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 # Literature Review
 
-The application of machine learning in medicine has garnered enormous attention over the past decade [@rabbani2022]. Artificial intelligence (AI) and especially the subdiscipline of machine learning (ML) have become hot topics that are generating increasing interest among laboratory professionals. AI is a rather broad term and can be defined as the theory and development of computer systems to perform complex tasks normally requiring human intelligence, such as decision-making, visual perception, speech recognition, and translation between languages. ML is the science of programming, which gives computers the ability to learn from data without being explicitly programmed [@debruyne2021]. The ever wider use of ML in clinical and basic medical research is reflected in the number of titles and abstracts of papers indexed on PubMed and published until 2006 as compared to 2007--2017, with a nearly 10-fold increase from 1000 to slightly more than 9000 articles in the that time frame [@cabitza2018]. A literature review by Rabbani et al. found 39 articles pertaining to the field of clinical chemistry in laboratory medicine between 2011 and 2021 [-@rabbani2022].
+The application of machine learning in medicine has garnered enormous attention over the past decade [@rabbani2022]. Artificial intelligence (AI) and especially the subdiscipline of machine learning (ML) have become hot topics generating increasing interest among laboratory professionals. AI is a rather broad term and can be defined as the theory and development of computer systems to perform complex tasks typically requiring human intelligence, such as decision-making, visual perception, speech recognition, and translation between languages. ML is the science of programming, allowing computers to learn from data without being explicitly programmed [@debruyne2021]. The ever more extensive use of ML in clinical and basic medical research is reflected in the number of titles and abstracts of papers indexed on PubMed and published until 2006 as compared to 2007--2017, with a nearly 10-fold increase from 1000 to slightly more than 9000 articles in that time frame [@cabitza2018]. A literature review by Rabbani et al. found 39 articles about the field of clinical chemistry in laboratory medicine between 2011 and 2021 \[-@rabbani2022\].
 
 ## A Brief Primer on Machine Learning
 
-While the aim of this literature review is not to provide an extensive representation of the mathematics behind ML algorithms, some basic concepts will be introduced to allow a sufficient understanding of the topics discussed in the paper. ML models can be classified into broad categories based on several criteria, such as the type of supervision, whether are not the algorithm can learn incrementally from an incoming stream of data (batch and online learning), and how they generalize (instance-based versus model-based learning) [@debruyne2021]. Rabbani et al. further classified the specfic clinical chemistry uses into five board categories, predicting laboratory test values, improving laboratory utilization, automating laboratory processes, promoting precision laboratory test interpretation, and improving laboratory medicine information systems [-@rabbani2022].
+While this literature review aims not to provide an extensive representation of the mathematics behind ML algorithms, some basic concepts will be introduced to allow a sufficient understanding of the topics discussed in the paper. ML models can be classified into broad categories based on several criteria. These categories include the type of supervision, whether are not the algorithm can learn incrementally from an incoming stream of data (batch and online learning), and how they generalize (instance-based versus model-based learning) [@debruyne2021]. Rabbani et al. further classified the specific clinical chemistry uses into five board categories, predicting laboratory test values, improving laboratory utilization, automating laboratory processes, promoting precision laboratory test interpretation, and improving laboratory medicine information systems \[-@rabbani2022\].
 
 ### Supervised vs Unsupervised Learning
 
-Four important categories can be distinguished based on the amount and type of supervision the models receive during training: supervised, unsupervised, semi-supervised, and reinforcement learning. In supervised learning, training data are labeled and data samples are predicted with knowledge about the desired solutions [@debruyne2021]. They are typically used for classification and regression purposes. Some of the most important supervised algorithms are Linear Regression, Logistic Regression, K-Nearest Neighbors (KNN), Support Vector Machines (SVMs), Decision Trees (DTs), Random Forests (RFs), and supervised neural networks. In unsupervised learning, training data are unlabeled. In other words, observations are classified without any prior data sample knowledge [@debruyne2021]. Unsupervised algorithms can be used for clustering (e.g. k-means clustering, density-based spatial clustering of applications with noise, hierarchical cluster analysis), visualization and dimensionality reduction (e.g. principal component analysis (PCA), kernel PCA, locally linear embedding, t-distributed stochastic neighbor embedding), anomaly detection and novelty detection (e.g. one-class SVM, isolation forest) and association rule learning (e.g. apriori, eclat). However, some models can deal with partially labeled training data (i.e. semi-supervised learning). At last, in reinforcement learning, an agent (i.e. the learning system) learns what actions to take to optimize the outcome of a strategy (i.e. a policy) or to get the maximum cumulative reward [@debruyne2021]. This system resembles humans learning to ride a bike and can typically be used in learning games, such as Go, chess, or even poker, or settings where the outcome is continuous rather than dichotomous (i.e. right or wrong)[@debruyne2021]. The proposed study will use supervised learning, as the data is labeled and an particular outcome is expected.
+Four important categories can be distinguished based on the amount and type of supervision the models receive during training: supervised, unsupervised, semi-supervised, and reinforcement learning. In supervised learning, training data are labeled, and data samples are predicted with knowledge about the desired solutions [@debruyne2021]. They are typically used for classification and regression purposes. Some of the essential supervised algorithms are Linear Regression, Logistic Regression, K-Nearest Neighbors (KNN), Support Vector Machines (SVMs), Decision Trees (DTs), Random Forests (RFs), and supervised neural networks. In unsupervised learning, training data are unlabeled. In other words, observations are classified without prior data sample knowledge [@debruyne2021]. Unsupervised algorithms can be used for clustering (e.g., k-means clustering, density-based spatial clustering of applications with noise, hierarchical cluster analysis), visualization, and dimensionality reduction (e.g., principal component analysis (PCA), kernel PCA, locally linear embedding, t-distributed stochastic neighbor embedding), anomaly detection and novelty detection (e.g., one-class SVM, isolation forest) and association rule learning (e.g. apriori, eclat). However, some models can deal with partially labeled training data (i.e., semi-supervised learning). At last, in reinforcement learning, an agent (i.e., the learning system) learns what actions to take to optimize the outcome of a strategy (i.e., a policy) or to get the maximum cumulative reward \[@debruyne2021\]. This system resembles humans learning to ride a bike and can typically be used in learning games, such as Go, chess, or even poker, or settings where the outcome is continuous rather than dichotomous (i.e., right or wrong)[@debruyne2021]. The proposed study will use supervised learning, as the data is labeled and a particular outcome is expected.
 
 ### Machine Learning Workflow
 
-Since this study will focus of supervised learning the review will focus on that. Machine learning can be broken into three board steps, data cleaning and processing, training and testing the model, finally the model is evaluated, deployed, and monitored [@debruyne2021]. In the first phase data is collected, cleaned, and labeled. Data cleaning or pre-processing is one of the most important steps in designing a reliable model [@debruyne2021]. Some examples of common pre-processing steps are handling of missing data, detection of outliers, and encoding of categorical data. Data at this stage is also split into training and testing data, typically following somewhere near a 70-30 split. These two data sets are used for different portions of the rest of model building. The Training set data is used to develop feature sets, train our algorithms, tune hyperparameters, compare models, and all of the other activities required to choose a final model (e.g., the model we want to put into production) [@boehmke2020]. Once the final model is chosen the test set data is used to estimate an unbiased assessment of the model's performance, which we refer to as the generalization error [@boehmke2020]. Most time (as much as 80%) is invested into the data processes stage. In the second phase, a ML model is trained and tested on the collected data after feature engineering. Feature engineering is performed on the training set to select a good set of features to train on. The ML model will only be able to learn efficiently if the training data contains enough relevant features and minimal irrelevant ones [@géron2019]. The data is then run through various models, Linear Regression, Logistic Regression, K-Nearest Neighbors (KNN), Support Vector Machines (SVMs), Decision Trees (DTs), Random Forests (RFs). Once a model is selected the third phase begins to evaluate the models performance. Historically, the performance of statistical models was largely based on goodness-of-fit tests and assessment of residuals. Unfortunately, misleading conclusions may follow from predictive models that pass these kinds of assessments [@breiman2001]. Today, it has become widely accepted that a more sound approach to assessing model performance is to assess the predictive accuracy via loss functions [@boehmke2020]. Loss functions are metrics that compare the predicted values to the actual value (the output of a loss function is often referred to as the error or pseudo residual). When performing resampling methods, we assess the predicted values for a validation set compared to the actual target value. The overall validation error of the model is computed by aggregating the errors across the entire validation data set [@boehmke2020].
+Since this study will focus on supervised learning, the review will focus on that. Machine learning can be broken into three board steps, data cleaning and processing, training and testing the model, and finally, the model is evaluated, deployed, and monitored [@debruyne2021]. In the first phase, data is collected, cleaned, and labeled. Data cleaning or pre-processing is one of the essential steps in designing a reliable model [@debruyne2021]. Some examples of common pre-processing steps are the handling of missing data, detection of outliers, and encoding of categorical data. Data at this stage is also split into training and testing data, typically following somewhere near a 70-30 split. These two data sets are used for different portions of the rest of the model building. The Training set data is used to develop feature sets, train our algorithms, tune hyperparameters, compare models, and all the other activities required to choose a final model (e.g., the model we want to put into production) [@boehmke2020]. Once the final model is chosen, the test set data is used to estimate an unbiased assessment of the model's performance, which we refer to as the generalization error [@boehmke2020]. Most time (as much as 80%) is invested into the data processes stage. After feature engineering, an ML model is trained and tested on the collected data in the second phase. Feature engineering is performed on the training set to select a good set of features to train on. The ML model will only be able to learn efficiently if the training data contains enough relevant features and minimal irrelevant ones \[@géron2019\]. The data is then run through various models, Linear Regression, Logistic Regression, K-Nearest Neighbors (KNN), Support Vector Machines (SVMs), Decision Trees (DTs), and Random Forests (RFs).
 
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+Once a model is selected, the third phase begins to evaluate the model's performance. Historically, the performance of statistical models was primarily based on goodness-of-fit tests and the assessment of residuals. Unfortunately, misleading conclusions may follow from predictive models that pass these assessments [@breiman2001]. Today, it has become widely accepted that a more sound approach to assessing model performance is to assess the predictive accuracy via loss functions [@boehmke2020]. *Loss functions* are metrics that compare the predicted values to the actual value (the output of a loss function is often referred to as the error or pseudo residual). When performing resampling methods, we assess the predicted values for a validation set compared to the actual target value. The overall validation error of the model is computed by aggregating the errors across the entire validation data set \[@boehmke2020\]
+
+.<!--# should I talk about Model types ?-->
 
 ### Machine Learning in the Clinical Laboratory
 
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 | **Author and Year** | **Objective and Machine Learning Task**                                                                         | **Best Model** | **Major Themes** |
-|---------------------|-----------------------------------------------------------------------------------------------------------------|----------------|------------------|
+|-----------|---------------------------------------|-----------|-----------|
 | Azarkhish (2012)    | Predict iron deficiency anemia and serum iron levels from CBC indices                                           | Neural Network | Prediction       |
 | Cao (2012)          | Triage manual review for urinalysis samples                                                                     | Tree-based     | Automation       |
 | Yang (2013)         | Predict normal reference ranges of ESR for various laboratories based on geographic and other clinical features | Neural Network | Interpretation   |