def tensor_from_file(tm: TensorMap, data: PatientData) -> np.ndarray:
ici_dates = tm.time_series_filter(data)
values = data[ICI_PREFIX].loc[ici_dates.index, key].to_numpy()
tensor = np.zeros((len(values), len(tm.channel_map)))
for i, value in enumerate(values):
tensor[i, tm.channel_map[value]] = 1
return tensor
def tensor_from_file_aortic_stenosis_category(
tm: TensorMap,
data: PatientData,
) -> np.ndarray:
"""Categorizes aortic stenosis as none, mild, moderate, or severe
from aortic valve mean gradient"""
echo_dates = tm.time_series_filter(data)
av_mean_gradient_key = continuous_tmap_names_and_keys["av_mean_gradient"]
av_mean_gradients = data[ECHO_PREFIX].loc[echo_dates.index,
av_mean_gradient_key]
# Initialize tensor array of zeros where each row is the channel map
num_categories = len(tm.channel_map)
tensor = np.zeros((len(av_mean_gradients), num_categories))
# Iterate through the peak velocities and mean gradients from all echos
for idx, av_mean_gradient in enumerate(av_mean_gradients):
if av_mean_gradient < 20:
category = "mild"
elif 20 <= av_mean_gradient < 40:
category = "moderate"
elif av_mean_gradient >= 40:
category = "severe"
else:
continue
tensor[idx, tm.channel_map[category]] = 1
return tensor
def admin_age_tensor_from_file(
tm: TensorMap, data: PatientData, **kwargs
) -> np.ndarray:
if "visits" in kwargs:
visits = kwargs["visits"]
if isinstance(visits, str):
visits = [visits]
else:
visits = tm.time_series_filter(data)
shape = (len(visits),) + tm.shape
tensor = np.zeros(shape)
for i, visit in enumerate(visits):
try:
path = f"{tm.path_prefix}/{visit}"
admit_date = get_unix_timestamps(data[path].attrs["admin_date"])
birth_date = get_unix_timestamps(data[path].attrs["birth_date"])
age = admit_date - birth_date
tensor[i] = age / 60 / 60 / 24 / 365
except (ValueError, KeyError) as e:
logging.debug(f"Could not get age from {data.id}/{visit}")
logging.debug(e)
return tensor
def tff_any(tm: TensorMap, data: PatientData) -> np.ndarray:
surgery_dates = tm.time_series_filter(data)
tensor = data[STS_PREFIX].loc[surgery_dates.index,
sts_outcome_keys].to_numpy()
tensor = tensor.any(axis=1).astype(int)
tensor = np.array([binarize("any", x) for x in tensor])
if not is_dynamic_shape(tm):
tensor = tensor[0]
return tensor
def tensor_from_file(tm: TensorMap, data: PatientData) -> np.ndarray:
surgery_dates = tm.time_series_filter(data)
tensor = data[STS_PREFIX].loc[surgery_dates.index, key].to_numpy()
if tm.channel_map is None:
raise ValueError(f"{tm.name} channel map is None")
tensor = np.array([one_hot(tm.channel_map, x) for x in tensor])
if not is_dynamic_shape(tm):
tensor = tensor[0]
return tensor
def tensor_from_file(tm: TensorMap, data: PatientData) -> np.ndarray:
surgery_dates = tm.time_series_filter(data)
tensor = data[STS_PREFIX].loc[surgery_dates.index, key].to_numpy()
tensor = np.array(
[binarize(key, x, negative_value, positive_value)
for x in tensor], )
if not is_dynamic_shape(tm):
tensor = tensor[0]
return tensor
def sex_double_tensor_from_file(tm: TensorMap, data: PatientData) -> np.ndarray:
visit = tm.time_series_filter(data)[0]
shape = (2,) + tm.shape
tensor = np.zeros(shape)
path = f"{tm.path_prefix}/{visit}"
value = data[path].attrs["sex"]
tensor[:, tm.channel_map[value.lower()]] = np.array([1, 1])
return tensor
def tensor_from_file(tm: TensorMap, data: PatientData) -> np.ndarray:
echo_dates = tm.time_series_filter(data)
mean_gradient_key = continuous_tmap_names_and_keys["av_mean_gradient"]
mean_gradients = data[ECHO_PREFIX].loc[echo_dates.index,
mean_gradient_key]
tensor = np.zeros((len(echo_dates), 2))
for idx, mean_gradient in enumerate(mean_gradients):
if np.isnan(mean_gradient):
continue
tensor[idx, 1 if low <= mean_gradient < high else 0] = 1
return tensor
def _tensor_from_file(tm: TensorMap, data: PatientData, **kwargs) -> np.ndarray:
unix_dates = kwargs.get("unix_dates")
if "visits" in kwargs:
visits = kwargs["visits"]
if isinstance(visits, str):
visits = [visits]
else:
visits = tm.time_series_filter(data)
temp = None
finalize = False
if tm.is_timeseries:
temp = [data[f"{tm.path_prefix}/{v}"].attrs[key] for v in visits]
max_len = max(map(len, temp))
shape = (len(visits), max_len)
else:
shape = (len(visits),) + tm.shape
if tm.is_categorical or tm.is_continuous or (tm.is_event and unix_dates):
tensor = np.zeros(shape)
elif tm.is_language or (tm.is_event and not unix_dates):
tensor = np.full(shape, "", object)
finalize = True
elif tm.is_timeseries and temp is not None:
if isinstance(temp[0][0], np.number):
tensor = np.zeros(shape)
else:
tensor = np.full(shape, "", object)
finalize = True
else:
raise ValueError("Unknown interpretation for static ICU data")
for i, visit in enumerate(visits):
try:
path = f"{tm.path_prefix}/{visit}"
value = data[path].attrs[key] if temp is None else temp[i]
if tm.channel_map:
tensor[i, tm.channel_map[value.lower()]] = 1
elif tm.is_event and unix_dates:
tensor[i] = get_unix_timestamps(value)
else:
tensor[i] = value
except (ValueError, KeyError) as e:
logging.debug(f"Error getting {key} from {data.id}/{visit}")
logging.debug(e)
if finalize:
tensor = np.array(tensor, dtype=str)
return tensor
def length_of_stay_tensor_from_file(tm: TensorMap, data: PatientData) -> np.ndarray:
visits = tm.time_series_filter(data)
shape = (len(visits),) + tm.shape
tensor = np.zeros(shape)
for i, visit in enumerate(visits):
try:
path = f"{tm.path_prefix}/{visit}"
end_date = get_unix_timestamps(data[path].attrs["end_date"])
start_date = get_unix_timestamps(data[path].attrs["admin_date"])
tensor[i] = (end_date - start_date) / 60 / 60
except (ValueError, KeyError) as e:
logging.debug(f"Could not get length of stay from {data.id}/{visit}")
logging.debug(e)
return tensor
def _days_between_tensor_from_file(tm: TensorMap,
data: PatientData) -> np.ndarray:
# Time series filter will be updated to return days for this tensor from file
days = tm.time_series_filter(data)
return days.to_numpy()[:, None]
def tensor_from_file(tm: TensorMap, data: PatientData) -> np.ndarray:
echo_dates = tm.time_series_filter(data)
tensor = data[ECHO_PREFIX].loc[echo_dates.index, key].to_numpy()
if is_dynamic_shape(tm):
tensor = tensor[:, None]
return tensor
def mrn_tensor_from_file(tm: TensorMap, data: PatientData) -> np.ndarray:
mrn = str(data.id)
return np.array([mrn] * len(tm.time_series_filter(data)))[:, None]
def visit_tensor_from_file(tm: TensorMap, data: PatientData) -> np.ndarray:
return np.array(tm.time_series_filter(data))[:, None]
def tensor_from_file(tm: TensorMap, data: PatientData) -> np.ndarray:
ici_dates = tm.time_series_filter(data)
positive_values = data[ICI_PREFIX].loc[ici_dates.index, key].to_numpy()
negative_values = 1 - positive_values
tensor = np.array([negative_values, positive_values]).T
return tensor
def tensor_from_file(tm: TensorMap, data: PatientData) -> np.ndarray:
ici_dates = tm.time_series_filter(data)
tensor = data[ICI_PREFIX].loc[ici_dates.index, key].to_numpy()[:, None]
return tensor
