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src/helm/benchmark/run_specs/medhelm_run_specs.py

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@@ -523,9 +523,7 @@ def get_mimic_bhc_spec() -> RunSpec:
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scenario_spec = ScenarioSpec(class_name="helm.benchmark.scenarios.mimic_bhc_scenario.MIMICBHCScenario", args={})
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adapter_spec = get_generation_adapter_spec(
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instructions=(
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"Summarize the clinical note into a brief hospital course."
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),
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instructions=("Summarize the clinical note into a brief hospital course."),
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input_noun="Clinical Note",
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output_noun="Brief Hospital Course",
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newline_after_input_noun=True,

src/helm/benchmark/scenarios/mimic_bhc_scenario.py

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@@ -16,9 +16,9 @@
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class MIMICBHCScenario(Scenario):
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"""
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MIMIC-IV-BHC presents a curated collection of preprocessed clinical discharge notes with labeled brief hospital course (BHC) summaries.
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This dataset is derived from MIMIC-IV (https://doi.org/10.1093/jamia/ocae312).
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MIMIC-IV-BHC presents a curated collection of preprocessed clinical discharge notes with labeled brief
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hospital course (BHC) summaries. This dataset is derived from MIMIC-IV (https://doi.org/10.1093/jamia/ocae312).
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In total, the dataset contains 270,033 clinical notes.
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The splits are provided by the dataset itself.
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@@ -47,7 +47,7 @@ class MIMICBHCScenario(Scenario):
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year={2024},
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publisher={Oxford University Press}
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}
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@article{aali2024mimic,
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title={MIMIC-IV-Ext-BHC: Labeled Clinical Notes Dataset for Hospital Course Summarization},
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author={Aali, Asad and Van Veen, Dave and Arefeen, YI and Hom, Jason and Bluethgen, Christian
@@ -81,8 +81,8 @@ def get_instances(self, output_path: str) -> List[Instance]:
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with open(file, "r") as f:
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data = [json.loads(line) for line in f]
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os.remove(file)
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for data_split, split in splits.items():
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for data_split, split in splits.items():
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clinical_notes: List[str] = [x["input"] for x in data]
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bhc_summaries: List[str] = [x["target"] for x in data]
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assert len(clinical_notes) == len(bhc_summaries), "Notes and summaries must have the same length"
@@ -97,4 +97,4 @@ def get_instances(self, output_path: str) -> List[Instance]:
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)
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)
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return instances
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return instances

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