Update dataset_utils.py
Browse files- dataset_utils.py +78 -63
dataset_utils.py
CHANGED
@@ -77,7 +77,39 @@ def vocab(task,diag_flag,proc_flag,out_flag,chart_flag,med_flag,lab_flag):
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return len(condVocabDict),len(procVocabDict),len(medVocabDict),len(outVocabDict),len(chartVocabDict),len(labVocabDict),ethVocabDict,genderVocabDict,ageVocabDict,insVocabDict
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-
def
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meds=data['Med']
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proc = data['Proc']
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out = data['Out']
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@@ -97,10 +129,7 @@ def concat_data(data,task,feat_cond,feat_proc,feat_out,feat_chart,feat_meds,feat
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##########COND#########
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if (feat_cond):
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-
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with open("./data/dict/"+task+"/condVocab", 'rb') as fp:
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conDict = pickle.load(fp)
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conds=pd.DataFrame(conDict,columns=['COND'])
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features=pd.DataFrame(np.zeros([1,len(conds)]),columns=conds['COND'])
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#onehot encode
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@@ -122,13 +151,10 @@ def concat_data(data,task,feat_cond,feat_proc,feat_out,feat_chart,feat_meds,feat
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##########PROC#########
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if (feat_proc):
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with open("./data/dict/"+task+"/procVocab", 'rb') as fp:
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procDic = pickle.load(fp)
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-
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if proc :
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feat=proc.keys()
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proc_val=[proc[key] for key in feat]
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procedures=pd.DataFrame(
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features=pd.DataFrame(np.zeros([1,len(procedures)]),columns=procedures['PROC'])
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procs=pd.DataFrame(columns=feat)
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for p,v in zip(feat,proc_val):
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@@ -137,20 +163,17 @@ def concat_data(data,task,feat_cond,feat_proc,feat_out,feat_chart,feat_meds,feat
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proc_df = pd.concat([features,procs],axis=1).fillna(0)
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proc_df.columns=pd.MultiIndex.from_product([["PROC"], proc_df.columns])
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else:
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procedures=pd.DataFrame(
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features=pd.DataFrame(np.zeros([1,len(procedures)]),columns=procedures['PROC'])
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features.columns=pd.MultiIndex.from_product([["PROC"], features.columns])
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proc_df=features.fillna(0)
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##########OUT#########
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if (feat_out):
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with open("./data/dict/"+task+"/outVocab", 'rb') as fp:
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outDic = pickle.load(fp)
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-
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if out :
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feat=out.keys()
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out_val=[out[key] for key in feat]
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outputs=pd.DataFrame(
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features=pd.DataFrame(np.zeros([1,len(outputs)]),columns=outputs['OUT'])
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outs=pd.DataFrame(columns=feat)
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for o,v in zip(feat,out_val):
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@@ -159,21 +182,18 @@ def concat_data(data,task,feat_cond,feat_proc,feat_out,feat_chart,feat_meds,feat
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out_df = pd.concat([features,outs],axis=1).fillna(0)
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out_df.columns=pd.MultiIndex.from_product([["OUT"], out_df.columns])
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else:
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outputs=pd.DataFrame(
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features=pd.DataFrame(np.zeros([1,len(outputs)]),columns=outputs['OUT'])
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features.columns=pd.MultiIndex.from_product([["OUT"], features.columns])
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out_df=features.fillna(0)
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##########CHART#########
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if (feat_chart):
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with open("./data/dict/"+task+"/chartVocab", 'rb') as fp:
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chartDic = pickle.load(fp)
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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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charts=pd.DataFrame(
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features=pd.DataFrame(np.zeros([1,len(charts)]),columns=charts['CHART'])
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chart=pd.DataFrame(columns=feat)
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for c,v in zip(feat,chart_val):
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@@ -182,20 +202,18 @@ def concat_data(data,task,feat_cond,feat_proc,feat_out,feat_chart,feat_meds,feat
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chart_df = pd.concat([features,chart],axis=1).fillna(0)
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chart_df.columns=pd.MultiIndex.from_product([["CHART"], chart_df.columns])
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else:
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charts=pd.DataFrame(
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features=pd.DataFrame(np.zeros([1,len(charts)]),columns=charts['CHART'])
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features.columns=pd.MultiIndex.from_product([["CHART"], features.columns])
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chart_df=features.fillna(0)
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##########LAB#########
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if (feat_lab):
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with open("./data/dict/"+task+"/labsVocab", 'rb') as fp:
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chartDic = pickle.load(fp)
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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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charts=pd.DataFrame(
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features=pd.DataFrame(np.zeros([1,len(charts)]),columns=charts['LAB'])
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chart=pd.DataFrame(columns=feat)
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for c,v in zip(feat,chart_val):
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@@ -205,20 +223,17 @@ def concat_data(data,task,feat_cond,feat_proc,feat_out,feat_chart,feat_meds,feat
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chart_df = pd.concat([features,chart],axis=1).fillna(0)
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chart_df.columns=pd.MultiIndex.from_product([["LAB"], chart_df.columns])
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else:
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charts=pd.DataFrame(
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features=pd.DataFrame(np.zeros([1,len(charts)]),columns=charts['LAB'])
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features.columns=pd.MultiIndex.from_product([["LAB"], features.columns])
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chart_df=features.fillna(0)
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###MEDS
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if (feat_meds):
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with open("./data/dict/"+task+"/medVocab", 'rb') as fp:
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medDic = pickle.load(fp)
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if meds:
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feat=meds['signal'].keys()
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med_val=[meds['amount'][key] for key in feat]
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meds=pd.DataFrame(
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features=pd.DataFrame(np.zeros([1,len(meds)]),columns=meds['MEDS'])
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med=pd.DataFrame(columns=feat)
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for m,v in zip(feat,med_val):
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@@ -227,7 +242,7 @@ def concat_data(data,task,feat_cond,feat_proc,feat_out,feat_chart,feat_meds,feat
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meds_df = pd.concat([features,med],axis=1).fillna(0)
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meds_df.columns=pd.MultiIndex.from_product([["MEDS"], meds_df.columns])
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else:
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meds=pd.DataFrame(
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features=pd.DataFrame(np.zeros([1,len(meds)]),columns=meds['MEDS'])
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features.columns=pd.MultiIndex.from_product([["MEDS"], features.columns])
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meds_df=features.fillna(0)
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@@ -237,7 +252,7 @@ def concat_data(data,task,feat_cond,feat_proc,feat_out,feat_chart,feat_meds,feat
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def generate_deep(data,task,feat_cond,feat_proc,feat_out,feat_chart,feat_meds,feat_lab):
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meds = []
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charts = []
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proc = []
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@@ -247,25 +262,20 @@ def generate_deep(data,task,feat_cond,feat_proc,feat_out,feat_chart,feat_meds,fe
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demo = []
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size_cond, size_proc, size_meds, size_out, size_chart, size_lab, eth_vocab,gender_vocab,age_vocab,ins_vocab=vocab(task.replace(" ","_"),feat_cond,feat_proc,feat_out,feat_chart,feat_meds,False)
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dyn,cond_df,demo=concat_data(data,task.replace(" ","_"),feat_cond,feat_proc,feat_out,feat_chart,feat_meds,feat_lab)
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if feat_chart:
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charts = dyn['CHART'].values
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-
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if feat_meds:
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meds = dyn['MEDS'].values
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if feat_proc:
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proc = dyn['PROC'].values
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if feat_out:
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out = dyn['OUT'].values
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if feat_lab:
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lab = dyn['LAB'].values
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if feat_cond:
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stat=cond_df.values[0]
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y = int(demo['label'])
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demo["gender"].replace(gender_vocab, inplace=True)
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@@ -274,34 +284,40 @@ def generate_deep(data,task,feat_cond,feat_proc,feat_out,feat_chart,feat_meds,fe
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demo["Age"].replace(age_vocab, inplace=True)
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demo=demo[["gender","ethnicity","insurance","Age"]]
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demo = demo.values[0]
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return stat, demo, meds, charts, out, proc, lab, y
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if concat:
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dyna
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dyna.columns
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dyna
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else:
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dyn_df
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for key in dyn.columns.levels[0]:
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dyn_temp
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if key
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agg
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else:
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agg
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dyn_df = pd.concat([dyn_df, agg], axis=0)
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dyn_df.columns = dyn_df.iloc[0]
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def generate_text(data,icd,items,feat_cond,feat_chart,feat_meds, feat_proc, feat_out):
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@@ -324,7 +340,7 @@ def generate_text(data,icd,items,feat_cond,feat_chart,feat_meds, feat_proc, 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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chart_text = '; '.join(f"{mean_val} for {feat_label}" for mean_val, feat_label in zip(chart_mean, feat_text))
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chart_text = f"The chart events measured were: {chart_text}.
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else:
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chart_text = ''
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else:
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@@ -340,7 +356,7 @@ def generate_text(data,icd,items,feat_cond,feat_chart,feat_meds, feat_proc, 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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meds_text = '; '.join(f"{mean_val} of {feat_label}" for mean_val, feat_label in zip(meds_mean, feat_text))
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meds_text = f"The mean amounts of medications administered during the episode were: {meds_text}.
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else:
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meds_text = ''
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else:
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@@ -352,8 +368,8 @@ def generate_text(data,icd,items,feat_cond,feat_chart,feat_meds, feat_proc, feat
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if proc:
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feat=proc.keys()
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feat_text = [(items[items['itemid']==f]['label']).to_string(index=False) for f in feat]
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template = 'The procedures performed were: {}.
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proc_text= template.format(';
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else:
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proc_text=''
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else:
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out_text=''
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return cond_text,chart_text,meds_text,proc_text,out_text
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-
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return len(condVocabDict),len(procVocabDict),len(medVocabDict),len(outVocabDict),len(chartVocabDict),len(labVocabDict),ethVocabDict,genderVocabDict,ageVocabDict,insVocabDict
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+
def open_dict(task,cond, proc, out, chart, lab, med):
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if cond:
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with open("./data/dict/"+task+"/condVocab", 'rb') as fp:
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condDict = pickle.load(fp)
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else:
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condDict = None
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if proc:
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with open("./data/dict/"+task+"/procVocab", 'rb') as fp:
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procDict = pickle.load(fp)
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else:
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procDict = None
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if out:
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with open("./data/dict/"+task+"/outVocab", 'rb') as fp:
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outDict = pickle.load(fp)
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else:
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outDict = None
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if chart:
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with open("./data/dict/"+task+"/chartVocab", 'rb') as fp:
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chartDict = pickle.load(fp)
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elif lab:
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with open("./data/dict/"+task+"/labsVocab", 'rb') as fp:
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chartDict = pickle.load(fp)
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else:
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chartDict = None
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if med:
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with open("./data/dict/"+task+"/medVocab", 'rb') as fp:
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medDict = pickle.load(fp)
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else:
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medDict = None
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return condDict, procDict, outDict, chartDict, medDict
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def concat_data(data,task,feat_cond,feat_proc,feat_out,feat_chart,feat_meds,feat_lab,condDict, procDict, outDict, chartDict, medDict):
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meds=data['Med']
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proc = data['Proc']
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out = data['Out']
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##########COND#########
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if (feat_cond):
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conds=pd.DataFrame(condDict,columns=['COND'])
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features=pd.DataFrame(np.zeros([1,len(conds)]),columns=conds['COND'])
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#onehot encode
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##########PROC#########
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if (feat_proc):
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if proc :
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feat=proc.keys()
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proc_val=[proc[key] for key in feat]
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procedures=pd.DataFrame(procDict,columns=['PROC'])
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features=pd.DataFrame(np.zeros([1,len(procedures)]),columns=procedures['PROC'])
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procs=pd.DataFrame(columns=feat)
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for p,v in zip(feat,proc_val):
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proc_df = pd.concat([features,procs],axis=1).fillna(0)
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proc_df.columns=pd.MultiIndex.from_product([["PROC"], proc_df.columns])
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else:
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procedures=pd.DataFrame(procDict,columns=['PROC'])
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features=pd.DataFrame(np.zeros([1,len(procedures)]),columns=procedures['PROC'])
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features.columns=pd.MultiIndex.from_product([["PROC"], features.columns])
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proc_df=features.fillna(0)
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##########OUT#########
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if (feat_out):
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if out :
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feat=out.keys()
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out_val=[out[key] for key in feat]
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outputs=pd.DataFrame(outDict,columns=['OUT'])
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features=pd.DataFrame(np.zeros([1,len(outputs)]),columns=outputs['OUT'])
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outs=pd.DataFrame(columns=feat)
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for o,v in zip(feat,out_val):
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out_df = pd.concat([features,outs],axis=1).fillna(0)
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out_df.columns=pd.MultiIndex.from_product([["OUT"], out_df.columns])
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else:
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outputs=pd.DataFrame(outDict,columns=['OUT'])
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features=pd.DataFrame(np.zeros([1,len(outputs)]),columns=outputs['OUT'])
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features.columns=pd.MultiIndex.from_product([["OUT"], features.columns])
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out_df=features.fillna(0)
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##########CHART#########
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if (feat_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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charts=pd.DataFrame(chartDict,columns=['CHART'])
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features=pd.DataFrame(np.zeros([1,len(charts)]),columns=charts['CHART'])
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chart=pd.DataFrame(columns=feat)
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for c,v in zip(feat,chart_val):
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chart_df = pd.concat([features,chart],axis=1).fillna(0)
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chart_df.columns=pd.MultiIndex.from_product([["CHART"], chart_df.columns])
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else:
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charts=pd.DataFrame(chartDict,columns=['CHART'])
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features=pd.DataFrame(np.zeros([1,len(charts)]),columns=charts['CHART'])
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features.columns=pd.MultiIndex.from_product([["CHART"], features.columns])
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chart_df=features.fillna(0)
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##########LAB#########
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+
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if (feat_lab):
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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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charts=pd.DataFrame(chartDict,columns=['LAB'])
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features=pd.DataFrame(np.zeros([1,len(charts)]),columns=charts['LAB'])
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chart=pd.DataFrame(columns=feat)
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for c,v in zip(feat,chart_val):
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chart_df = pd.concat([features,chart],axis=1).fillna(0)
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chart_df.columns=pd.MultiIndex.from_product([["LAB"], chart_df.columns])
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else:
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charts=pd.DataFrame(chartDict,columns=['LAB'])
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features=pd.DataFrame(np.zeros([1,len(charts)]),columns=charts['LAB'])
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features.columns=pd.MultiIndex.from_product([["LAB"], features.columns])
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chart_df=features.fillna(0)
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###MEDS
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if (feat_meds):
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if meds:
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feat=meds['signal'].keys()
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med_val=[meds['amount'][key] for key in feat]
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meds=pd.DataFrame(medDict,columns=['MEDS'])
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features=pd.DataFrame(np.zeros([1,len(meds)]),columns=meds['MEDS'])
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med=pd.DataFrame(columns=feat)
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for m,v in zip(feat,med_val):
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meds_df = pd.concat([features,med],axis=1).fillna(0)
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meds_df.columns=pd.MultiIndex.from_product([["MEDS"], meds_df.columns])
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else:
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245 |
+
meds=pd.DataFrame(medDict,columns=['MEDS'])
|
246 |
features=pd.DataFrame(np.zeros([1,len(meds)]),columns=meds['MEDS'])
|
247 |
features.columns=pd.MultiIndex.from_product([["MEDS"], features.columns])
|
248 |
meds_df=features.fillna(0)
|
|
|
252 |
|
253 |
|
254 |
|
255 |
+
def generate_deep(data,task,feat_cond,feat_proc,feat_out,feat_chart,feat_meds,feat_lab,condDict, procDict, outDict, chartDict, medDict):
|
256 |
meds = []
|
257 |
charts = []
|
258 |
proc = []
|
|
|
262 |
demo = []
|
263 |
|
264 |
size_cond, size_proc, size_meds, size_out, size_chart, size_lab, eth_vocab,gender_vocab,age_vocab,ins_vocab=vocab(task.replace(" ","_"),feat_cond,feat_proc,feat_out,feat_chart,feat_meds,False)
|
265 |
+
dyn,cond_df,demo=concat_data(data,task.replace(" ","_"),feat_cond,feat_proc,feat_out,feat_chart,feat_meds,feat_lab,condDict, procDict, outDict, chartDict, medDict)
|
266 |
if feat_chart:
|
267 |
charts = dyn['CHART'].values
|
|
|
268 |
if feat_meds:
|
269 |
meds = dyn['MEDS'].values
|
|
|
270 |
if feat_proc:
|
271 |
proc = dyn['PROC'].values
|
272 |
+
print(proc)
|
273 |
if feat_out:
|
274 |
out = dyn['OUT'].values
|
|
|
275 |
if feat_lab:
|
276 |
lab = dyn['LAB'].values
|
|
|
277 |
if feat_cond:
|
278 |
stat=cond_df.values[0]
|
|
|
279 |
y = int(demo['label'])
|
280 |
|
281 |
demo["gender"].replace(gender_vocab, inplace=True)
|
|
|
284 |
demo["Age"].replace(age_vocab, inplace=True)
|
285 |
demo=demo[["gender","ethnicity","insurance","Age"]]
|
286 |
demo = demo.values[0]
|
|
|
287 |
return stat, demo, meds, charts, out, proc, lab, y
|
288 |
|
289 |
|
290 |
+
def generate_ml(dyn,stat,demo,concat_cols,concat):
|
291 |
+
X_df=pd.DataFrame()
|
292 |
if concat:
|
293 |
+
dyna=dyn.copy()
|
294 |
+
dyna.columns=dyna.columns.droplevel(0)
|
295 |
+
dyna=dyna.to_numpy()
|
296 |
+
dyna=np.nan_to_num(dyna, copy=False)
|
297 |
+
dyna=dyna.reshape(1,-1)
|
298 |
+
dyn_df=pd.DataFrame(data=dyna,columns=concat_cols)
|
299 |
else:
|
300 |
+
dyn_df=pd.DataFrame()
|
301 |
+
for key in dyn.columns.levels[0]:
|
302 |
+
dyn_temp=dyn[key]
|
303 |
+
if ((key=="CHART") or (key=="MEDS")):
|
304 |
+
agg=dyn_temp.aggregate("mean")
|
305 |
+
agg=agg.reset_index()
|
306 |
else:
|
307 |
+
agg=dyn_temp.aggregate("max")
|
308 |
+
agg=agg.reset_index()
|
|
|
309 |
|
310 |
+
if dyn_df.empty:
|
311 |
+
dyn_df=agg
|
312 |
+
else:
|
313 |
+
dyn_df=pd.concat([dyn_df,agg],axis=0)
|
314 |
+
dyn_df=dyn_df.T
|
315 |
dyn_df.columns = dyn_df.iloc[0]
|
316 |
+
dyn_df=dyn_df.iloc[1:,:]
|
317 |
+
|
318 |
+
X_df=pd.concat([dyn_df,stat],axis=1)
|
319 |
+
X_df=pd.concat([X_df,demo],axis=1)
|
320 |
+
return X_df
|
321 |
|
322 |
|
323 |
def generate_text(data,icd,items,feat_cond,feat_chart,feat_meds, feat_proc, feat_out):
|
|
|
340 |
chart_mean = [round(np.mean(c), 3) for c in chart_val]
|
341 |
feat_text = [(items[items['itemid'] == f]['label']).to_string(index=False) for f in feat]
|
342 |
chart_text = '; '.join(f"{mean_val} for {feat_label}" for mean_val, feat_label in zip(chart_mean, feat_text))
|
343 |
+
chart_text = f"The chart events measured were: {chart_text}."
|
344 |
else:
|
345 |
chart_text = ''
|
346 |
else:
|
|
|
356 |
meds_mean = [round(np.mean(c), 3) for c in meds_val]
|
357 |
feat_text = [(items[items['itemid'] == f]['label']).to_string(index=False) for f in feat]
|
358 |
meds_text = '; '.join(f"{mean_val} of {feat_label}" for mean_val, feat_label in zip(meds_mean, feat_text))
|
359 |
+
meds_text = f"The mean amounts of medications administered during the episode were: {meds_text}."
|
360 |
else:
|
361 |
meds_text = ''
|
362 |
else:
|
|
|
368 |
if proc:
|
369 |
feat=proc.keys()
|
370 |
feat_text = [(items[items['itemid']==f]['label']).to_string(index=False) for f in feat]
|
371 |
+
template = 'The procedures performed were: {}.'
|
372 |
+
proc_text= template.format(';'.join(feat_text))
|
373 |
else:
|
374 |
proc_text=''
|
375 |
else:
|
|
|
389 |
out_text=''
|
390 |
|
391 |
return cond_text,chart_text,meds_text,proc_text,out_text
|
|