Publications

Predicting length of stay with administrative data from acute and emergency care: an embedding approach

Vincent Lequertier, Tao Wang, Julien Fondrevelle, Vincent Augusto, Stéphanie Polazzi, Antoine Duclos

Abstract

Hospital beds management is critical for the quality of patient care, while length of inpatient stay is often estimated empirically by physicians or chief nurses of medical wards. Providing an efficient method for forecasting the length of stay (LOS) is expected to improve resources and discharges planning. Predictions should be accurate and work for as many patients as possible, despite their heterogeneous profiles. In this work, a LOS prediction method based on deep learning and embeddings is developed by using generic hospital administrative data from a French national hospital discharge database, as well as emergency care. Data concerned 497 626 stays of 304 931 patients from 6 hospitals in Lyon, France, from 2011 to 2019. Results of a 5-fold cross-validation showed an accuracy of 0.73 and a kappa score of 0.67 for the embeddings method. This outperformed the baseline which used the raw input features directly.

Publication
2021 IEEE 17th International Conference on Automation Science and Engineering (CASE)
Date
August, 2021
Links
DOI

Hospital Length of Stay Prediction Methods: A Systematic Review

Vincent Lequertier, Tao Wang, Julien Fondrevelle, Vincent Augusto, Antoine Duclos

Abstract

Objective: This systematic review sought to establish a picture of length of stay (LOS) prediction methods based on available hospital data and study protocols designed to measure their performance. Materials and Methods: An English literature search was done relative to hospital LOS prediction from 1972 to September 2019 according to the PRISMA guidelines. Articles were retrieved from PubMed, ScienceDirect, and arXiv databases. Information were extracted from the included papers according to a standardized assessment of population setting and study sample, data sources and input variables, LOS prediction methods, validation study design, and performance evaluation metrics. Results: Among 74 selected articles, 98.6% (73/74) used patients’ data to predict LOS; 27.0% (20/74) used temporal data; and 21.6% (16/74) used the data about hospitals. Overall, regressions were the most popular prediction methods (64.9%, 48/74), followed by machine learning (20.3%, 15/74) and deep learning (17.6%, 13/74). Regarding validation design, 35.1% (26/74) did not use a test set, whereas 47.3% (35/74) used a separate test set, and 17.6% (13/74) used cross-validation. The most used performance metrics were R2 (47.3%, 35/74), mean squared (or absolute) error (24.4%, 18/74), and the accuracy (14.9%, 11/74). Over the last decade, machine learning and deep learning methods became more popular (P = 0.016), and test sets and cross-validation got more and more used (P = 0.014). Conclusions: Methods to predict LOS are more and more elaborate and the assessment of their validity is increasingly rigorous. Re-

Publication
Medical Care
Date
July, 2021
Links
DOI

Doc2Vec on the PubMed corpus: study of a new approach to generate related articles

Emeric Dynomant, Stéfan J. Darmoni, Émeline Lejeune, Gaëtan Kerdelhué, Jean-Philippe Leroy, Vincent Lequertier, Stéphane Canu, Julien Grosjean

Abstract

PubMed is the biggest and most used bibliographic database worldwide, hosting more than 26M biomedical publications. One of its useful features is the \"similar articles\" section, allowing the end-user to find scientific articles linked to the consulted document in term of context. The aim of this study is to analyze whether it is possible to replace the statistic model PubMed Related Articles (pmra) with a document embedding method. Doc2Vec algorithm was used to train models allowing to vectorize documents. Six of its parameters were optimised by following a grid-search strategy to train more than 1,900 models. Parameters combination leading to the best accuracy was used to train models on abstracts from the PubMed database. Four evaluations tasks were defined to determine what does or does not influence the proximity between documents for both Doc2Vec and pmra. The two different Doc2Vec architectures have different abilities to link documents about a common context. The terminological indexing, words and stems contents of linked documents are highly similar between pmra and Doc2Vec PV-DBOW architecture. These algorithms are also more likely to bring closer documents having a similar size. In contrary, the manual evaluation shows much better results for the pmra algorithm. While the pmra algorithm links documents by explicitly using terminological indexing in its formula, Doc2Vec does not need a prior indexing. It can infer relations between documents sharing a similar indexing, without any knowledge about them, particularly regarding the PV-DBOW architecture. In contrary, the human evaluation, without any clear agreement between evaluators, implies future studies to better understand this difference between PV-DBOW and pmra algorithm.

Publication
arXiv
Date
November, 2019
Links
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