update book for online publishing

_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
 
 
 

abstract.qmd

@@ -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.

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.