# Discussion

Intro Paragraph - In
progress<!--# Write after I write everything else -->

## Summary of Results

The findings of this study indicate that within another commonly ordered
laboratory testing, the diagnostic value of Free T4 can be predicted
accurately 80% of the time. While examining only the elevated TSH
results, the algorithm had a false positive rate of 2% and a false
negative rate of 16%. In the original data, 76% of the time, the result
was non-diagnostic for Hypo-Thryodism. For the decreased TSH results,
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.

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 

While the current algorithm did not quite achieve an accuracy ready for
deployment, it is hypothesized that a system like this could be
implemented in clinical decision-making systems. As stated previously,
current practice is a physician (or other care providers) orders a TSH,
and if the value is outside laboratory-established reference ranges, the
Free T4 is added on. In the current study database, this reflex testing
was non-diagnostic 76% of the time for elevated TSH values and 67% for
decreased TSH values. Using clinical decision support first to predict
whether the Free T4 would be diagnostic, the care provider can use this
prediction and other patient signs and symptoms to determine if running
a Free T4 lab test is needed.

Similarly to Luo et al., the idea that the diagnostic information
offered by Free T4 often duplicates what other diagnostic tests provide
suggests a notion of "informationally" redundant testing [-@luo2016]. It
is speculated that informationally redundant testing occurs in various
diagnostic settings and diagnostic workups. It is much more frequent
than the more traditionally defined and narrowly framed notion of
redundant testing, which most often includes unintended duplications of
the same or similar tests. Under this narrow definition, redundant
laboratory testing is estimated to waste more than \$5 billion annually
in the United States, potentially dwarfed by the waste from
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

### MIMIC Database

While the MIMIC-IV database allowed for a first run of the study, it
does suffer from some issues compared to other patient results. The
MIMIC-IV database only contains results from ICU patients. Thus the
result may not represent normal results for patients typically screened
for hyper or hypothyroidism. In a study by Tyler et al., they found that
laboratory value ranges from critically ill patients deviate
significantly from those of healthy controls [-@tyler2018]. In their
study, distribution curves based on ICU data differed significantly from
the hospital standard range (mean \[SD\] overlapping coefficient, 0.51
\[0.32-0.69\]) [@tyler2018]. The data ranges from 2008 to 2019. During
this time, there could have been several unknown laboratory changes.
Often laboratories change methods, reference ranges, or even vendors.
None of this data is available in the MIMIC database. A change in method
or vendor could cause a shift in results, thus causing the algorithm to
assign incorrect outcomes.

The dataset also sufferers from incompleteness. Due to the fact the
database was not explicitly designed for this study, many patients do
not have complete sets of lab results. The study also had to pick and
choose lab tests to allow for as many sets of TSH and Free T4 results as
possible. For instance, in a study by Luo et al., a total of 42
different lab tests were selected for a Machine Learning study, compared
to only 16 selected for this study [-@luo2016]. The patient demographic
data also suffered from the same incompleteness. Due to this fact, only
the age and gender of the patient were used in developing the algorithm.
An early study by Schectman et al. found the mean TSH level of Blacks
was 0.4 (SE .053) mU/L lower than that for Whites after age and sex
adjustment, race explaining 6.5 percent of the variation in TSH levels
[-@schectman1991]. This variation in results should potentially be
included in developing a future algorithm. However, as it stands, the
current data set has incomplete data for patient race and ethnicity.

### Other Limitations

Should I write about my computer? - It is not capable of running the
more powerful algorithm

### Future Studies

Explain how to fix these issues.
