A method for rapid machine learning development for data mining with doctor-in-the-loop

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.

The Impact of Climate Change on Aeromedical Retrieval Services in Remote Northern Australia: Planning for a Hotter Future

“…The reason for retrieval was coded from retrieval service 'reason for referral' data electronically entered at the time that the referral was made by the triaging doctor. We used a machine learning algorithm on this 'reason for referral' data [20] to code all retrievals into medical, surgical, trauma, psychiatric, and obstetric emergencies on the basis of the specialist care type likely to be received once arriving at the hospital. Coding based on the ICD (International Classification of Disease) was not possible due to a lack of diagnostic information prior to hospital arrival as referrals are made in remote locations with minimal diagnostic capacity and sometimes by non-clinician referrers; thus, records of 'reason for referral' could not be more granular from a coding perspective.…”