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License: Apache License 2.0
LaRA: Large-language-model based semantic similarity for RAre disease
License: Apache License 2.0
args = parse_args()
disease_data = load_pickle(os.path.join(DATA_DIR, "diseases.pickle"))
patient_data = load_pickle(os.path.join(DATA_DIR, "patients.pickle"))
disease_data, patient_data = cleanse_data(disease_data, patient_data)
all_symptom_vectors = torch.tensor(
disease_data.all_symptom_vectors, dtype=torch.float32
)
train_val_patients_list, test_patients_list = train_test_split(
patient_data.data, random_state=args.random_state # SSL인데 환자데이터가 갑자기 왠?
)
train_patients_list, val_patients_list = train_test_split(
train_val_patients_list, random_state=args.random_state
)
train_patients = Patients(train_patients_list)
val_patients = Patients(val_patients_list)
test_patients = Patients(test_patients_list)
mlf_logger = MLFlowLogger(
experiment_name=EXP_SYMPTOM,
run_name=args.run_name,
tracking_uri=TRACKING_URI,
)
train_dataset = StochasticPairwiseDataset(
train_patients,
disease_data,
augmentation_factor=args.augmentation_factor,
max_len=args.max_len,
initial_fraction=args.initial_fraction,
fraction_decay_rate=args.fraction_decay_rate,
augmentator=[
SampleSymptoms(),
AddNoiseSymptoms(all_symptom_vectors),
TruncateOrPad(args.max_len, stochastic=True, weighted_sampling=False),
],
).train()
val_dataset = StochasticPairwiseDataset(
val_patients,
disease_data,
max_len=args.max_len,
).validate()
LLM enhanced semantic similarity 논문의 우수성을 증명하기 위한 근거는 아래와 같습니다.
: Figure1. LaRA. Method overview(https://github.com/4pygmalion/LaRa/blob/main/data/images/LaRa.png) @4pygmalion
: Table1. Demographic characteristics (In-house dataset의 환자의 내용) @wjeong53
| Variable | Eligible participant (N=***) |
|--------------------------|------------------------------|
| Gender (n, (%)) | |
| Male | |
| Female | |
| N phenotypes (mean [SD]) | |
| Phenotypes | |
| Nervous system | |
| Musculoskeletal system | |
| Head or neck | |
| Eye | |
| Cardiovascular system | |
| Others | |
|--------------------------|------------------------------|
=> 의미: 본문내에 큰 의미는 없고, 우리의 환자데이터셋이 엄청 특이케이스로 뽑힌 환자들이 아니며 일반적인 rare disease patients로 분석했다라는 것의 근거만 마련하면 될 것 같습니다. 보통 의학 논문에서 Result1으로 환자의 인구학적 통계들을 많이 제시하는데, 저희도 ML문제이지만, 그 분석대상은 희귀질환환자(의료계)니까, 넣어도 괜찮을 것 같습니다. 이건 의견잇으면 알려주세요.
=> 기타: 3ASC에서도 비슷하게 논문에 테이블 넣었어요. 아래의 manuscript에 table1을 참고해보셔도 좋을 듯합니다.
: Figure2. Disease prioritization performance in In-house dataset @wjeong53
: Figure 3: Disease prioritization performance in publicitly available dataset @wjeong53
: Figure 4: Individual case review (posthoc interpretation) @wjeong53
[1] 3ASC_v2.docx
[2] Pheno2disease: Bioinformatics에서 SOTA라고 주장하는 방법론의 논문입니다. https://academic.oup.com/bib/article/24/4/bbad172/7185480?login=false
[3] (https://zenodo.org/records/3905420)
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