DHSC-Capstone/chapter2.qmd

# 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].

## 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].

### 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.

### 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].

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### 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   |

: Table 1. Summary of characteristics of machine learning algorithms
[@rabbani2022].

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## Reflex Testing

The laboratory diagnosis of thyroid dysfunction relies on the
measurement of circulating concentrations of thyrotropin (TSH), free
thyroxine (fT4), and, in some cases, free triiodothyronine (fT3). TSH
measurement is generally regarded as the most sensitive initial
laboratory test for screening individuals for thyroid hormone
abnormalities [@woodmansee2018]. TSH and fT4 have a complex, nonlinear
relationship, such that small changes in fT4 result in relatively large
changes in TSH [@plebani2020]. Many clinicians and laboratories check
TSH alone as the initial test for thyroid problems and then only add a
Free T4 measurement if the TSH is abnormal (outside the laboratory
normal reference range), this is known as reflex testing
[@woodmansee2018]. Reflex testing became possible with the advent of
laboratory information systems (LIS) that were sufficiently flexible to
permit modification of existing test requests at various stages of the
analytical process [@srivastava2010]. Reflex testing is widely used, the
major aim being to optimize the use of laboratory tests. However the
common practice of reflex testing relies simply on hard coded rules that
allow no flexibility. For instance in the case of TSH, free T4 will be
added to the patient order whenever the value falls outside of the
established laboratory reference range. This bring into the fold the
issue that the thresholds used to trigger reflex addition of tests vary
widely. In a study by Murphy he found the hypocalcaemic threshold to
trigger magnesium measurement varied from 1.50 mmol/L up to 2.20 mmol/L
[-@murphy2021]. Even allowing for differences in the nature, size and
staffing of hospital laboratories, and populations served, the extent of
the observed variation invites scrutiny [@murphy2021].

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