Evidence map›Paper›PMID 33686873›Full record

ArticleJournal of diabetes science and technology2022

Machine Learning-Based Anomaly Detection Algorithms to Alert Patients Using Sensor Augmented Pump of Infusion Site Failures.

Lorenzo Meneghetti, Eyal Dassau, Francis J Doyle, Simone Del Favero

Registry-linked trialAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of diabetes science and technology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT06147583 (Pilot Study for the Evaluation of Algorithms for the Detection of Subcutaneous Insulin Pump Malfunctions in Subjects With Type 1 Diabetes), which is not on this map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
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.

NCT06147583 naunknown statusnot on this mapstarted 2023, after this paper: background citation

Pilot Study for the Evaluation of Algorithms for the Detection of Subcutaneous Insulin Pump Malfunctions in Subjects With Type 1 Diabetes

TypeinterventionalSponsorUniversity of PadovaRan2023 to 2024Enrolled20ConditionsDiabetes Mellitus, Type IArmsSimulation of an insulin pump failure
3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
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

4 authors.

Lorenzo MeneghettiDepartment of Information Engineering, University of Padua, Padua, Italy.
Eyal DassauHarvard John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USA.ORCID 0000-0001-5333-6892
Francis J DoyleHarvard John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USA.
Simone Del FaveroDepartment of Information Engineering, University of Padua, Padua, Italy.ORCID 0000-0002-8214-2752

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundPersonal insulin pumps have shown to be effective in improving the quality of therapy for people with type 1 diabetes (T1D). However, the safety of this technology is limited by the possible infusion site failures, which are linked with hyperglycemia and ketoacidosis. Thanks to the large availability of collected data provided by modern therapeutic technologies, machine learning algorithms have the potential to provide new way to identify failures early and avert adverse events.

methodsA clinical dataset (

resultsIn the optimal configuration, we obtained a performance of 0.75 Sensitivity (15 out of 20 total failures detected) and 0.08 FP/day, outperforming previously proposed literature algorithms. The algorithm was able to anticipate the replacement of the malfunctioning infusion sets by ~2 h on average.

conclusionsOn the considered dataset, the proposed algorithm showed the potential to improve the safety of patients treated with sensor-augmented pump systems.

Indexed as

Blood Glucose Self-MonitoringDiabetes Mellitus, Type 1AlgorithmsBlood GlucoseHumansHypoglycemic AgentsInsulinInsulin Infusion SystemsMachine LearningBlood GlucoseHypoglycemic AgentsInsulinanomaly detectionfault detectioninfusion site failuresmachine learningSAP

Identifiers

PMID33686873
PMCPMC9294564

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Registered trials

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.