51 lines
3 KiB
Text
51 lines
3 KiB
Text
# Abstract {.unnumbered}
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**Introduction**: This research study focuses on developing and testing
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a machine learning algorithm to predict the FT4 result or diagnose hyper
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or hypothyroidism in clinical chemistry. The goal is to bridge the gap
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between hard-coded reflex testing and fully manual reflective testing
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using machine learning algorithms. The significance of this study lies
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in the increasing healthcare costs, where laboratory services contribute
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significantly to medical decisions and budgets. By implementing
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automated reflex testing with machine learning algorithms, unnecessary
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laboratory tests can be reduced, resulting in cost savings and improved
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efficiency in the healthcare system.
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**Methods:** The study was performed using the Medical Information Mart
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for Intensive Care (MIMIC) database for data collection. The database
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consists of de-identified health-related data from critical care units.
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Eighteen variables, including patient demographics and lab values, were
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selected for the study. The data set was filtered based on specific
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criteria, and an outcome variable was created to determine if the Free
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T4 value was diagnostic. The data handling and modeling were performed
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using R and R Studio. Regression and classification models were screened
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using a random grid search to tune hyperparameters, and random forest
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models were selected as the final models based on their performance. The
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selected hyperparameters for both regression and classification models
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are specified.
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**Results:** The study analyzed a dataset of 11,340 observations,
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randomly splitting it into a training set (9071 observations) and a
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testing set (2269 observations) based on the Free T4 laboratory
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diagnostic value stratification. Classification algorithms were used to
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predict whether Free T4 would be diagnostic, achieving an accuracy of
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0.796 and an AUC of 0.918. The model had a sensitivity of 0.632 and a
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specificity of 0.892. The importance of individual analytes was
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assessed, with TSH being the most influential variable. The study also
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evaluated the predictability of Free T4 results using regression,
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achieving a Root Mean Square Error (RMSE) of 0.334. The predicted
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results had an accuracy of 0.790, similar to the classification model.
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**Discussion:** The study found that the diagnostic value of Free T4 can
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be accurately predicted 80% of the time using machine learning
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algorithms. However, the model had limitations in terms of sensitivity,
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with a false negative rate of 16% for elevated TSH results and 20% for
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decreased TSH results. The model achieved a specificity of 89% but did
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not meet the threshold for clinical deployment. The importance of
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individual analytes was explored, revealing unexpected correlations
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between TSH and hematology results, which could be valuable for future
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algorithms. Real-world applications could use predictive models in
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clinical decision-making systems to determine the need for Free T4 lab
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tests based on predictions and patient signs and symptoms. However,
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implementing such algorithms in existing laboratory information systems
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poses challenges.
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