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1. Towards Fair Patient-Trial Matching via Patient-Criterion Level Fairness Constraint

   Chia-Yuan Chang, Jiayi Yuan (January 2024, AMIA2023)

   Clinical trials are indispensable in developing new treatments, but they face obstacles in patient recruitment and retention, hindering the enrollment of necessary participants. To tackle these challenges, deep learning frameworks have been created to match patients to trials. These frameworks calculate the similarity between patients and clinical trial eligibility criteria, considering the discrepancy between inclusion and exclusion criteria. Recent studies have shown that these frameworks outperform earlier approaches. However, deep learning models may raise fairness issues in patient-trial matching when certain sensitive groups of individuals are underrepresented in clinical trials, leading to incomplete or inaccurate data and potential harm. To tackle the issue of fairness, this work proposes a fair patient-trial matching framework by generating a patient-criterion level fairness constraint. The proposed framework considers the inconsistency between the embedding of inclusion and exclusion criteria among patients of different sensitive groups. The experimental results on real-world patient-trial and patient-criterion matching tasks demonstrate that the proposed framework can successfully alleviate the predictions that tend to be biased. 

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2. Learning to Rank Entities for Set Expansion from Unstructured Data

   https://doi.org/10.1145/3409256.3409811  

   Yu, Puxuan; Rahimi, Razieh; Huang, Zhiqi; Allan, James (September 2020, Proceedings of the ACM SIGIR International Conference on the Theory of Information Retrieval (ICTIR 2020))

   null (Ed.)

   Entity set expansion (ESE) refers to mining ``siblings'' of some user-provided seed entities from unstructured data. It has drawn increasing attention in the IR and NLP communities for its various applications. To the best of our knowledge, there has not been any work towards a supervised neural model for entity set expansion from unstructured data. We suspect that the main reason is the lack of massive annotated entity sets. In order to solve this problem, we propose and implement a toolkit called {DBpedia-Sets}, which automatically extracts entity sets from any plain text collection and can provide a large number of distant supervision data for neural model training. We propose a two-channel neural re-ranking model {NESE} that jointly learns exact and semantic matching of entity contexts. The former accepts entity-context co-occurrence information and the latter learns a non-linear transformer from generally pre-trained embeddings to ESE-task specific embeddings for entities. Experiments on real datasets of different scales from different domains show that {NESE} outperforms state-of-the-art approaches in terms of precision and MAP, where the improvements are statistically significant and are higher when the given corpus is larger. 

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3. ConfliBERT-Spanish: A Pre-trained Spanish Language Model for Political Conflict and Violence

   https://doi.org/10.1109/CiSt56084.2023.10409883  

   Yang, Wooseong; Alsarra, Sultan; Abdeljaber, Luay; Zawad, Niamat; Delaram, Zeinab; Osorio, Javier; Khan, Latifur; Brandt, Patrick T; D’Orazio, Vito (December 2023, Vakka)

   This article introduces ConfliBERT-Spanish, a pre-trained language model specialized in political conflict and violence for text written in the Spanish language. Our methodology relies on a large corpus specialized in politics and violence to extend the capacity of pre-trained models capable of processing text in Spanish. We assess the performance of ConfliBERT-Spanish in comparison to Multilingual BERT and BETO baselines for binary classification, multi-label classification, and named entity recognition. Results show that ConfliBERT-Spanish consistently outperforms baseline models across all tasks. These results show that our domain-specific language-specific cyberinfrastructure can greatly enhance the performance of NLP models for Latin American conflict analysis. This methodological advancement opens vast opportunities to help researchers and practitioners in the security sector to effectively analyze large amounts of information with high degrees of accuracy, thus better equipping them to meet the dynamic and complex security challenges affecting the region. 

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4. LATTE: Latent Type Modeling for Biomedical Entity Linking

   Zhu, Ming; Celikkaya, Busra; Bhatia, Parminder; Reddy, Chandan K. (January 2020, AAAI Conference on Artificial Intelligence (AAAI))

   Entity linking is the task of linking mentions of named entities in natural language text, to entities in a curated knowledge-base. This is of significant importance in the biomedical domain, where it could be used to semantically annotate a large volume of clinical records and biomedical literature, to standardized concepts described in an ontology such as Unified Medical Language System (UMLS). We observe that with precise type information, entity disambiguation becomes a straightforward task. However, fine-grained type information is usually not available in biomedical domain. Thus, we propose LATTE, a LATent Type Entity Linking model, that improves entity linking by modeling the latent fine-grained type information about mentions and entities. Unlike previous methods that perform entity linking directly between the mentions and the entities, LATTE jointly does entity disambiguation, and latent fine-grained type learning, without direct supervision. We evaluate our model on two biomedical datasets: MedMentions, a large scale public dataset annotated with UMLS concepts, and a de-identified corpus of dictated doctor’s notes that has been annotated with ICD concepts. Extensive experimental evaluation shows our model achieves significant performance improvements over several state-of-the-art techniques. 

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5. Predictive modeling of clinical trial terminations using feature engineering and embedding learning

   https://doi.org/10.1038/s41598-021-82840-x  

   Elkin, Magdalyn E.; Zhu, Xingquan (December 2021, Scientific Reports)

   null (Ed.)

   Abstract In this study, we propose to use machine learning to understand terminated clinical trials. Our goal is to answer two fundamental questions: (1) what are common factors/markers associated to terminated clinical trials? and (2) how to accurately predict whether a clinical trial may be terminated or not? The answer to the first question provides effective ways to understand characteristics of terminated trials for stakeholders to better plan their trials; and the answer to the second question can direct estimate the chance of success of a clinical trial in order to minimize costs. By using 311,260 trials to build a testbed with 68,999 samples, we use feature engineering to create 640 features, reflecting clinical trial administration, eligibility, study information, criteria etc. Using feature ranking, a handful of features, such as trial eligibility, trial inclusion/exclusion criteria, sponsor types etc. , are found to be related to the clinical trial termination. By using sampling and ensemble learning, we achieve over 67% Balanced Accuracy and over 0.73 AUC (Area Under the Curve) scores to correctly predict clinical trial termination, indicating that machine learning can help achieve satisfactory prediction results for clinical trial study. 

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