Abstract
Objective As the storage of clinical data has transitioned into electronic formats, medical
informatics has become increasingly relevant in providing diagnostic aid. The purpose
of this review is to evaluate machine learning models that use text data for diagnosis
and to assess the diversity of the included study populations.
Methods We conducted a systematic literature review on three public databases. Two authors
reviewed every abstract for inclusion. Articles were included if they used or developed
machine learning algorithms to aid in diagnosis. Articles focusing on imaging informatics
were excluded.
Results From 2,260 identified papers, we included 78. Of the machine learning models used,
neural networks were relied upon most frequently (44.9%). Studies had a median population
of 661.5 patients, and diseases and disorders of 10 different body systems were studied.
Of the 35.9% (N = 28) of papers that included race data, 57.1% (N = 16) of study populations were majority White, 14.3% were majority Asian, and 7.1%
were majority Black. In 75% (N = 21) of papers, White was the largest racial group represented. Of the papers included,
43.6% (N = 34) included the sex ratio of the patient population.
Discussion With the power to build robust algorithms supported by massive quantities of clinical
data, machine learning is shaping the future of diagnostics. Limitations of the underlying
data create potential biases, especially if patient demographics are unknown or not
included in the training.
Conclusion As the movement toward clinical reliance on machine learning accelerates, both recording
demographic information and using diverse training sets should be emphasized. Extrapolating
algorithms to demographics beyond the original study population leaves large gaps
for potential biases.
Keywords
machine learning - diagnosis - computer assisted - clinical decision-making - electronic
health records - gender data
