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update chapter 2 and index

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Kyle Belanger 2022-09-11 16:19:11 -04:00
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61
chapter2.qmd
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@ -112,31 +112,48 @@ computed by aggregating the errors across the entire validation data set
### Machine Learning in the Clinical Laboratory
<!--# Can I copy this table? -->
<!--# Table needs to be modified -->
| **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 |
| **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].
###
<!--# Need to fill in this section -->
####
## 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].
<!--# insert table and study from strivastava about hypo/hyper thyroid -->
<!--# data from woodmansee and plebani -->

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index.qmd
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@ -20,16 +20,11 @@ existing test requests at various stages of the analytical process
testing, those tests added automatically by a set of rules established
in each laboratory. In most current clinical laboratories, reflex
testing is performed with a 'hard' cutoff, using a specifically
established range with no means of flexibility [@murphy2021].
<!--# Rewrite this section -->
This study will examine the use of Machine learning to develop
algorithms to allow flexibility for automatic reflex testing in clinical
chemistry. The goal is to fill the gap between hard coded reflex testing
and fully manual reflective testing using machine learning algorithms.
<!--# -->
established range with no means of flexibility [@murphy2021]. This study
will examine the use of Machine learning to develop algorithms to allow
flexibility for automatic reflex testing in clinical chemistry. The goal
is to fill the gap between hard coded reflex testing and fully manual
reflective testing using machine learning algorithms.
## Statement of Problem
@ -77,15 +72,8 @@ widened to decrease the number of unnecessary Free T4 measurements
performed. This reduction would reduce overall costs to the medical
system without likely causing negative consequences of missing the
detection of people with thyroid hormone abnormalities
[@whitneyw.woodmansee2018].
<!--# This paragraph should be written and most removed-->
The reduction in testing aside, the hard-coded rule still exists.
Additionally, machine learning may predict missing values in a patient's
record or even suggest further testing on a particular patient. In a
study at Massachusetts General hospital, researchers predicted ferritin
results based on already run laboratory testing [@charnaalbert2020].
[@woodmansee2018]. Even with the potential reduction in testing the
hard-coded reflex rule still exists.
## Purposed Study Set Up

14
references.bib
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@ -214,3 +214,17 @@ PMID: 33045173}
doi = {10.1214/ss/1009213726},
url = {http://dx.doi.org/10.1214/ss/1009213726}
}
@article{woodmansee2018,
title = {Determination of optimal TSH ranges for reflex Free T4 testing},
author = {Woodmansee, Whitney W.},
year = {2018},
month = {02},
date = {2018-02},
journal = {Clinical Thyroidology for the Public},
pages = {3--4},
volume = {11},
number = {2},
url = {https://www.thyroid.org/patient-thyroid-information/ct-for-patients/february-2018/vol-11-issue-2-p-3-4/},
langid = {canadian}
}