update chapter 2 and index
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chapter2.qmd
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chapter2.qmd
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@ -112,31 +112,48 @@ computed by aggregating the errors across the entire validation data set
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### Machine Learning in the Clinical Laboratory
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### Machine Learning in the Clinical Laboratory
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<!--# Can I copy this table? -->
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| **Author and Year** | **Objective and Machine Learning Task** | **Best Model** | **Major Themes** |
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| **Author and Year** | **Objective and Machine Learning Task** | **Best Model** | **Major Themes** |
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|-------------|---------------------------------|-------------|-------------|
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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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| 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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Cao (2012) \|
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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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Triage manual review for urinalysis samples |
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Tree-based | Automation |
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Yang (2013) \|
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Predict normal reference ranges of ESR for various laboratories based on geographic and other clinical features |
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Neural Network |
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Interpretation |
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: Table 1. Summary of characteristics of machine learning algorithms
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: Table 1. Summary of characteristics of machine learning algorithms
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[@rabbani2022].
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[@rabbani2022].
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###
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####
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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 generally regarded as the most sensitive initial
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laboratory test for screening individuals for thyroid hormone
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abnormalities [@woodmansee2018]. TSH and fT4 have a complex, nonlinear
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relationship, such that small changes in fT4 result in relatively large
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changes in TSH [@plebani2020]. Many clinicians and laboratories check
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TSH alone as the initial test for thyroid problems and then only add a
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Free T4 measurement if the TSH is abnormal (outside the laboratory
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normal reference range), this is known as reflex testing
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[@woodmansee2018]. Reflex testing became possible with the advent of
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laboratory information systems (LIS) that were sufficiently flexible to
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permit modification of existing test requests at various stages of the
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analytical process [@srivastava2010]. Reflex testing is widely used, the
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major aim being to optimize the use of laboratory tests. However the
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common practice of reflex testing relies simply on hard coded rules that
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allow no flexibility. For instance in the case of TSH, free T4 will be
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added to the patient order whenever the value falls outside of the
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established laboratory reference range. This bring into the fold the
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issue that the thresholds used to trigger reflex addition of tests vary
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widely. In a study by Murphy he found the hypocalcaemic threshold to
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trigger magnesium measurement varied from 1.50 mmol/L up to 2.20 mmol/L
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[-@murphy2021]. Even allowing for differences in the nature, size and
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staffing of hospital laboratories, and populations served, the extent of
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the observed variation invites scrutiny [@murphy2021].
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26
index.qmd
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index.qmd
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@ -20,16 +20,11 @@ existing test requests at various stages of the analytical process
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testing, those tests added automatically by a set of rules established
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testing, those tests added automatically by a set of rules established
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in each laboratory. In most current clinical laboratories, reflex
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in each laboratory. In most current clinical laboratories, reflex
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testing is performed with a 'hard' cutoff, using a specifically
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testing is performed with a 'hard' cutoff, using a specifically
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established range with no means of flexibility [@murphy2021].
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established range with no means of flexibility [@murphy2021]. This study
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will examine the use of Machine learning to develop algorithms to allow
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flexibility for automatic reflex testing in clinical chemistry. The goal
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is to fill the gap between hard coded reflex testing and fully manual
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This study will examine the use of Machine learning to develop
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reflective testing using machine learning algorithms.
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algorithms to allow flexibility for automatic reflex testing in clinical
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chemistry. The goal is to fill the gap between hard coded reflex testing
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and fully manual reflective testing using machine learning algorithms.
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## Statement of Problem
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## Statement of Problem
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@ -77,15 +72,8 @@ widened to decrease the number of unnecessary Free T4 measurements
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performed. This reduction would reduce overall costs to the medical
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performed. This reduction would reduce overall costs to the medical
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system without likely causing negative consequences of missing the
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system without likely causing negative consequences of missing the
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detection of people with thyroid hormone abnormalities
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detection of people with thyroid hormone abnormalities
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[@whitneyw.woodmansee2018].
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[@woodmansee2018]. Even with the potential reduction in testing the
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hard-coded reflex rule still exists.
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The reduction in testing aside, the hard-coded rule still exists.
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Additionally, machine learning may predict missing values in a patient's
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record or even suggest further testing on a particular patient. In a
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study at Massachusetts General hospital, researchers predicted ferritin
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results based on already run laboratory testing [@charnaalbert2020].
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## Purposed Study Set Up
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## Purposed Study Set Up
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@ -214,3 +214,17 @@ PMID: 33045173}
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doi = {10.1214/ss/1009213726},
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doi = {10.1214/ss/1009213726},
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url = {http://dx.doi.org/10.1214/ss/1009213726}
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url = {http://dx.doi.org/10.1214/ss/1009213726}
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}
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}
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@article{woodmansee2018,
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title = {Determination of optimal TSH ranges for reflex Free T4 testing},
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author = {Woodmansee, Whitney W.},
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year = {2018},
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month = {02},
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date = {2018-02},
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journal = {Clinical Thyroidology for the Public},
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pages = {3--4},
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volume = {11},
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number = {2},
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url = {https://www.thyroid.org/patient-thyroid-information/ct-for-patients/february-2018/vol-11-issue-2-p-3-4/},
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langid = {canadian}
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}
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