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import functools
import os
import re
from collections import OrderedDict
from typing import Dict, List, Optional
import duckdb
import pandas as pd
import torch
from .tables import ed_cxr_token_type_ids, ed_module_tables, mimic_cxr_tables
def mimic_cxr_text_path(dir, subject_id, study_id, ext='txt'):
return os.path.join(dir, 'p' + str(subject_id)[:2], 'p' + str(subject_id),
's' + str(study_id) + '.' + ext)
def format(text):
# Remove newline, tab, repeated whitespaces, and leading and trailing whitespaces:
text = re.sub(r'\n|\t', ' ', text)
text = re.sub(r'\s+', ' ', text)
text = text.strip()
return text
def rgetattr(obj, attr, *args):
def _getattr(obj, attr):
return getattr(obj, attr, *args)
return functools.reduce(_getattr, [obj] + attr.split('.'))
def df_to_tensor_index_columns(
df: pd.DataFrame,
tensor: torch.Tensor,
group_idx_to_y_idx: Dict,
groupby: str,
index_columns: List[str],
):
"""
Converts a dataframe with index columns to a tensor, where each index of the y-axis is determined by the
'groupby' column.
"""
assert len(group_idx_to_y_idx) == tensor.shape[0]
all_columns = index_columns + [groupby]
y_indices = [group_idx_to_y_idx[row[groupby]] for _, row in df[all_columns].iterrows() for i in index_columns if row[i] == row[i]]
x_indices = [row[i] for _, row in df[all_columns].iterrows() for i in index_columns if row[i] == row[i]]
tensor[y_indices, x_indices] = 1.0
return tensor
def df_to_tensor_value_columns(
df: pd.DataFrame,
tensor: torch.Tensor,
group_idx_to_y_idx: Dict,
groupby: str,
value_columns: List[str],
value_column_to_idx: Dict,
):
"""
Converts a dataframe with value columns to a tensor, where each index of the y-axis is determined by the
'groupby' column. The x-index is determined by a dictionary using the column name.
"""
assert len(group_idx_to_y_idx) == tensor.shape[0]
all_columns = value_columns + [groupby]
y_indices = [group_idx_to_y_idx[row[groupby]] for _, row in df[all_columns].iterrows() for i in value_columns if row[i] == row[i]]
x_indices = [value_column_to_idx[i] for _, row in df[all_columns].iterrows() for i in value_columns if row[i] == row[i]]
element_value = [row[i] for _, row in df[all_columns].iterrows() for i in value_columns if row[i] == row[i]]
tensor[y_indices, x_indices] = torch.tensor(element_value, dtype=tensor.dtype)
return tensor
class EDCXRSubjectRecords:
def __init__(
self,
database_path: str,
dataset_dir: Optional[str] = None,
reports_dir: Optional[str] = None,
token_type_ids_starting_idx: Optional[int] = None,
time_delta_map = lambda x: x,
debug: bool = False
):
self.database_path = database_path
self.dataset_dir = dataset_dir
self.reports_dir = reports_dir
self.time_delta_map = time_delta_map
self.debug = debug
self.connect = duckdb.connect(self.database_path, read_only=True)
self.streamlit_flag = False
self.clear_start_end_times()
# Module configurations:
self.ed_module_tables = ed_module_tables
self.mimic_cxr_tables = mimic_cxr_tables
lut_info = self.connect.sql("FROM lut_info").df()
for k, v in (self.ed_module_tables | self.mimic_cxr_tables).items():
if v.load and (v.value_columns or v.index_columns):
v.value_column_to_idx = {}
if v.index_columns:
v.total_indices = lut_info[lut_info['table_name'] == k]['end_index'].item() + 1
else:
v.total_indices = 0
for i in v.value_columns:
v.value_column_to_idx[i] = v.total_indices
v.total_indices += 1
# Token type identifiers:
self.token_type_to_token_type_id = ed_cxr_token_type_ids
if token_type_ids_starting_idx is not None:
self.token_type_to_token_type_id = {k: v + token_type_ids_starting_idx for k, v in self.token_type_to_token_type_id.items()}
def __len__(self):
return len(self.subject_ids)
def clear_start_end_times(self):
self.start_time, self.end_time = None, None
def admission_ed_stay_ids(self, hadm_id):
if hadm_id:
return self.connect.sql(f'SELECT stay_id FROM edstays WHERE subject_id = {self.subject_id} AND hadm_id = {hadm_id}').df()['stay_id'].tolist()
else:
return self.connect.sql(f'SELECT stay_id FROM edstays WHERE subject_id = {self.subject_id}').df()['stay_id'].tolist()
def subject_study_ids(self):
mimic_cxr = self.connect.sql(
f'SELECT study_id, study_datetime FROM mimic_cxr WHERE subject_id = {self.subject_id}',
).df()
if self.start_time and self.end_time:
mimic_cxr = self.filter_admissions_by_time_span(mimic_cxr, 'study_datetime')
mimic_cxr = mimic_cxr.drop_duplicates(subset=['study_id']).sort_values(by='study_datetime')
return dict(zip(mimic_cxr['study_id'], mimic_cxr['study_datetime']))
def load_ed_module(self, hadm_id=None, stay_id=None, reference_time=None):
if not self.start_time and stay_id is not None:
edstay = self.connect.sql(
f"""
SELECT intime, outtime
FROM edstays
WHERE stay_id = {stay_id}
"""
).df()
self.start_time = edstay['intime'].item()
self.end_time = edstay['outtime'].item()
self.load_module(self.ed_module_tables, hadm_id=hadm_id, stay_id=stay_id, reference_time=reference_time)
def load_mimic_cxr(self, study_id, reference_time=None):
self.load_module(self.mimic_cxr_tables, study_id=study_id, reference_time=reference_time)
if self.streamlit_flag:
self.report_path = mimic_cxr_text_path(self.reports_dir, self.subject_id, study_id, 'txt')
def load_module(self, module_dict, hadm_id=None, stay_id=None, study_id=None, reference_time=None):
for k, v in module_dict.items():
if self.streamlit_flag or v.load:
query = f"FROM {k}"
conditions = []
if hasattr(self, 'subject_id') and v.subject_id_filter:
conditions.append(f"subject_id={self.subject_id}")
if v.hadm_id_filter:
assert hadm_id is not None
conditions.append(f"hadm_id={hadm_id}")
if v.stay_id_filter:
assert stay_id is not None
conditions.append(f"stay_id={stay_id}")
if v.study_id_filter:
assert study_id is not None
conditions.append(f"study_id={study_id}")
if v.mimic_cxr_sectioned:
assert study_id is not None
conditions.append(f"study='s{study_id}'")
ands = ['AND'] * (len(conditions) * 2 - 1)
ands[0::2] = conditions
if conditions:
query += " WHERE ("
query += ' '.join(ands)
query += ")"
df = self.connect.sql(query).df()
if v.load:
columns = [v.groupby] + v.time_columns + v.index_columns + v.text_columns + v.value_columns + v.target_sections
# Use the starting time of the stay/admission as the time:
if v.use_start_time:
df['start_time'] = self.start_time
columns += ['start_time']
if reference_time is not None:
time_column = v.time_columns[-1] if not v.use_start_time else 'start_time'
# Remove rows that are after the reference time to maintain causality:
df = df[df[time_column] < reference_time]
if self.streamlit_flag:
setattr(self, k, df)
if v.load:
columns = list(dict.fromkeys(columns)) # remove repetitions.
df = df.drop(columns=df.columns.difference(columns), axis=1)
setattr(self, f'{k}_feats', df)
def return_ed_module_features(self, stay_id, reference_time=None):
example_dict = {}
if stay_id is not None:
self.load_ed_module(stay_id=stay_id, reference_time=reference_time)
for k, v in self.ed_module_tables.items():
if v.load:
df = getattr(self, f'{k}_feats')
if self.debug:
example_dict.setdefault('ed_tables', []).append(k)
if not df.empty:
assert f'{k}_index_value_feats' not in example_dict
# The y-index and the time for each group:
time_column = v.time_columns[-1] if not v.use_start_time else 'start_time'
group_idx_to_y_idx, group_idx_to_datetime = OrderedDict(), OrderedDict()
groups = df.dropna(subset=v.index_columns + v.value_columns + v.text_columns, axis=0, how='all')
groups = groups.drop_duplicates(subset=[v.groupby])[list(dict.fromkeys([v.groupby, time_column]))]
groups = groups.reset_index(drop=True)
for i, row in groups.iterrows():
group_idx_to_y_idx[row[v.groupby]] = i
group_idx_to_datetime[row[v.groupby]] = row[time_column]
if (v.index_columns or v.value_columns) and group_idx_to_y_idx:
example_dict[f'{k}_index_value_feats'] = torch.zeros(len(group_idx_to_y_idx), v.total_indices)
if v.index_columns:
example_dict[f'{k}_index_value_feats'] = df_to_tensor_index_columns(
df=df,
tensor=example_dict[f'{k}_index_value_feats'],
group_idx_to_y_idx=group_idx_to_y_idx,
groupby=v.groupby,
index_columns=v.index_columns,
)
if v.value_columns:
example_dict[f'{k}_index_value_feats'] = df_to_tensor_value_columns(
df=df,
tensor=example_dict[f'{k}_index_value_feats'],
group_idx_to_y_idx=group_idx_to_y_idx,
groupby=v.groupby,
value_columns=v.value_columns,
value_column_to_idx=v.value_column_to_idx
)
example_dict[f'{k}_index_value_token_type_ids'] = torch.full(
[example_dict[f'{k}_index_value_feats'].shape[0]],
self.token_type_to_token_type_id[k],
dtype=torch.long,
)
event_times = list(group_idx_to_datetime.values())
assert all([i == i for i in event_times])
time_delta = [self.compute_time_delta(i, reference_time) for i in event_times]
example_dict[f'{k}_index_value_time_delta'] = torch.tensor(time_delta)[:, None]
assert example_dict[f'{k}_index_value_feats'].shape[0] == example_dict[f'{k}_index_value_time_delta'].shape[0]
if v.text_columns:
for j in group_idx_to_y_idx.keys():
group_text = df[df[v.groupby] == j]
for i in v.text_columns:
column_text = [format(k) for k in list(dict.fromkeys(group_text[i].tolist())) if k is not None]
if column_text:
example_dict.setdefault(f'{k}_{i}', []).append(f"{', '.join(column_text)}.")
event_times = group_text[time_column].iloc[0]
time_delta = self.compute_time_delta(event_times, reference_time, to_tensor=False)
example_dict.setdefault(f'{k}_{i}_time_delta', []).append(time_delta)
return example_dict
def return_mimic_cxr_features(self, study_id, reference_time=None):
example_dict = {}
if study_id is not None:
self.load_mimic_cxr(study_id=study_id, reference_time=reference_time)
for k, v in self.mimic_cxr_tables.items():
if v.load:
df = getattr(self, f'{k}_feats')
if self.debug:
example_dict.setdefault('mimic_cxr_inputs', []).append(k)
if not df.empty:
# The y-index for each group:
group_idx_to_y_idx = OrderedDict()
groups = df.dropna(
subset=v.index_columns + v.value_columns + v.text_columns + v.target_sections,
axis=0,
how='all'
)
groups = groups.drop_duplicates(subset=[v.groupby])[[v.groupby]]
groups = groups.reset_index(drop=True)
for i, row in groups.iterrows():
group_idx_to_y_idx[row[v.groupby]] = i
if v.index_columns and group_idx_to_y_idx:
example_dict[f'{k}_index_value_feats'] = torch.zeros(len(group_idx_to_y_idx), v.total_indices)
if v.index_columns:
example_dict[f'{k}_index_value_feats'] = df_to_tensor_index_columns(
df=df,
tensor=example_dict[f'{k}_index_value_feats'],
group_idx_to_y_idx=group_idx_to_y_idx,
groupby=v.groupby,
index_columns=v.index_columns,
)
example_dict[f'{k}_index_value_token_type_ids'] = torch.full(
[example_dict[f'{k}_index_value_feats'].shape[0]],
self.token_type_to_token_type_id[k],
dtype=torch.long,
)
if v.text_columns:
for j in group_idx_to_y_idx.keys():
group_text = df[df[v.groupby] == j]
for i in v.text_columns:
column_text = [format(k) for k in list(dict.fromkeys(group_text[i].tolist())) if k is not None]
if column_text:
example_dict.setdefault(f'{i}', []).append(f"{', '.join(column_text)}.")
if v.target_sections:
for j in group_idx_to_y_idx.keys():
group_text = df[df[v.groupby] == j]
for i in v.target_sections:
column_text = [format(k) for k in list(dict.fromkeys(group_text[i].tolist())) if k is not None]
assert len(column_text) == 1
example_dict[i] = column_text[-1]
return example_dict
def compute_time_delta(self, event_time, reference_time, denominator = 3600, to_tensor=True):
"""
How to we transform time-delta inputs? It appears that minutes are used as the input to
a weight matrix in "Self-Supervised Transformer for Sparse and Irregularly Sampled Multivariate
Clinical Time-Series". This is almost confirmed by the CVE class defined here:
https://github.com/sindhura97/STraTS/blob/main/strats_notebook.ipynb, where the input has
a size of one.
"""
time_delta = reference_time - event_time
time_delta = time_delta.total_seconds() / (denominator)
assert isinstance(time_delta, float), f'time_delta should be float, not {type(time_delta)}.'
if time_delta < 0:
raise ValueError(f'time_delta should be greater than or equal to zero, not {time_delta}.')
time_delta = self.time_delta_map(time_delta)
if to_tensor:
time_delta = torch.tensor(time_delta)
return time_delta
def filter_admissions_by_time_span(self, df, time_column):
return df[(df[time_column] > self.start_time) & (df[time_column] <= self.end_time)]
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