264 lines
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264 lines
17 KiB
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# Literature Review
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The application of machine learning in medicine has garnered enormous
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attention over the past decade [@rabbani2022]. Artificial intelligence
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(AI) and especially the subdiscipline of machine learning (ML) have
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become hot topics generating increasing interest among laboratory
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professionals. AI is a rather broad term and can be defined as the
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theory and development of computer systems to perform complex tasks
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typically requiring human intelligence, such as decision-making, visual
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perception, speech recognition, and translation between languages. ML is
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the science of programming, allowing computers to learn from data
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without being explicitly programmed [@debruyne2021]. The ever more
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extensive use of ML in clinical and basic medical research is reflected
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in the number of titles and abstracts of papers indexed on PubMed and
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published until 2006 as compared to 2007--2017, with a nearly 10-fold
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increase from 1000 to slightly more than 9000 articles in that time
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frame [@cabitza2018]. A literature review by Rabbani et al. found 39
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articles about the field of clinical chemistry in laboratory medicine
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between 2011 and 2021 [-@rabbani2022].
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## A Brief Primer on Machine Learning
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While this literature review aims not to provide an extensive
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representation of the mathematics behind ML algorithms, some basic
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concepts will be introduced to allow a sufficient understanding of the
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topics discussed in the paper. ML models can be classified into broad
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categories based on several criteria. These categories include the type
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of supervision, whether are not the algorithm can learn incrementally
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from an incoming stream of data (batch and online learning), and how
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they generalize (instance-based versus model-based learning)
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[@debruyne2021]. Rabbani et al. further classified the specific clinical
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chemistry uses into five broad categories, predicting laboratory test
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values, improving laboratory utilization, automating laboratory
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processes, promoting precision laboratory test interpretation, and
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improving laboratory medicine information systems [-@rabbani2022].
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### Supervised vs. Unsupervised Learning
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Four important categories can be distinguished based on the amount and
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type of supervision the models receive during training: supervised,
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unsupervised, semi-supervised, and reinforcement learning. Training data
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are labeled in supervised learning, and data samples are predicted with
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knowledge about the desired solutions [@debruyne2021]. They are
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typically used for classification and regression purposes. Some of the
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essential supervised algorithms are Linear Regression, Logistic
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Regression, K-Nearest Neighbors (KNN), Support Vector Machines (SVMs),
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Decision Trees (DTs), Random Forests (RFs), and supervised neural
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networks. In unsupervised learning, training data are unlabeled. In
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other words, observations are classified without prior data sample
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knowledge [@debruyne2021]. Unsupervised algorithms can be used for
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clustering (e.g., k-means clustering, density-based spatial clustering
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of applications with noise, hierarchical cluster analysis),
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visualization, and dimensionality reduction (e.g., principal component
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analysis (PCA), kernel PCA, locally linear embedding, t-distributed
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stochastic neighbor embedding), anomaly detection and novelty detection
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(e.g., one-class SVM, isolation forest) and association rule learning
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(e.g., apriori, eclat). However, some models can deal with partially
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labeled training data (i.e., semi-supervised learning). At last, in
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reinforcement learning, an agent (i.e., the learning system) learns what
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actions to take to optimize the outcome of a strategy (i.e., a policy)
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or to get the maximum cumulative reward [@debruyne2021]. This system
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resembles humans learning to ride a bike. It can typically be used in
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learning games, such as Go, chess, or even poker, or settings where the
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outcome is continuous rather than dichotomous (i.e., right or
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wrong)[@debruyne2021]. The proposed study will use supervised learning,
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as the data is labeled, and a particular outcome is expected.
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### Model Types
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#### Random Forests
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Random forests are an ensemble learning method that combines multiple
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decision trees to make predictions or classify data. It was first
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introduced by Leo Breiman in 2001 and has since gained popularity due to
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its robustness and accuracy [@liaw2002]. The algorithm creates many
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decision trees, each trained on a different subset of the data using
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bootstrap aggregating or "bagging." The random forests algorithm (for
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both classification and regression) is as follows:
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1. Draw ntree bootstrap samples from the original data.
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2. For each bootstrap sample, grow an unpruned classification or
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regression tree, with the following modification: at each node,
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rather than choosing the best split among all predictors, randomly
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sample mtry of the predictors and choose the best split from among
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those variables. (Bagging can be considered a special case of random
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forests obtained when mtry = p, the number of predictors.)
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3. Predict new data by aggregating the predictions of the ntree trees
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(i.e., majority votes for classification, average for regression)
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[@liaw2002].
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Random forests offer several advantages that make them well-suited for
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predictive modeling in healthcare:
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1. Robustness: Random forests are less prone to overfitting than
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individual decision trees. The aggregation of multiple trees helps
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to reduce the impact of outliers and noise in the data, resulting in
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more stable and reliable predictions.
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2. Variable Importance: Random forests provide estimates of the
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importance of different features in making predictions. This
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information aids in feature selection, identifying the most
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influential factors, and gaining insights into the underlying data
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relationships.
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3. Handling Complex Data: Random forests can take various data types,
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including categorical and numerical features, without extensive
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preprocessing. This flexibility makes them suitable for healthcare
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datasets often comprising diverse variables [@breiman2001a].
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#### Gradient Boosting
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Gradient boosting machines (GBMs) are an extremely popular machine
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learning algorithm that have proven successful across many domains and
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is one of the leading methods for winning Kaggle competitions. Whereas
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random forests build an ensemble of deep independent trees, GBMs build
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an ensemble of shallow trees in sequence, with each tree learning and
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improving on the previous one. Although shallow trees by themselves are
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relatively weak predictive models, they can be "boosted" to produce a
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powerful "committee" that, when appropriately tuned, is often hard to
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beat with other algorithms [@boehmke2020]. Gradient boosting involves
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the following key steps:
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1. Building an Initial Model: The algorithm creates an initial model,
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typically a simple decision tree, to make predictions.
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2. Calculation of Residuals: The residuals represent the differences
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between the actual values and the predictions of the current model.
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3. Fitting Subsequent Models: Subsequent weak models are trained to
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predict the residuals of the previous model. These models are fitted
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to minimize residual errors, typically using gradient descent
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optimization.
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4. Ensemble Creation: The predictions of all the weak models are
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combined by summing them, creating a strong predictive model.
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5. Iterative Improvement: The process is repeated for multiple
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iterations, with each new model attempting to reduce further the
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errors made by the previous models[@chen2016].
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Gradient boosting offers several advantages that include:
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1. High Predictive Accuracy: By combining multiple weak models,
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gradient boosting can achieve high predictive accuracy, often
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outperforming other machine learning algorithms.
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2. Handling Complex Relationships: Gradient boosting can capture
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complex nonlinear relationships between input and target variables,
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making it suitable for datasets with intricate patterns.
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3. Robustness to Outliers and Noise: The iterative nature of gradient
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boosting helps reduce the impact of outliers and noise in the data,
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leading to more robust predictions [@chen2016].
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### Machine Learning Workflow
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Since this study will focus on supervised learning, the review will
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focus on that. Machine learning can be broken into three broad steps,
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data cleaning and processing, training and testing the model, and
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finally, the model is evaluated, deployed, and monitored
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[@debruyne2021]. In the first phase, data is collected, cleaned, and
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labeled. Data cleaning or pre-processing is one of the essential steps
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in designing a reliable model [@debruyne2021]. Some examples of common
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pre-processing steps are the handling of missing data, detection of
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outliers, and encoding of categorical data. Data at this stage is also
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split into training and testing data, typically following somewhere near
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a 70-30 split. These two data sets are used for different portions of
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the rest of the model building. The Training set data is used to develop
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feature sets, train our algorithms, tune hyperparameters, compare
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models, and all the other activities required to choose a final model
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(e.g., the model we want to put into production) [@boehmke2020]. Once
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the final model is selected, the test set data is used to estimate an
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unbiased assessment of the model's performance, which we refer to as the
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generalization error [@boehmke2020]. Most time (as much as 80%) is
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invested into the data processes stage. After feature engineering, an ML
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model is trained and tested on the collected data in the second phase.
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Feature engineering is performed on the training set to select a good
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set of features to train on. The ML model will only be able to learn
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efficiently if the training data contains enough relevant features and
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minimal irrelevant ones [@géron2019]. The data is then run through
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various models, Linear Regression, Logistic Regression, K-Nearest
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Neighbors (KNN), Support Vector Machines (SVMs), Decision Trees (DTs),
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and Random Forests (RFs).
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Once a model is selected, the third phase begins to evaluate the model's
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performance. Historically, the performance of statistical models was
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primarily based on goodness-of-fit tests and the assessment of
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residuals. Unfortunately, misleading conclusions may follow from
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predictive models that pass these assessments [@breiman2001]. Today, it
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has become widely accepted that a more sound approach to assessing model
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performance is to determine the predictive accuracy via loss functions
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[@boehmke2020]. *Loss functions* are metrics that compare the predicted
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values to the actual value (the output of a loss function is often
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referred to as the error or pseudo residual). When performing resampling
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methods, we assess the predicted values for a validation set compared to
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the actual target value. The overall validation error of the model is
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computed by aggregating the errors across the entire validation data set
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[@boehmke2020]
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### Machine Learning in the Clinical Laboratory
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Rabbani et al. performed a comprehensive study of the current state of
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machine learning in laboratory medicine [-@rabbani2022]. This study
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revealed several exciting applications, including predicting laboratory
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test values, improving laboratory utilization, automating laboratory
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processes, promoting precision laboratory test interpretation, and
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improving laboratory medicine information systems. In these studies,
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tree-based learning algorithms and neural networks often performed best.
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@tbl-lab_ml displays the overview of their research.
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| **Author and Year** | **Objective and Machine Learning Task** | **Best Model** | **Major Themes** |
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|:--------------------|:-----------------------------------------------------------------------------------------------------------------------|:----------------------|:---------------------------------------|
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| Azarkhish (2012) | Predict iron deficiency anemia and serum iron levels from CBC indices | Neural Network | Prediction |
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| Cao (2012) | Triage manual review for urinalysis samples | Tree-based | Automation |
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| Yang (2013) | Predict normal reference ranges of ESR for various laboratories based on geographic and other clinical features | Neural Network | Interpretation |
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| Lidbury (2015) | Predict liver function test results from other tests in the panel, highlighting redundancy in the liver function panel | Tree-based | Prediction, Utilization |
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| Demirci (2016) | Classify whether critical lab result is valid or invalid using other lab values and clinical information | Neural Network | Automation, Interpretation, Validation |
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| Luo (2016) | Predict ferritin from other tests in iron panel | Tree-based | Prediction, Utilization |
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| Poole (2016) | Create personalized reference ranges that take into account patients' diagnoses | Unsupervised learning | Interpretation |
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| Parr (2018) | Automate mapping of Veterans Affair laboratory data to LOINC codes | Tree-based | Information systems, Automation |
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| Wilkes (2018) | Classify urine steroid profiles as normal or abnormal, and further interpret into specific disease processes | Tree-based | Interpretation, Automation |
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| Fillmore (2019) | Automate mapping of Veterans Affair laboratory data to LOINC codes | Tree-based | Information systems, Automation |
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| Lee (2019) | Predict LDL-C levels from a limited lipid panel more accurately than current gold standard equations | Neural Network | Interpretation, Prediction |
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| Xu (2019) | Identify redundant laboratory tests and predict their results as normal or abnormal | Tree-based | Prediction, Utilization |
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| Islam (2020) | Use prior ordering patterns to create an algorithm that can recommend best practice tests for specific diagnoses | Neural Network | Utilization |
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| Peng (2020) | Interpret newborn screening assays based on gestational age and other clinical information to reduce false positives | Tree-based | Interpretation, Utilization |
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| Wang (2020) | Automatically verify if lab test result is valid or invalid | Tree-based | Validation, Automation |
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| Dunn (2021) | Predict laboratory test results from wearable data | Tree-based | Prediction |
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| Fang (2021) | Classify blood specimen as clotted or not clotted based on coagulation indices | Neural Network | Quality control |
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| Farrell (2021) | Automatically identify mislabelled laboratory samples | Neural Network | Quality control, Automation |
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: Summary of characteristics of machine learning algorithms
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[@rabbani2022]. {#tbl-lab_ml}
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## Reflex Testing
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The laboratory diagnosis of thyroid dysfunction relies on the
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measurement of circulating concentrations of thyrotropin (TSH), free
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thyroxine (fT4), and, in some cases, free triiodothyronine (fT3). TSH
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measurement is the most sensitive initial laboratory test for screening
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individuals for thyroid hormone abnormalities [@woodmansee2018]. TSH and
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fT4 have a complex, nonlinear relationship, such that small changes in
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fT4 result in relatively significant changes in TSH [@plebani2020]. Many
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clinicians and laboratories check TSH alone as the initial test for
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thyroid problems and only add a Free T4 measurement if the TSH is
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abnormal (outside the laboratory's normal reference range). This is
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known as reflex testing [@woodmansee2018]. Reflex testing became
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possible with the advent of laboratory information systems (LIS) that
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were sufficiently flexible to permit modification of existing test
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requests at various stages of the analytical process [@srivastava2010].
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Reflex testing is widely used, the principal aim being to optimize the
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use of laboratory tests. However, the common practice of reflex testing
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relies simply on hard-coded rules that allow no flexibility. For
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instance, in the case of TSH, free T4 will be added to the patient order
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whenever the value falls outside the established laboratory reference
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range. This brings into the fold the issue that the thresholds used to
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trigger reflex addition of tests vary widely. In a study by Murphy, he
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found the hypocalcaemic threshold to trigger magnesium measurement
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varied from 1.50 mmol/L up to 2.20 mmol/L [-@murphy2021]. Even allowing
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for differences in the nature, size, and staffing of hospital
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laboratories and populations served, the extent of the observed
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variation invites scrutiny [@murphy2021].
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