<jupyter_start><jupyter_text>FDA Validation: Population Descriptions of Patients<jupyter_code>my_dicom = pydicom.dcmread(test_dicoms[2])
my_dicom
my_dicom = pydicom.dcmread(test_dicoms[3])
my_dicom
# Define lists.
age_list = []
gender_list = []

for i in test_dicoms:    
    ds = pydicom.dcmread(i)
    age_list.append(ds.PatientAge)
    gender_list.append(ds.PatientSex)
## Log the age list.
age_list
## Log the gender list.
gender_list
## We are done with this notebook!
import IPython
app = IPython.Application.instance()
app.kernel.do_shutdown(True)<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.10616438356164383, 'fraction_numerical': 0.003424657534246575, 'mean_word_length': 5.724137931034483, 'pattern_counts': {'":': 0, '<': 5, '<?xml version=': 0, '>': 5, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'chain_length': '1', 'dir': 'jupyter-structured-clean-dedup', 'id': '207058', 'license': 'MIT', 'n_tokens_mistral': 240, 'n_tokens_neox': 231, 'n_words': 44, 'path': '.ipynb_checkpoints/Inference-checkpoint.ipynb', 'repo_name': 'youldash/Pneumonia-Detection-from-Chest-X-Rays', 'size': '584'}