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Hospitalization Length of Stay Prediction using Patient Event Sequences

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Emil Riis Hansen, Thomas Dyhre Nielsen, Thomas Mulvad, Mads Nibe Strausholm, Tomer Sagi, Katja Hose

Machine Learning · Submitted 20 Mar 2023

abstract compiled · 2026-09-23 · no language-model review

The authors' main claim Experimental results show that M-BERT can achieve high accuracy on a variety of LOS problems and outperforms traditional nonsequence-based machine learning approaches.

how it is stated · asserted 0.97 read the record →

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arXiv ID2303.11042 (version 1)
Pith IDJZGL4LPD, Pith's permanent record of this paper and its review
CitationEmil Riis Hansen, Katja Hose, Mads Nibe Strausholm, Thomas Dyhre Nielsen, Thomas Mulvad, Tomer Sagi. "Hospitalization Length of Stay Prediction using Patient Event Sequences." arXiv:2303.11042 (2023). https://pith.science/paper/JZGL4LPD
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The paper's own sentences, arranged. Each label is drawn from a closed vocabulary; the number beside it is the probability the classifier gave that label. Numbers in the tables are the authors', checked by code. No sentence in this record was written by a model.

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Compiled by classifier-1.13.0 via TypeSafe API, question bank abstract-p1-20260923, 2026-09-23: 1 request, 4,193 input tokens.

What the abstract itself states, as probabilities: claims first 0.13 · states limitation 0.05 · human participants 0.97 · controlled comparison 0.47 · randomized assignment 0.01 · states quantitative result 0.76 · compares with prior results 0.92 · mentions code or data availability 0.03.

The abstract, sentence by sentence

arXiv listing abstract v1. Results and the main claim are underlined; .

Predicting patients hospital length of stay (LOS) is essential for improving resource allocation and supporting decision-making in healthcare organizations. This paper proposes a novel approach for predicting LOS by modeling patient information as sequences of events. Specifically, we present a transformer-based model, termed Medic-BERT (M-BERT), for LOS prediction using the unique features describing patients medical event sequences. We performed empirical experiments on a cohort of more than 45k emergency care patients from a large Danish hospital. Experimental results show that M-BERT can achieve high accuracy on a variety of LOS problems and outperforms traditional nonsequence-based machine learning approaches.

background contribution method result interpretation main claim

What the classifier reports for each sentence. The number is the probability it gave the label.
#sentencerolethe authors' own workstates the main conclusionstrength
s0 Predicting patients hospital length of stay (LOS) is essential for improving resource allocation and supporting decision-making in healthcare organizations. background 1.00 0.05 0.03 asserted 1.00
s1 This paper proposes a novel approach for predicting LOS by modeling patient information as sequences of events. contribution 0.98 0.79 0.06 asserted 0.98
s2 Specifically, we present a transformer-based model, termed Medic-BERT (M-BERT), for LOS prediction using the unique features describing patients medical event sequences. method 0.84 0.80 0.06 asserted 0.97
s3 We performed empirical experiments on a cohort of more than 45k emergency care patients from a large Danish hospital. method 0.96 0.97 0.02 asserted 0.99
s4 Experimental results show that M-BERT can achieve high accuracy on a variety of LOS problems and outperforms traditional nonsequence-based machine learning approaches. result 1.00 0.96 0.85 asserted 0.97

What kind of paper the classifier reports this is: method or tool 1.00 · evidence from observation or experiment 0.68.

What was examined, and what was not

sources read
  • arXiv listing abstract v1
not examined
  • full text
  • figures and tables
  • references and citing sentences
  • relations to other papers
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  • P abstract
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  • S sections
  • T body sentences
  • C citations
  • R cross-paper relations

Every quoted sentence on this record is the authors'. Every label is from Pith's closed vocabulary, with the probability shown beside it. Every number in the measurements table is the authors', checked by code. Record as JSON

The paper
Original abstract

Predicting patients hospital length of stay (LOS) is essential for improving resource allocation and supporting decision-making in healthcare organizations. This paper proposes a novel approach for predicting LOS by modeling patient information as sequences of events. Specifically, we present a transformer-based model, termed Medic-BERT (M-BERT), for LOS prediction using the unique features describing patients medical event sequences. We performed empirical experiments on a cohort of more than 45k emergency care patients from a large Danish hospital. Experimental results show that M-BERT can achieve high accuracy on a variety of LOS problems and outperforms traditional nonsequence-based machine learning approaches.

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