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The dataset viewer is not available for this split.
Cannot extract the features (columns) for the split 'train' of the config 'default' of the dataset.
Error code:   FeaturesError
Exception:    ArrowTypeError
Message:      Expected bytes, got a 'dict' object
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/split/first_rows.py", line 322, in compute
                  compute_first_rows_from_parquet_response(
                File "/src/services/worker/src/worker/job_runners/split/first_rows.py", line 88, in compute_first_rows_from_parquet_response
                  rows_index = indexer.get_rows_index(
                File "/src/libs/libcommon/src/libcommon/parquet_utils.py", line 640, in get_rows_index
                  return RowsIndex(
                File "/src/libs/libcommon/src/libcommon/parquet_utils.py", line 521, in __init__
                  self.parquet_index = self._init_parquet_index(
                File "/src/libs/libcommon/src/libcommon/parquet_utils.py", line 538, in _init_parquet_index
                  response = get_previous_step_or_raise(
                File "/src/libs/libcommon/src/libcommon/simple_cache.py", line 591, in get_previous_step_or_raise
                  raise CachedArtifactError(
              libcommon.simple_cache.CachedArtifactError: The previous step failed.
              
              During handling of the above exception, another exception occurred:
              
              Traceback (most recent call last):
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/packaged_modules/json/json.py", line 122, in _generate_tables
                  pa_table = paj.read_json(
                File "pyarrow/_json.pyx", line 308, in pyarrow._json.read_json
                File "pyarrow/error.pxi", line 154, in pyarrow.lib.pyarrow_internal_check_status
                File "pyarrow/error.pxi", line 91, in pyarrow.lib.check_status
              pyarrow.lib.ArrowInvalid: JSON parse error: Column() changed from object to array in row 0
              
              During handling of the above exception, another exception occurred:
              
              Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/split/first_rows.py", line 240, in compute_first_rows_from_streaming_response
                  iterable_dataset = iterable_dataset._resolve_features()
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 2216, in _resolve_features
                  features = _infer_features_from_batch(self.with_format(None)._head())
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 1239, in _head
                  return _examples_to_batch(list(self.take(n)))
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 1389, in __iter__
                  for key, example in ex_iterable:
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 1044, in __iter__
                  yield from islice(self.ex_iterable, self.n)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 282, in __iter__
                  for key, pa_table in self.generate_tables_fn(**self.kwargs):
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/packaged_modules/json/json.py", line 162, in _generate_tables
                  pa_table = pa.Table.from_pydict(mapping)
                File "pyarrow/table.pxi", line 1813, in pyarrow.lib._Tabular.from_pydict
                File "pyarrow/table.pxi", line 5347, in pyarrow.lib._from_pydict
                File "pyarrow/array.pxi", line 373, in pyarrow.lib.asarray
                File "pyarrow/array.pxi", line 343, in pyarrow.lib.array
                File "pyarrow/array.pxi", line 42, in pyarrow.lib._sequence_to_array
                File "pyarrow/error.pxi", line 154, in pyarrow.lib.pyarrow_internal_check_status
                File "pyarrow/error.pxi", line 91, in pyarrow.lib.check_status
              pyarrow.lib.ArrowTypeError: Expected bytes, got a 'dict' object

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🤗 + 🧑‍⚕️🖊️📚🩺🇮🇹 = PsyNIT

From this repository you can download the PsyNIT (Psychiatric Ner for ITalian) dataset.

PsyNIT is a native Italian NER (Named Entity Recognition) dataset, composed by Italian Research Hospital Centro San Giovanni Di Dio Fatebenefratelli. It was created starting from 100 electronic medical reports, manually anonymized (removing personal patient data, physicians’ references, dates, and locations). The anonymized documents were annotated by a psychologist with 10 years of experience. The electronic medical reports contained various information about patients: demographic variables, medical history, results of tests and medical examinations, reports from medical exams, and more. Four sections of such documents were extracted:

  • Pharmacological history, usually a structured list of medications that the patient is taking and their dosages.
  • Remote pathologic history and active disease, usually a list of past and current relevant diseases.
  • Cognitive proximate pathological history, typically unstructured, includes medical examinations the patient has undergone. It also includes information about the patient’s personal life, such as marital status, daily habits, sleep disorders, and any relevant aspects of his/her behavior.
  • Psychological evaluation, typically unstructured, reports the result of (neuro)psychological examinations, together with comments from the attending physician.

The class of entities in PsyNIT are:

  • Diagnosis and comorbidities (779 examples, 13.23% of the dataset), including medical concepts that encompass and identify a disease with a clinically classified definition. For our purposes, this class has been used to annotate both the main disease for which the medical report was written, and any other disease or medical condition, pre-existing or coexisting, from which the patient suffers. Examples are : “Neoplasia vescicale” (bladder neoplasia), “Ipoacusia” (hearing loss), “Ipofolatemia” (hypopholatemia).
  • Cognitive symptoms (2386 examples, 40.52% of the dataset), that reflect the individual’s abilities in different cognitive domains. These are various aspects of high-level intellectual functioning, such as processing speed, reasoning, judgment, attention, memory, knowledge, decision-making, planning, language production and comprehension and visuospatial abilities. In neuropsychiatric or cognitive disorders, various cognitive symptoms can be observed, showing the cognitive impairment of patients in different cognitive domains. Examples include: “Anomia” (anomie), “Capacità introspettiva” (introspective ability), “Organizzazione e pianificazione visuospaziale” (visuospatial organization and planning).
  • Neuropsychiatric symptoms (707 examples, 12.01% of the dataset), that refer to a set of non-cognitive symptoms that occur in the majority of patients with dementia during the course of the disease. These symptoms are referred to behavioral changes (such as mood disorders, anxiety, sleep problems, apathy, delusions, hallucinations), behavioral problems (like disinhibition, irritability or aggression), aberrant motor behavior and changes in eating behavior. Examples include: “Apatico” (apathetic), “Sintomi depressivi” (depressive symptoms), “Irritabile” (irritable).
  • Drug treatment (162 examples, 2.75% of the dataset), including any substance used to prevent or treat a medical problem, without dosage. Examples include: “Madopar”, “Urorec”.
  • Medical assessment (1854 examples, 31.49% of the dataset), used to obtain an objective measure or information about a medical condition or disease. Examples include: “EEG” (ElectroEncephaloGram), “MMSE” (Mini-Mental State Examination), “RM encefalo” (brain magnetic resonance imaging).

Check the full paper for further details, and feel free to contact us if you have some inquiry!

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Models trained or fine-tuned on IVN-RIN/PsyNIT