Evidence map›Paper›PMID 35447732›Full record

ArticleBioengineering (Basel, Switzerland)2022

Machine Learning and Regression Analysis to Model the Length of Hospital Stay in Patients with Femur Fracture.

Carlo Ricciardi, Alfonso Maria Ponsiglione, Arianna Scala, Anna Borrelli, Mario Misasi, Gaetano Romano, Giuseppe Russo, Maria Triassi, Giovanni Improta

Open access · goldAbstract read
In one paragraph

Article in Bioengineering (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
12citing papers in PubMed, 1 pooled it
6.8field-weighted citation impact, top 2% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

12 citing papers in PubMed, 1 synthesis or guideline pooled it, 32 citations in OpenAlex.

  1. Pooled it
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  4. Machine Learning for Biomedical Applications.Bioengineering (Basel, Switzerland) · 2024
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors at 4 institutions in 1 country.

Carlo RicciardiDepartment of Electrical Engineering and Information Technology, University of Naples "Federico II", 80125 Naples, Italy.ORCID 0000-0001-7290-6432
Alfonso Maria PonsiglioneDepartment of Electrical Engineering and Information Technology, University of Naples "Federico II", 80125 Naples, Italy.ORCID 0000-0003-1346-515X
Arianna ScalaDepartment of Public Health, University Hospital of Naples "Federico II", 80131 Naples, Italy.ORCID 0000-0001-9735-5227
Anna BorrelliHealth Department, University Hospital of Salerno "San Giovanni di Dio e Ruggi d'Aragona", 84126 Salerno, Italy.ORCID 0000-0002-2633-8507
Mario MisasiDepartment of the Orthopaedics, National Hospital (A.O.R.N.) Antonio Cardarelli, 80131 Naples, Italy.
Gaetano RomanoDepartment of the Orthopaedics, National Hospital (A.O.R.N.) Antonio Cardarelli, 80131 Naples, Italy.
Giuseppe RussoNational Hospital (A.O.R.N.) Antonio Cardarelli, 80131 Naples, Italy.
Maria TriassiDepartment of Public Health, University Hospital of Naples "Federico II", 80131 Naples, Italy.
Giovanni ImprotaDepartment of Public Health, University Hospital of Naples "Federico II", 80131 Naples, Italy.ORCID 0000-0002-9485-1687
Federico II University Hospital · ITOspedale Antonio Cardarelli · ITUniversity of Naples Federico II · ITOspedali Riuniti San Giovanni di Dio e Ruggi d'Aragona · IT

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Fractures of the femur are a frequent problem in elderly people, and it has been demonstrated that treating them with a diagnostic-therapeutic-assistance path within 48 h of admission to the hospital reduces complications and shortens the length of the hospital stay (LOS). In this paper, the preoperative data of 1082 patients were used to further extend the previous research and to generate several models that are capable of predicting the overall LOS: First, the LOS, measured in days, was predicted through a regression analysis; then, it was grouped by weeks and was predicted with a classification analysis. The KNIME analytics platform was applied to divide the dataset for a hold-out cross-validation, perform a multiple linear regression and implement machine learning algorithms. The best coefficient of determination (R

Indexed as

clinical pathwaymachine learningmultiple linear regressionorthopaedic

Identifiers

PMID35447732
PMCPMC9029792
OpenAlexW4223900207

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.