Dataset Information

HINT: Hierarchical interaction network for clinical-trial-outcome predictions.

ABSTRACT: Clinical trials are crucial for drug development but often face uncertain outcomes due to safety, efficacy, or patient-recruitment problems. We propose the Hierarchical Interaction Network (HINT) to predict clinical trial outcomes. First, HINT encodes multi-modal data (drug molecule, target disease, trial eligibility criteria) into embeddings. Then, HINT trains knowledge-embedding modules using drug pharmacokinetic and historical trial data. Finally, a hierarchical interaction graph connects all of the embeddings to capture their interactions and predict trial outcomes. HINT was trained and validated on 1,160 phase I trials, 4,449 phase II trials, and 3,436 phase III trials. It obtained 0.665, 0.620, and 0.847 F1 scores on separate test sets of 627 phase I, 1,653 phase II, and 1,140 phase III trials, respectively. HINT significantly outperforms the best baseline method on most metrics. The benchmark dataset and codes are released at https://github.com/futianfan/clinical-trial-outcome-prediction.

SUBMITTER: Fu T

PROVIDER: S-EPMC9024011 | biostudies-literature | 2022 Apr

REPOSITORIES: biostudies-literature

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HINT: Hierarchical interaction network for clinical-trial-outcome predictions.

Fu Tianfan T Huang Kexin K Xiao Cao C Glass Lucas M LM Sun Jimeng J

Patterns (New York, N.Y.) 20220203 4

Clinical trials are crucial for drug development but often face uncertain outcomes due to safety, efficacy, or patient-recruitment problems. We propose the Hierarchical Interaction Network (HINT) to predict clinical trial outcomes. First, HINT encodes multi-modal data (drug molecule, target disease, trial eligibility criteria) into embeddings. Then, HINT trains knowledge-embedding modules using drug pharmacokinetic and historical trial data. Finally, a hierarchical interaction graph connects all ...[more]

PMID: 35465223

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Project description:ObjectiveCohort selection for clinical trials is a key step for clinical research. We proposed a hierarchical neural network to determine whether a patient satisfied selection criteria or not.Materials and methodsWe designed a hierarchical neural network (denoted as CNN-Highway-LSTM or LSTM-Highway-LSTM) for the track 1 of the national natural language processing (NLP) clinical challenge (n2c2) on cohort selection for clinical trials in 2018. The neural network is composed of 5 components: (1) sentence representation using convolutional neural network (CNN) or long short-term memory (LSTM) network; (2) a highway network to adjust information flow; (3) a self-attention neural network to reweight sentences; (4) document representation using LSTM, which takes sentence representations in chronological order as input; (5) a fully connected neural network to determine whether each criterion is met or not. We compared the proposed method with its variants, including the methods only using the first component to represent documents directly and the fully connected neural network for classification (denoted as CNN-only or LSTM-only) and the methods without using the highway network (denoted as CNN-LSTM or LSTM-LSTM). The performance of all methods was measured by micro-averaged precision, recall, and F1 score.ResultsThe micro-averaged F1 scores of CNN-only, LSTM-only, CNN-LSTM, LSTM-LSTM, CNN-Highway-LSTM, and LSTM-Highway-LSTM were 85.24%, 84.25%, 87.27%, 88.68%, 88.48%, and 90.21%, respectively. The highest micro-averaged F1 score is higher than our submitted 1 of 88.55%, which is 1 of the top-ranked results in the challenge. The results indicate that the proposed method is effective for cohort selection for clinical trials.DiscussionAlthough the proposed method achieved promising results, some mistakes were caused by word ambiguity, negation, number analysis and incomplete dictionary. Moreover, imbalanced data was another challenge that needs to be tackled in the future.ConclusionIn this article, we proposed a hierarchical neural network for cohort selection. Experimental results show that this method is good at selecting cohort.

Project description:BACKGROUND: HinT proteins are found in prokaryotes and eukaryotes and belong to the superfamily of HIT proteins, which are characterized by an histidine-triad sequence motif. While the eukaryotic variants hydrolyze AMP derivates and modulate transcription, the function of prokaryotic HinT proteins is less clearly defined. In Mycoplasma hominis, HinT is concomitantly expressed with the proteins P60 and P80, two domains of a surface exposed membrane complex, and in addition interacts with the P80 moiety. RESULTS: An cluster of hitABL genes, similar to that of M. hominis was found in M. pulmonis, M. mycoides subspecies mycoides SC, M. mobile and Mesoplasma florum. RT-PCR analyses provided evidence that the P80, P60 and HinT homologues of M. pulmonis were polycistronically organized, suggesting a genetic and physical interaction between the proteins encoded by these genes in these species. While the hit loci of M. pneumoniae and M. genitalium encoded, in addition to HinT, a protein with several transmembrane segments, the hit locus of Ureaplasma parvum encoded a pore-forming protein, UU270, a P60 homologue, UU271, HinT, UU272, and a membrane protein of unknown function, UU273. Although a full-length mRNA spanning the four genes was not detected, amplification of all intergenic regions from the center of UU270 to the end of UU273 by RT-PCR may be indicative of a common, but unstable mRNA. In Chlamydiaceae the hit gene is flanked upstream by a gene predicted to encode a metal dependent hydrolase and downstream by a gene putatively encoding a protein with ARM-repeats, which are known to be involved in protein-protein interactions. In RT-PCR analyses of C. pneumoniae, regions comprising only two genes, Cp265/Cp266 and Cp266/Cp267 were able to be amplified. In contrast to this in vivo interaction analysis using the yeast two-hybrid system and in vitro immune co-precipitation revealed an interaction between Cp267, which contains the ARM repeats, Cp265, the predicted hydrolase, and Cp266, the HinT protein. CONCLUSION: In the Mollicutes HinT proteins were shown to be linked with membrane proteins while in the Chlamydiaceae they were genetically and physically associated with cytoplasmic proteins, one of which is predicted to be a metal-dependent phosphoesterase. Future work will elucidate whether these differing associations indicate that HinT proteins have evolved independently or are indeed two hotspots of a common sphere of action of bacterial HinT proteins.

Dataset Information

HINT: Hierarchical interaction network for clinical-trial-outcome predictions.

Publications

HINT: Hierarchical interaction network for clinical-trial-outcome predictions.

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