Please use this identifier to cite or link to this item:
|Title:||A method for rapid machine learning development for data mining with doctor-in-the-loop.|
|Citation:||Copyright: © 2023 Bull et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.|
PLoS One. 2023 May 10;18(5):e0284965. doi: 10.1371/journal.pone.0284965. eCollection 2023.
|Abstract:||Classifying free-text from historical databases into research-compatible formats is a barrier for clinicians undertaking audit and research projects. The aim of this study was to (a) develop interactive active machine-learning model training methodology using readily available software that was (b) easily adaptable to a wide range of natural language databases and allowed customised researcher-defined categories, and then (c) evaluate the accuracy and speed of this model for classifying free text from two unique and unrelated clinical notes into coded data. A user interface for medical experts to train and evaluate the algorithm was created. Data requiring coding in the form of two independent databases of free-text clinical notes, each of unique natural language structure. Medical experts defined categories relevant to research projects and performed 'label-train-evaluate' loops on the training data set. A separate dataset was used for validation, with the medical experts blinded to the label given by the algorithm. The first dataset was 32,034 death certificate records from Northern Territory Births Deaths and Marriages, which were coded into 3 categories: haemorrhagic stroke, ischaemic stroke or no stroke. The second dataset was 12,039 recorded episodes of aeromedical retrieval from two prehospital and retrieval services in Northern Territory, Australia, which were coded into 5 categories: medical, surgical, trauma, obstetric or psychiatric. For the first dataset, macro-accuracy of the algorithm was 94.7%. For the second dataset, macro-accuracy was 92.4%. The time taken to develop and train the algorithm was 124 minutes for the death certificate coding, and 144 minutes for the aeromedical retrieval coding. This machine-learning training method was able to classify free-text clinical notes quickly and accurately from two different health datasets into categories of relevance to clinicians undertaking health service research.|
|Click to open Pubmed Article:||https://www.ezpdhcs.nt.gov.au/login?url=https://www.ncbi.nlm.nih.gov/pubmed/37163511|
|Journal title:||PloS one|
|Appears in Collections:||(a) NT Health Research Collection|
Files in This Item:
There are no files associated with this item.
Items in ePublications are protected by copyright, with all rights reserved, unless otherwise indicated.