Update Mimic4Dataset.py
Browse files- Mimic4Dataset.py +4 -52
Mimic4Dataset.py
CHANGED
@@ -10,7 +10,7 @@ from sklearn.model_selection import train_test_split
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from sklearn.preprocessing import LabelEncoder
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import yaml
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import numpy as np
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from .dataset_utils import vocab, concat_data, generate_deep, generate_ml
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from .task_cohort import create_cohort
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@@ -532,61 +532,13 @@ class Mimic4Dataset(datasets.GeneratorBasedBuilder):
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items= pd.read_csv(self.mimic_path+'/icu/d_items.csv.gz',compression='gzip', header=0)
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with open(filepath, 'rb') as fp:
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dico = pickle.load(fp)
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for key, data in dico.items():
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#Diagnosis
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if self.feat_cond:
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conds = data['Cond']['fids']
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cond_text=[]
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for code in conds:
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desc = icd[icd['code']==code]
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if not desc.empty:
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cond_text.append(desc['description'].to_string(index=False))
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template = 'The patient is diagnosed with {}.'
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cond_text = template.format(';'.join(cond_text))
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else :
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cond_text=''
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#chart
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if self.feat_chart:
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chart = data['Chart']
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if chart:
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charts=chart['val']
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feat=charts.keys()
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chart_val=[charts[key] for key in feat]
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chart_mean = [round(np.mean(c),3) for c in chart_val]
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feat_text = [(items[items['itemid']==f]['label']).to_string(index=False) for f in feat]
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template='{} for {}'
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chart_text = []
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for mean_val, feat_label in zip(chart_mean, feat_text):
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text = template.format(mean_val,feat_label)
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chart_text.append(text)
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chart_text='The chart events mesured are :{}.' + ';'.join(chart_text)
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else:
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chart_text=''
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if self.feat_meds:
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meds = data['Med']
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if meds:
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meds=meds['val']
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feat=meds['signal'].keys()
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meds_val=[meds[key] for key in feat]
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meds_mean = [round(np.mean(c),3) for c in meds_val]
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feat_text = [(items[items['itemid']==f]['label']).to_string(index=False) for f in feat]
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template='{} of {}'
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meds_text = []
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for mean_val, feat_label in zip(meds_mean, feat_text):
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text = template.format(mean_val,feat_label)
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meds_text.append(text)
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meds_text='The medications administered are :{}.' + ';'.join(meds_text)
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else:
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meds_text=''
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yield int(key),{
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'label' : data['label'],
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'text': cond_text+chart_text+meds_text
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}
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#############################################################################################################################
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from sklearn.preprocessing import LabelEncoder
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import yaml
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import numpy as np
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from .dataset_utils import vocab, concat_data, generate_deep, generate_ml, generate_text
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from .task_cohort import create_cohort
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items= pd.read_csv(self.mimic_path+'/icu/d_items.csv.gz',compression='gzip', header=0)
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with open(filepath, 'rb') as fp:
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dico = pickle.load(fp)
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for key, data in dico.items():
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cond_text,chart_text,meds_text,proc_text,out_text = self.generate_text(data,icd,items, self.feat_cond, self.feat_chart, self.feat_meds, self.feat_proc, self.feat_out)
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yield int(key),{
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'label' : data['label'],
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'text': cond_text+chart_text+meds_text+proc_text+out_text
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}
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#############################################################################################################################
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