diff --git a/_quarto.yml b/_quarto.yml index ccca8ee..55803da 100644 --- a/_quarto.yml +++ b/_quarto.yml @@ -7,7 +7,6 @@ book: date: "8/2/2022" chapters: - index.qmd - - abstract.qmd - chapter1.qmd - chapter2.qmd - chapter3.qmd @@ -24,11 +23,11 @@ format: html: theme: journal default-image-extension: png - docx: - reference-doc: extras/custom-reference.docx - number-sections: true - number-depth: 1 - default-image-extension: emf + # docx: + # reference-doc: extras/custom-reference.docx + # number-sections: true + # number-depth: 1 + # default-image-extension: emf diff --git a/abstract.qmd b/abstract.qmd deleted file mode 100644 index 8e4a479..0000000 --- a/abstract.qmd +++ /dev/null @@ -1,51 +0,0 @@ -## Abstract {.unnumbered} - -**Introduction**: This research study focuses on developing and testing -a machine learning algorithm to predict the FT4 result or diagnose hyper -or hypothyroidism in clinical chemistry. The goal is to bridge the gap -between hard-coded reflex testing and fully manual reflective testing -using machine learning algorithms. The significance of this study lies -in the increasing healthcare costs, where laboratory services contribute -significantly to medical decisions and budgets. By implementing -automated reflex testing with machine learning algorithms, unnecessary -laboratory tests can be reduced, resulting in cost savings and improved -efficiency in the healthcare system. - -**Methods:** The study was performed using the Medical Information Mart -for Intensive Care (MIMIC) database for data collection. The database -consists of de-identified health-related data from critical care units. -Eighteen variables, including patient demographics and lab values, were -selected for the study. The data set was filtered based on specific -criteria, and an outcome variable was created to determine if the Free -T4 value was diagnostic. The data handling and modeling were performed -using R and R Studio. Regression and classification models were screened -using a random grid search to tune hyperparameters, and random forest -models were selected as the final models based on their performance. The -selected hyperparameters for both regression and classification models -are specified. - -**Results:** The study analyzed a dataset of 11,340 observations, -randomly splitting it into a training set (9071 observations) and a -testing set (2269 observations) based on the Free T4 laboratory -diagnostic value stratification. Classification algorithms were used to -predict whether Free T4 would be diagnostic, achieving an accuracy of -0.796 and an AUC of 0.918. The model had a sensitivity of 0.632 and a -specificity of 0.892. The importance of individual analytes was -assessed, with TSH being the most influential variable. The study also -evaluated the predictability of Free T4 results using regression, -achieving a Root Mean Square Error (RMSE) of 0.334. The predicted -results had an accuracy of 0.790, similar to the classification model. - -**Discussion:** The study found that the diagnostic value of Free T4 can -be accurately predicted 80% of the time using machine learning -algorithms. However, the model had limitations in terms of sensitivity, -with a false negative rate of 16% for elevated TSH results and 20% for -decreased TSH results. The model achieved a specificity of 89% but did -not meet the threshold for clinical deployment. The importance of -individual analytes was explored, revealing unexpected correlations -between TSH and hematology results, which could be valuable for future -algorithms. Real-world applications could use predictive models in -clinical decision-making systems to determine the need for Free T4 lab -tests based on predictions and patient signs and symptoms. However, -implementing such algorithms in existing laboratory information systems -poses challenges. diff --git a/chapter3_files/figure-docx/unnamed-chunk-3-1.png b/chapter3_files/figure-docx/unnamed-chunk-3-1.png new file mode 100644 index 0000000..06da413 Binary files /dev/null and b/chapter3_files/figure-docx/unnamed-chunk-3-1.png differ diff --git a/index.qmd b/index.qmd index aaf6f0d..30946da 100644 --- a/index.qmd +++ b/index.qmd @@ -1 +1,51 @@ -# {.unnumbered .unlisted} +# Abstract {.unnumbered} + +**Introduction**: This research study focuses on developing and testing +a machine learning algorithm to predict the FT4 result or diagnose hyper +or hypothyroidism in clinical chemistry. The goal is to bridge the gap +between hard-coded reflex testing and fully manual reflective testing +using machine learning algorithms. The significance of this study lies +in the increasing healthcare costs, where laboratory services contribute +significantly to medical decisions and budgets. By implementing +automated reflex testing with machine learning algorithms, unnecessary +laboratory tests can be reduced, resulting in cost savings and improved +efficiency in the healthcare system. + +**Methods:** The study was performed using the Medical Information Mart +for Intensive Care (MIMIC) database for data collection. The database +consists of de-identified health-related data from critical care units. +Eighteen variables, including patient demographics and lab values, were +selected for the study. The data set was filtered based on specific +criteria, and an outcome variable was created to determine if the Free +T4 value was diagnostic. The data handling and modeling were performed +using R and R Studio. Regression and classification models were screened +using a random grid search to tune hyperparameters, and random forest +models were selected as the final models based on their performance. The +selected hyperparameters for both regression and classification models +are specified. + +**Results:** The study analyzed a dataset of 11,340 observations, +randomly splitting it into a training set (9071 observations) and a +testing set (2269 observations) based on the Free T4 laboratory +diagnostic value stratification. Classification algorithms were used to +predict whether Free T4 would be diagnostic, achieving an accuracy of +0.796 and an AUC of 0.918. The model had a sensitivity of 0.632 and a +specificity of 0.892. The importance of individual analytes was +assessed, with TSH being the most influential variable. The study also +evaluated the predictability of Free T4 results using regression, +achieving a Root Mean Square Error (RMSE) of 0.334. The predicted +results had an accuracy of 0.790, similar to the classification model. + +**Discussion:** The study found that the diagnostic value of Free T4 can +be accurately predicted 80% of the time using machine learning +algorithms. However, the model had limitations in terms of sensitivity, +with a false negative rate of 16% for elevated TSH results and 20% for +decreased TSH results. The model achieved a specificity of 89% but did +not meet the threshold for clinical deployment. The importance of +individual analytes was explored, revealing unexpected correlations +between TSH and hematology results, which could be valuable for future +algorithms. Real-world applications could use predictive models in +clinical decision-making systems to determine the need for Free T4 lab +tests based on predictions and patient signs and symptoms. However, +implementing such algorithms in existing laboratory information systems +poses challenges.