updated codebase

Author: Andy-jqa

trialgpt_ranking/rank_results.py

@@ -0,0 +1,120 @@
+__author__ = "qiao"
+
+"""
+Rank the trials given the matching and aggregation results
+"""
+
+import json
+import sys
+
+eps = 1e-9
+
+def get_matching_score(matching):
+	# count only the valid ones
+	included = 0
+	not_inc = 0
+	na_inc = 0
+	no_info_inc = 0
+
+	excluded = 0
+	not_exc = 0
+	na_exc = 0
+	no_info_exc = 0
+	
+	# first count inclusions
+	for criteria, info in matching["inclusion"].items():
+		
+		if len(info) != 3:
+			continue
+
+		if info[2] == "included":
+			included += 1	
+		elif info[2] == "not included":
+			not_inc += 1
+		elif info[2] == "not applicable":
+			na_inc += 1
+		elif info[2] == "not enough information":
+			no_info_inc += 1
+	
+	# then count exclusions
+	for criteria, info in matching["exclusion"].items():
+
+		if len(info) != 3:
+			continue
+
+		if info[2] == "excluded":
+			excluded += 1	
+		elif info[2] == "not excluded":
+			not_exc += 1
+		elif info[2] == "not applicable":
+			na_exc += 1
+		elif info[2] == "not enough information":
+			no_info_exc += 1
+
+	# get the matching score
+	score = 0
+	
+	score += included / (included + not_inc + no_info_inc + eps)
+	
+	if not_inc > 0:
+		score -= 1
+	
+	if excluded > 0:
+		score -= 1
+	
+	return score 
+
+
+def get_agg_score(assessment):
+	try:
+		rel_score = float(assessment["relevance_score_R"])
+		eli_score = float(assessment["eligibility_score_E"])
+	except:
+		rel_score = 0
+		eli_score = 0
+	
+	score = (rel_score + eli_score) / 100
+
+	return score 
+
+
+if __name__ == "__main__":
+	# args are the results paths
+	matching_results_path = sys.argv[1]
+	agg_results_path = sys.argv[2]
+
+	# loading the results
+	matching_results = json.load(open(matching_results_path))
+	agg_results = json.load(open(agg_results_path))
+	
+	# loop over the patients
+	for patient_id, label2trial2results in matching_results.items():
+
+		trial2score = {}
+
+		for _, trial2results in label2trial2results.items():
+			for trial_id, results in trial2results.items():
+
+				matching_score = get_matching_score(results)
+				
+				if patient_id not in agg_results or trial_id not in agg_results[patient_id]:
+					print(f"Patient {patient_id} Trial {trial_id} not in the aggregation results.")
+					agg_score = 0
+				else:
+					agg_score = get_agg_score(agg_results[patient_id][trial_id])
+
+				trial_score = matching_score + agg_score
+				
+				trial2score[trial_id] = trial_score
+
+		sorted_trial2score = sorted(trial2score.items(), key=lambda x: -x[1])
+		
+		print()
+		print(f"Patient ID: {patient_id}")
+		print("Clinical trial ranking:")
+		
+		for trial, score in sorted_trial2score:
+			print(trial, score)
+
+		print("===")
+		print()