additionally, we show
                      that our feature space exhibits a rich substructure at
                      multiple scales.},
      cin          = {JSC},
      ddc          = {600},
      cid          = {I:(DE-Juel1)JSC-20090406},
      pnm          = {5112 - Cross-Domain Algorithms, Tools, Methods Labs (ATMLs)
                      and Research Groups (POF4-511) / SMITH -
                      Medizininformatik-Konsortium - Beitrag Forschungszentrum
                      Jülich (01ZZ1803M)},
      pid          = {G:(DE-HGF)POF4-5112 / G:(BMBF)01ZZ1803M},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {36906642},
      UT           = {WOS:000958974100039},
      doi          = {10.1038/s41598-023-30986-1},
      url          = {https://juser.fz-juelich.de/record/1005756},
}