Wikipedia-Based Automatic Diagnosis Prediction in Clinical Decision Support Systems

When making clinical decisions, physicians often consult biomedical literatures for reference. In this case, an effective clinical decision support system, provided with a patient’s health information, should be able to generate accurate queries and return to the physicians with useful articles. Related works in the Clinical Decision Support (CDS) track of TREC 2015 demonstrated the usefulness of knowing patients’ diagnosis information for supporting more effective retrieval, but the diagnosis information is often missing in most cases. Furthermore, it is still a great challenge to perform large-scale automatic diagnosis prediction. This motivates us to propose an automatic diagnosis prediction method to enhance the retrieval in a clinical decision support system, where the evidence for the prediction is extracted from Wikipedia. Through the evaluation conducted on 2014 CDS tasks, our method reaches the best performance among all submitted runs. In the next step, graph structured evidence will be integrated to make the prediction more accurate

Détecter la non-adhérence médicamenteuse dans les forums de discussion avec les méthodes de recherche d'information

International audienceDetect drug non-compliance in Internet fora using Information Retrieval methods Information retrieval methods permit to explore textual data. We exploit them for the detection of messages with drug non-compliance such as they occur in discussion fora. Drug non-compliance corresponds to cases when patient does not respect indications given by medical doctors and changes drug intake (he can increase or decrease the dosage, for instance). The exploited search engine shows 0.9 precision on the first 10 results with balanced data, and 0.4 precision with unbalanced data. In the last case, the natural distribution is respected and the data contain very few non-compliance messages. Precision dicreases and recall increases when more top answers are considered. We also exploit the search engine with new unseen data and on precise types of drug non-complianceLes méthodes de recherche d'information permettent d'explorer les données textuelles. Nous les exploitons pour la détection de messages avec la non-adhérence médicamenteuse dans les forums de discussion. La non-adhérence médicamenteuse correspond aux cas lorsqu'un patient ne respecte pas les indications de son médecin et modifie les prises de médicaments (augmente ou diminue les doses, par exemple). Le moteur de recherche exploité montre 0,9 de précision sur les 10 premiers résultats avec un corpus équilibré, et 0,4 avec un corpus respectant la distribution naturelle des messages, qui est très déséquilibrée en défaveur de la catégorie recherchée. La précision diminue avec l'augmentation du nombre de résultats considérés alors que le rappel augmente. Nous exploitons également le moteur de recherche sur de nouvelles données et avec des types précis de non-adhérence

Enhancing Clinical Decision Support Systems with Public Knowledge Bases

With vast amount of biomedical literature available online, doctors have the benefits of consulting the literature before making clinical decisions, but they are facing the daunting task of finding needles in haystacks. In this situation, it would help doctors if an effective clinical decision support system could generate accurate queries and return a manageable size of highly useful articles. Existing studies showed the useful-ness of patients’ diagnosis information in such scenario, but diagnosis is often missing in most cases. Furthermore, existing diagnosis prediction systems mainly focus on predicting a small range of diseases with well-formatted features, and it is still a great challenge to perform large-scale automatic diagnosis predictions based on noisy pa-tient medical records. In this paper, we propose automatic diagnosis prediction meth-ods for enhancing the retrieval in a clinical decision support system, where the predic-tion is based on evidences automatically collected from publicly accessible online knowledge bases such as Wikipedia and Semantic MEDLINE Database (SemMedDB). The assumption is that relevant diseases and their corresponding symptoms co-occur more frequently in these knowledge bases. Our methods perfor-mance was evaluated using test collections from the Clinical Decision Support (CDS) track in TREC 2014, 2015 and 2016. The results show that our best method can au-tomatically predict diagnosis with about 65.56% usefulness, and such predictions can significantly improve the biomedical literatures retrieval. Our methods can generate comparable retrieval results to the state-of-art methods, which utilize much more complicated methods and some manually crafted medical knowledge. One possible future work is to apply these methods in collaboration with real doctors

What Makes a Top-Performing Precision Medicine Search Engine? Tracing Main System Features in a Systematic Way

From 2017 to 2019 the Text REtrieval Conference (TREC) held a challenge task on precision medicine using documents from medical publications (PubMed) and clinical trials. Despite lots of performance measurements carried out in these evaluation campaigns, the scientific community is still pretty unsure about the impact individual system features and their weights have on the overall system performance. In order to overcome this explanatory gap, we first determined optimal feature configurations using the Sequential Model-based Algorithm Configuration (SMAC) program and applied its output to a BM25-based search engine. We then ran an ablation study to systematically assess the individual contributions of relevant system features: BM25 parameters, query type and weighting schema, query expansion, stop word filtering, and keyword boosting. For evaluation, we employed the gold standard data from the three TREC-PM installments to evaluate the effectiveness of different features using the commonly shared infNDCG metric.Comment: Accepted for SIGIR2020, 10 page

TrialMatch: A Transformer Architecture to Match Patients to Clinical Trials

Around 80% of clinical trials fail to meet the patient recruitment requirements, which not only hinders the market growth but also delays patients’ access to new and effec- tive treatments. A possible approach is to use Electronic Health Records (EHRs) to help match patients to clinical trials. Past attempts at achieving this exact goal took place, but due to a lack of data, they were unsuccessful. In 2021 Text REtrieval Conference (TREC) introduced the Clinical Trials Track, where participants were challenged with retrieving relevant clinical trials given the patient’s descriptions simulating admission notes. Utilizing the track results as a baseline, we tackled the challenge, for this, we re- sort to Information Retrieval (IR), implementing a pipeline for document ranking where we explore the different retrieval methods, how to filter the clinical trials based on the criteria, and reranking with Transformer based models. To tackle the problem, we ex- plored models pre-trained on the biomedical domain, how to deal with long queries and documents through query expansion and passage selection, and how to distinguish an eligible clinical trial from an excluded clinical trial, using techniques such as Named Entity Recognition (NER) and Clinical Assertion. Our results let to the finding that the current state-of-the-art Bidirectional Encoder Representations from Transformers (BERT) bi-encoders outperform the cross-encoders in the mentioned task, whilst proving that sparse retrieval methods are capable of obtaining competitive outcomes, and to finalize we showed that the use of the demographic information available can be used to improve the final result.Cerca de 80% dos ensaios clínicos não satisfazem os requisitos de recrutamento de paci- entes, o que não só dificulta o crescimento do mercado como também impede o acesso dos pacientes a novos e eficazes tratamentos. Uma abordagem possível é utilizar os Pron- tuários Eletrônicos para ajudar a combinar doentes a ensaios clínicos. Tentativas passadas para alcançar este exato objetivo tiveram lugar, mas devido à falta de dados, não foram bem sucedidos. Em 2021, a TREC introduziu a Clinical Trials Track, onde os participantes foram desafiados com a recuperação ensaios clínicos relevantes, dadas as descrições dos pacientes simulando notas de admissão. Utilizando os resultados da track como base, en- frentámos o desafio, para isso recorremos à Recuperação de Informação, implementando uma pipeline para a classificação de documentos onde exploramos os diferentes métodos de recuperação, como filtrar os ensaios clínicos com base nos critérios, e reclassificação com modelos baseados no Transformer. Para enfrentar o problema, explorámos modelos pré-treinados no domínio biomédico, como lidar com longas descrições e documentos, e como distinguir um ensaio clínico elegível de um ensaio clínico excluído, utilizando técnicas como Reconhecimento de Entidade Mencionada e Asserção Clínica. Os nossos re- sultados permitem concluir que os actuais bi-encoders de última geração BERT superam os cross-encoders BERT na tarefa mencionada, provamos que os métodos de recuperação esparsos são capazes de obter resultados competitivos, e para finalizar mostramos que a utilização da informação demográfica disponível pode ser utilizada para melhorar o resultado fina

Utilizing Knowledge Bases In Information Retrieval For Clinical Decision Support And Precision Medicine

Accurately answering queries that describe a clinical case and aim at finding articles in a collection of medical literature requires utilizing knowledge bases in capturing many explicit and latent aspects of such queries. Proper representation of these aspects needs knowledge-based query understanding methods that identify the most important query concepts as well as knowledge-based query reformulation methods that add new concepts to a query. In the tasks of Clinical Decision Support (CDS) and Precision Medicine (PM), the query and collection documents may have a complex structure with different components, such as disease and genetic variants that should be transformed to enable an effective information retrieval. In this work, we propose methods for representing domain-specific queries based on weighted concepts of different types whether exist in the query itself or extracted from the knowledge bases and top retrieved documents. Besides, we propose an optimization framework, which allows unifying query analysis and expansion by jointly determining the importance weights for the query and expansion concepts depending on their type and source. We also propose a probabilistic model to reformulate the query given genetic information in the query and collection documents. We observe significant improvement of retrieval accuracy will be obtained for our proposed methods over state-of-the-art baselines for the tasks of clinical decision support and precision medicine