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arxiv: 1907.06058 · v1 · pith:OMPRAGNWnew · submitted 2019-07-13 · 💻 cs.LG · cs.CY· stat.ML

Aggregate-Eliminate-Predict: Detecting Adverse Drug Events from Heterogeneous Electronic Health Records

Maria Bampa , Panagiotis Papapetrou This is my paper

Pith reviewed 2026-05-24 22:02 UTC · model grok-4.3

classification 💻 cs.LG cs.CYstat.ML
keywords adverse drug eventselectronic health recordsfeature selectionmachine learningclassificationheterogeneous dataAUC
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The pith

Adding diagnosis and drug codes to lab measurements improves adverse drug event detection from electronic health records.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper tries to establish that aggregating lab measurements with diagnosis codes and drug prescriptions, then applying recursive feature selection, substantially improves the detection of adverse drug events in electronic health records. This matters because electronic health records contain rich but varied data types that previous methods ignored, potentially missing important signals for patient safety. The authors test this extension on five datasets with six classifiers and report statistically significant AUC improvements while keeping the selected features medically meaningful. A reader would see this as a practical way to make better use of existing hospital data for predicting medication-related harms.

Core claim

By extending the prior framework to include diagnosis codes and drug codes in addition to lab measurements and applying recursive feature selection to identify the top-k important features, the method achieves higher area under the ROC curve (AUC) values that are statistically significant compared to the lab-only approach.

What carries the argument

Recursive feature selection on aggregated heterogeneous EHR features consisting of lab measurements, diagnosis codes, and drug codes.

If this is right

  • The combined feature set outperforms lab measurements alone in predictive performance.
  • Feature selection ensures only the most relevant medical features are used.
  • Results are consistent across multiple classifiers and datasets.
  • The approach maintains interpretability through medically relevant features.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Similar aggregation and selection could improve other clinical prediction tasks involving mixed data types.
  • The selected features might reveal new insights into risk factors for specific adverse events.
  • Deploying this in clinical decision support systems could reduce the incidence of adverse drug events.

Load-bearing premise

The five medical datasets are representative of real-world heterogeneous electronic health records and recursive feature selection avoids introducing bias or overfitting.

What would settle it

Running the same experiments on an additional independent set of electronic health records and finding no statistically significant AUC improvement from adding the diagnosis and drug codes.

Figures

Figures reproduced from arXiv: 1907.06058 by Maria Bampa, Panagiotis Papapetrou.

Figure 1
Figure 1. Figure 1: An EHR sequence and a window of interest [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: post-hoc Nemenyi test for multiple comparison of [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Feature importance on D61.1 a er feature elimina [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗
read the original abstract

We study the problem of detecting adverse drug events in electronic healthcare records. The challenge in this work is to aggregate heterogeneous data types involving diagnosis codes, drug codes, as well as lab measurements. An earlier framework proposed for the same problem demonstrated promising predictive performance for the random forest classifier by using only lab measurements as data features. We extend this framework, by additionally including diagnosis and drug prescription codes, concurrently. In addition, we employ a recursive feature selection mechanism on top, that extracts the top-k most important features. Our experimental evaluation on five medical datasets of adverse drug events and six different classifiers, suggests that the integration of these additional features provides substantial and statistically significant improvements in terms of AUC, while employing medically relevant features.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 2 minor

Summary. The manuscript extends a prior framework for adverse drug event (ADE) detection from heterogeneous electronic health records by adding diagnosis and drug prescription codes to lab measurements, then applies recursive feature selection to retain the top-k most important features. Experiments across five medical datasets and six classifiers report substantial, statistically significant AUC improvements attributable to the added features and selection step.

Significance. If the AUC gains prove robust under properly nested evaluation, the approach could strengthen ADE detection by better exploiting heterogeneous EHR data types. The evaluation spans multiple datasets and classifiers, which provides a modest check on generalizability.

major comments (2)
  1. [Abstract and §4] Abstract and §4 (Experimental Evaluation): The central claim of 'statistically significant' AUC improvements from the added features and recursive feature selection is load-bearing, yet the manuscript supplies no description of the cross-validation procedure, whether feature selection was performed inside or outside the CV loop, or how class imbalance was handled. Without this, the reported gains cannot be verified as free of optimistic bias.
  2. [§3.2 and §4] §3.2 (Recursive Feature Selection) and §4: Recursive feature selection (top-k by importance) is applied after aggregation; if executed on the full dataset before train/test splits or outer CV folds, label information can leak into the selected feature set. This directly undermines the claim that improvements reflect genuine signal from heterogeneous features rather than evaluation artifacts.
minor comments (2)
  1. [Abstract] Abstract: The phrase 'substantial and statistically significant improvements' would be strengthened by naming the exact baseline (prior lab-only model) and reporting the magnitude of AUC deltas alongside p-values.
  2. [§4] §4: Tables reporting AUC results should include the value of k used for top-k selection and indicate whether the same k was applied uniformly across all datasets and classifiers.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the careful review and constructive comments on our manuscript. The concerns about experimental methodology and potential evaluation bias are important, and we address each major comment below with plans for revision.

read point-by-point responses

  1. Referee: [Abstract and §4] Abstract and §4 (Experimental Evaluation): The central claim of 'statistically significant' AUC improvements from the added features and recursive feature selection is load-bearing, yet the manuscript supplies no description of the cross-validation procedure, whether feature selection was performed inside or outside the CV loop, or how class imbalance was handled. Without this, the reported gains cannot be verified as free of optimistic bias.

    Authors: We acknowledge that the manuscript omits a detailed description of the cross-validation procedure. In the experiments, we used 5-fold stratified cross-validation to address class imbalance, with recursive feature selection performed inside each CV fold on training data only. Statistical significance of AUC differences was evaluated via paired t-tests across folds. We will add a dedicated subsection to §4 describing the full evaluation protocol, including the nested structure for feature selection and imbalance handling, to allow independent verification that the reported gains are free of optimistic bias. revision: yes

  2. Referee: [§3.2 and §4] §3.2 (Recursive Feature Selection) and §4: Recursive feature selection (top-k by importance) is applied after aggregation; if executed on the full dataset before train/test splits or outer CV folds, label information can leak into the selected feature set. This directly undermines the claim that improvements reflect genuine signal from heterogeneous features rather than evaluation artifacts.

    Authors: We agree that feature selection on the full dataset would introduce leakage and undermine the claims. Our implementation performed recursive feature selection nested inside each training fold of the cross-validation, using only training data to compute importance scores and select the top-k features before model training. We will revise §3.2 to explicitly describe this nested procedure and update §4 with the corresponding experimental details and pseudocode to demonstrate the absence of leakage. revision: yes

Circularity Check

0 steps flagged

No circularity in empirical ML evaluation

full rationale

The paper describes an empirical extension of a prior framework for adverse drug event detection: it aggregates heterogeneous EHR features (lab measurements plus diagnosis/drug codes), applies recursive feature selection to retain top-k features, and reports AUC gains across five datasets and six classifiers. No equations, fitted parameters, or derived quantities are presented that are then relabeled as independent predictions. The reference to an 'earlier framework' is a standard citation for baseline comparison and does not serve as a load-bearing uniqueness theorem or self-definitional anchor. All performance claims rest on external experimental outcomes rather than any reduction to the paper's own inputs by construction.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract-only review supplies no information on free parameters, axioms, or invented entities.

pith-pipeline@v0.9.0 · 5656 in / 960 out tokens · 27674 ms · 2026-05-24T22:02:15.404858+00:00 · methodology

discussion (0)

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Reference graph

Works this paper leans on

17 extracted references · 17 canonical work pages

  1. [1]

    [n. d.]. Overview - Adverse Drug Events - health.gov. ([n. d.]). h/t_tps://health. gov/hcq/ade.asp

    work page

  2. [2]

    Francesco Baga/t_tini, Isak Karlsson, Jonathan Rebane, and Panagiotis Papapetrou

    work page

  3. [3]

    BMC Medical Informatics and Decision Making 19, 1 (12 2019), 7

    A classi/f_ication framework for exploiting sparse multi-variate temporal features with application to adverse drug event detection in medical records. BMC Medical Informatics and Decision Making 19, 1 (12 2019), 7. h/t_tps://doi.org/ 10.1186/s12911-018-0717-4

    work page doi:10.1186/s12911-018-0717-4 2019

  4. [4]

    Hercules Dalianis, A Henriksson, Maria Kvist, Sumithra Velupillai, and R Weegar

    work page

  5. [5]

    CAiSE-2015 Industry Track co-located with 27th Conference on Advanced Information Systems Engineering (CAiSE - CEUR) 1381 (01 2015), 1–18

    HEALTH BANK - A workbench for data science applications in health- care. CAiSE-2015 Industry Track co-located with 27th Conference on Advanced Information Systems Engineering (CAiSE - CEUR) 1381 (01 2015), 1–18

    work page 2015

  6. [6]

    Janez Demˇsar. 2006. Statistical Comparisons of Classi/f_iers over Multiple Data Sets. Technical Report. 1–30 pages

    work page 2006

  7. [7]

    Robert Eriksson, Peter Bjdstrup Jensen, Sune Frankild, Lars Juhl Jensen, and Sren Brunak. 2013. Dictionary construction and identi/f_ication of possible adverse drug events in Danish clinical narrative text. Journal of the American Medical Informatics Association : JAMIA 20, 5 (2013), 947–53

    work page 2013

  8. [8]

    Tom Fawce/t_t. 2006. An introduction to ROC analysis.Pa/t_tern Recognition Le/t_ters 27, 8 (6 2006), 861–874

    work page 2006

  9. [9]

    D Formica, J Sultana, PM Cutroneo, S Lucchesi, R Angelica, S Crisafulli, Y Ingrascio/t_ta, F Salvo, E Spina, and G Tri/f_ir`o. 2018. /T_he economic burden of preventable adverse drug reactions: a systematic review of observational studies. Expert Opinion on Drug Safety 17, 7 (7 2018), 681–695

    work page 2018

  10. [10]

    H¨armark and A

    L. H¨armark and A. C. van Grootheest. 2008. Pharmacovigilance: methods, recent developments and future perspectives.European Journal of Clinical Pharmacology 64, 8 (8 2008), 743–752

    work page 2008

  11. [11]

    Genevieve B Melton and George Hripcsak. 2005. Automated detection of adverse events using natural language processing of discharge summaries. Journal of the American Medical Informatics Association : JAMIA 12, 4 (2005), 448–57

    work page 2005

  12. [12]

    Jing Zhao. 2015. Temporal weighting of clinical events in electronic health records for pharmacovigilance. In 2015 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 375–381

    work page 2015

  13. [13]

    Jing Zhao and Aron Henriksson. 2016. Learning temporal weights of clinical events using variable importance. BMC medical informatics and decision making 16 Suppl 2, Suppl 2 (2016), 71

    work page 2016

  14. [14]

    Jing Zhao, Aron Henriksson, Lars Asker, and Henrik Bostr¨om. 2015. Predictive modeling of structured electronic health records for adverse drug event detection. BMC Medical Informatics and Decision Making 15, S4 (12 2015), S1

    work page 2015

  15. [15]

    Jing Zhao, Aron Henriksson, and Henrik Bostrom. 2014. Detecting Adverse Drug Events Using Concept Hierarchies of Clinical Codes. In 2014 IEEE International Conference on Healthcare Informatics . IEEE, 285–293

    work page 2014

  16. [16]

    Jing Zhao, Aron Henriksson, and Henrik Bostrom. 2015. Cascading adverse drug event detection in electronic health records. In2015 IEEE International Conference on Data Science and Advanced Analytics (DSAA) . IEEE, 1–8

    work page 2015

  17. [17]

    Jing Zhao, Aron Henriksson, Maria Kvist, Lars Asker, and Henrik Bostr¨om. 2015. Handling Temporality of Clinical Events for Drug Safety Surveillance. Annual Symposium proceedings. AMIA Symposium 2015 (2015), 1371–80. 4

    work page 2015