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Kyle Belanger 2023-06-20 16:58:01 -04:00
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chapter2.qmd
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@ -211,14 +211,14 @@ tree-based learning algorithms and neural networks often performed best.
@tbl-lab_ml displays the overview of their research.
| **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 |
| Lidbury (2015) | Predict liver function test results from other tests in the panel, highlighting redundancy in the liver function panel | Tree-based | Prediction, Utilization |
| Demirci (2016) | Classify whether critical lab result is valid or invalid using other lab values and clinical information | Neural Network | Automation, Interpretation, Validation |
| Luo (2016) | Predict ferritin from other tests in iron panel | Tree-based | Prediction, Utilization |
| Poole (2016) | Create personalized reference ranges that take into account patients\' diagnoses | Unsupervised learning | Interpretation |
| Poole (2016) | Create personalized reference ranges that take into account patients' diagnoses | Unsupervised learning | Interpretation |
| Parr (2018) | Automate mapping of Veterans Affair laboratory data to LOINC codes | Tree-based | Information systems, Automation |
| Wilkes (2018) | Classify urine steroid profiles as normal or abnormal, and further interpret into specific disease processes | Tree-based | Interpretation, Automation |
| Fillmore (2019) | Automate mapping of Veterans Affair laboratory data to LOINC codes | Tree-based | Information systems, Automation |

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chapter5.qmd
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@ -15,11 +15,12 @@ the algorithm had a false positive rate of 8% and a false negative rate
of 20%. In the original data, 67% of the time, the result was
non-diagnostic for Hyper-Thryodism.
1846 - Hypo
423 - Hyper
9170 - High TSH (hypo)
While TSH was expected to be the most important variable in building
random forest models, it was quite unexpected that the next three values
would be Hematology results. In the clinical laboratory, TSH and CBCs
are often run on different analyzers and often in completely different
departments. Finding this slight correlation could be valuable to
building further algorithms.
## Real World Applications
@ -49,6 +50,13 @@ informationally redundant testing [@luo2016]. However, since Free T4 and
all other tests used in this study are performed on automated
instruments, the cost savings to the lab and patient may be minimal.
As Rabbani et al. study showed, Machine Learning in the Clinical
Laboratory is an emerging field. However, few existing studies relate to
predicting laboratory values based on other results [-@rabbani2022]. The
few studies that do exist follow a similar premise. All are trying to
reduce redundant laboratory testing and thus lower the cost burden on
the patient.
## Study Limitations
Section overview - In progress