Biomedical information extraction for matching patients to clinical trials

Digital Medical information had an astonishing growth on the last decades, driven by an unprecedented number of medical writers, which lead to a complete revolution in what and how much information is available to the health professionals. The problem with this wave of information is that performing a precise selection of the information retrieved by medical information repositories is very exhaustive and time consuming for physicians. This is one of the biggest challenges for physicians with the new digital era: how to reduce the time spent finding the perfect matching document for a patient (e.g. intervention articles, clinical trial, prescriptions). Precision Medicine (PM) 2017 is the track by the Text REtrieval Conference (TREC), that is focused on this type of challenges exclusively for oncology. Using a dataset with a large amount of clinical trials, this track is a good real life example on how information retrieval solutions can be used to solve this types of problems. This track can be a very good starting point for applying information extraction and retrieval methods, in a very complex domain. The purpose of this thesis is to improve a system designed by the NovaSearch team for TREC PM 2017 Clinical Trials task, which got ranked on the top-5 systems of 2017. The NovaSearch team also participated on the 2018 track and got a 15% increase on precision compared to the 2017 one. It was used multiple IR techniques for information extraction and processing of data, including rank fusion, query expansion (e.g. Pseudo relevance feedback, Mesh terms expansion) and experiments with Learning to Rank (LETOR) algorithms. Our goal is to retrieve the best possible set of trials for a given patient, using precise documents filters to exclude the unwanted clinical trials. This work can open doors in what can be done for searching and perceiving the criteria to exclude or include the trials, helping physicians even on the more complex and difficult information retrieval tasks

Towards an Architecture for Efficient Distributed Search of Multimodal Information

The creation of very large-scale multimedia search engines, with more than one billion images and videos, is a pressing need of digital societies where data is generated by multiple connected devices. Distributing search indexes in cloud environments is the inevitable solution to deal with the increasing scale of image and video collections. The distribution of such indexes in this setting raises multiple challenges such as the even partitioning of data space, load balancing across index nodes and the fusion of the results computed over multiple nodes. The main question behind this thesis is how to reduce and distribute the multimedia retrieval computational complexity? This thesis studies the extension of sparse hash inverted indexing to distributed settings. The main goal is to ensure that indexes are uniformly distributed across computing nodes while keeping similar documents on the same nodes. Load balancing is performed at both node and index level, to guarantee that the retrieval process is not delayed by nodes that have to inspect larger subsets of the index. Multimodal search requires the combination of the search results from individual modalities and document features. This thesis studies rank fusion techniques focused on reducing complexity by automatically selecting only the features that improve retrieval effectiveness. The achievements of this thesis span both distributed indexing and rank fusion research. Experiments across multiple datasets show that sparse hashes can be used to distribute documents and queries across index entries in a balanced and redundant manner across nodes. Rank fusion results show that is possible to reduce retrieval complexity and improve efficiency by searching only a subset of the feature indexes

Temporal Information Models for Real-Time Microblog Search

Real-time search in Twitter and other social media services is often biased towards the most recent results due to the “in the moment” nature of topic trends and their ephemeral relevance to users and media in general. However, “in the moment”, it is often difficult to look at all emerging topics and single-out the important ones from the rest of the social media chatter. This thesis proposes to leverage on external sources to estimate the duration and burstiness of live Twitter topics. It extends preliminary research where itwas shown that temporal re-ranking using external sources could indeed improve the accuracy of results. To further explore this topic we pursued three significant novel approaches: (1) multi-source information analysis that explores behavioral dynamics of users, such as Wikipedia live edits and page view streams, to detect topic trends and estimate the topic interest over time; (2) efficient methods for federated query expansion towards the improvement of query meaning; and (3) exploiting multiple sources towards the detection of temporal query intent. It differs from past approaches in the sense that it will work over real-time queries, leveraging on live user-generated content. This approach contrasts with previous methods that require an offline preprocessing step

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

Automatically selecting patients for clinical trials with justifications

Clinical trials are human research studies that are used to evaluate the effectiveness of a surgical, medical, or behavioral intervention. They have been widely used by researchers to determine whether a new treatment, such as a new medication, is safe and effective in humans. A clinical trial is frequently performed to determine whether a new treatment is more successful than the current treatment or has less harmful side effects. However, clinical trials have a high failure rate. One method applied is to find patients based on patient records. Unfortunately, this is a difficult process. This is because this process is typically performed manually, making it time-consuming and error-prone. Consequently, clinical trial deadlines are often missed, and studies do not move forward. Time can be a determining factor for success. Therefore, it would be advantageous to have automatic support in this process. Since it is also important to be able to validate whether the patients were selected correctly for the trial, avoiding eventual health problems, it would be important to have a mechanism to present justifications for the selected patients. In this dissertation, we present one possible solution to solve the problem of patient selection for clinical trials. We developed the necessary algorithms and created a simple and intuitive web application that features the selection of patients for clinical trials automatically. This was achieved by combining knowledge expressed in different formalisms. We integrated medical knowledge using ontologies, with criteria that were expressed using nonmonotonic rules. To address the validation procedure automatically, we developed a mechanism that generates the justifications for each selection together with the results of the patients who were selected. In the end, it is expected that a user can easily enter a set of trial criteria, and the application will generate the results of the selected patients and their respective justifications, based on the criteria inserted, medical information and a database of patient information.Os ensaios clínicos são estudos de pesquisa em humanos, utilizados para avaliar a eficácia de uma intervenção cirúrgica, médica ou comportamental. Estes estudos, têm sido amplamente utilizados pelos investigadores para determinar se um novo tratamento, como é o caso de um novo medicamento, é seguro e eficaz em humanos. Um ensaio clínico é realizado frequentemente, para determinar se um novo tratamento tem mais sucesso do que o tratamento atual ou se tem menos efeitos colaterais prejudiciais. No entanto, os ensaios clínicos têm uma taxa de insucesso alta. Um método aplicado é encontrar pacientes com base em registos. Infelizmente, este é um processo difícil. Isto deve-se ao facto deste processo ser normalmente realizado à mão, o que o torna demorado e propenso a erros. Consequentemente, o prazo dos ensaios clínicos é muitas vezes ultrapassado e os estudos acabam por não avançar. O tempo pode ser por vezes um fator determinante para o sucesso. Seria então vantajoso ter algum apoio automático neste processo. Visto que também seria importante validar se os pacientes foram selecionados corretamente para o ensaio, evitando até eventuais problemas de saúde, seria importante ter um mecanismo que apresente justificações para os pacientes selecionados. Nesta dissertação, apresentamos uma possível solução para resolver o problema da seleção de pacientes para ensaios clínicos, através da criação de uma aplicação web, intuitiva e fácil de utilizar, que apresenta a seleção de pacientes para ensaios clínicos de forma automática. Isto foi alcançado através da combinação de conhecimento expresso em diferentes formalismos. Integrámos o conhecimento médico usando ontologias, com os critérios que serão expressos usando regras não monotónicas. Para tratar do processo de validação, desenvolvemos um mecanismo que gera justificações para cada seleção juntamente com os resultados dos pacientes selecionados. No final, é esperado que o utilizador consiga inserir facilmente um conjunto de critérios de seleção, e a aplicação irá gerar os resultados dos pacientes selecionados e as respetivas justificações, com base nos critérios inseridos, informações médicas e uma base de dados com informações dos pacientes

Use Case Oriented Medical Visual Information Retrieval & System Evaluation

Large amounts of medical visual data are produced daily in hospitals, while new imaging techniques continue to emerge. In addition, many images are made available continuously via publications in the scientific literature and can also be valuable for clinical routine, research and education. Information retrieval systems are useful tools to provide access to the biomedical literature and fulfil the information needs of medical professionals. The tools developed in this thesis can potentially help clinicians make decisions about difficult diagnoses via a case-based retrieval system based on a use case associated with a specific evaluation task. This system retrieves articles from the biomedical literature when querying with a case description and attached images. This thesis proposes a multimodal approach for medical case-based retrieval with focus on the integration of visual information connected to text. Furthermore, the ImageCLEFmed evaluation campaign was organised during this thesis promoting medical retrieval system evaluation

New approaches to interactive multimedia content retrieval from different sources

Mención Internacional en el título de doctorInteractive Multimodal Information Retrieval systems (IMIR) increase the capabilities of traditional search systems with the ability to retrieve information in different types (modes) and from different sources. The increase in online content while diversifying means of access to information (phones, tablets, smart watches) encourages the growing need for this type of system. In this thesis a formal model for describing interactive multimodal information retrieval systems querying various information retrieval engines has been defined. This model includes formal and widespread definition of each component of an IMIR system, namely: multimodal information organized in collections, multimodal query, different retrieval engines, a source management system (handler), a results management module (fusion) and user interactions. This model has been validated in two stages. The first, in a use case focused on information retrieval on sports. A prototype that implements a subset of the features of the model has been developed: a multimodal collection that is semantically related, three types of multimodal queries (text, audio and text + image), six different retrieval engines (question answering, full-text search, search based on ontologies, OCR in image, object detection in image and audio transcription), a strategy for source selection based on rules defined by experts, a strategy of combining results and recording of user interactions. NDCG (normalized discounted cumulative gain) has been used for comparing the results obtained for each retrieval engine. These results are: 10,1% (Question answering), 80% (full text search) and 26;8% (ontology search). These results are on the order of works of the state of art considering forums like CLEF. When the retrieval engine combination is used, the information retrieval performance increases by a percentage gain of 771,4% with question answering, 7,2% with full text search and 145,5% with Ontology search. The second scenario is focused on a prototype retrieving information from social media in the health domain. A prototype has been developed which is based on the proposed model and integrates health domain social media user-generated information, knowledge bases, query, retrieval engines, sources selection module, results' combination module and GUI. In addition, the documents included in the retrieval system have been previously processed by a process that extracts semantic information in health domain. In addition, several adaptation techniques applied to the retrieval functionality of an IMIR system have been defined by analyzing past interactions using decision trees, neural networks and clusters. After modifying the sources selection strategy (handler), the system has been reevaluated using classification techniques. The same queries and relevance judgments done by users in the sports domain prototype will be used for this evaluation. This evaluation compares the normalized discounted cumulative gain (NDCG) measure obtained with two different approaches: the multimodal system using predefined rules and the same multimodal system once the functionality is adapted by past user interactions. The NDCG has shown an improvement between -2,92% and 2,81% depending on the approaches used. We have considered three features to classify the approaches: (i) the classification algorithm; (ii) the query features; and (iii) the scores for computing the orders of retrieval engines. The best result is obtained using probabilities-based classification algorithm, the retrieval engines ranking generated with Averaged-Position score and the mode, type, length and entities of the query. Its NDCG value is 81,54%.Los Sistemas Interactivos de Recuperación de Información Multimodal (IMIR) incrementan las capacidades de los sistemas tradicionales de búsqueda con la posibilidad de recuperar información de diferentes tipos (modos) y a partir de diferentes fuentes. El incremento del contenido en internet a la vez que la diversificación de los medios de acceso a la información (móviles, tabletas, relojes inteligentes) fomenta la necesidad cada vez mayor de este tipo de sistemas. En esta tesis se ha definido un modelo formal para la descripción de sistemas de recuperación de información multimodal e interactivos que consultan varios motores de recuperación. Este modelo incluye la definición formal y generalizada de cada componente de un sistema IMIR, a saber: información multimodal organizada en colecciones, consulta multimodal, diferentes motores de recuperación, sistema de gestión de fuentes (handler), módulo de gestión de resultados (fusión) y las interacciones de los usuarios. Este modelo se ha validado en dos escenarios. El primero, en un caso de uso focalizado en recuperación de información relativa a deportes. Se ha desarrollado un prototipo que implementa un subconjunto de todas las características del modelo: una colección multimodal que se relaciona semánticamente, tres tipos de consultas multimodal (texto, audio y texto + imagen), seis motores diferentes de recuperación (búsqueda de respuestas, búsqueda de texto completo, búsqueda basada en ontologías, OCR en imagen, detección de objetos en imagen y transcripción de audio), una estrategia de selección de fuentes basada en reglas definidas por expertos, una estrategia de combinación de resultados y el registro de las interacciones. Se utiliza la medida NDCG (normalized discounted cumulative gain) para describir los resultados obtenidos por cada motor de recuperación. Estos resultados son: 10,1% (Question Answering), 80% (Búsqueda a texto completo) y 26,8% (Búsqueda en ontologías). Estos resultados están en el orden de los trabajos del estado de arte considerando foros como CLEF (Cross-Language Evaluation Forum). Cuando se utiliza la combinación de motores de recuperación, el rendimiento de recuperación de información se incrementa en un porcentaje de ganancia de 771,4% con Question Answering, 7,2% con Búsqueda a texto completo y 145,5% con Búsqueda en ontologías. El segundo escenario es un prototipo centrado en recuperación de información de medios sociales en el dominio de salud. Se ha desarrollado un prototipo basado en el modelo propuesto y que integra información del dominio de salud generada por el usuario en medios sociales, bases de conocimiento, consulta, motores de recuperación, módulo de selección de fuentes, módulo de combinación de resultados y la interfaz gráfica de usuario. Además, los documentos incluidos en el sistema de recuperación han sido previamente anotados mediante un proceso de extracción de información semántica del dominio de salud. Además, se han definido técnicas de adaptación de la funcionalidad de recuperación de un sistema IMIR analizando interacciones pasadas mediante árboles de decisión, redes neuronales y agrupaciones. Una vez modificada la estrategia de selección de fuentes (handler), se ha evaluado de nuevo el sistema usando técnicas de clasificación. Las mismas consultas y juicios de relevancia realizadas por los usuarios en el primer prototipo sobre deportes se han utilizado para esta evaluación. La evaluación compara la medida NDCG (normalized discounted cumulative gain) obtenida con dos enfoques diferentes: el sistema multimodal usando reglas predefinidas y el mismo sistema multimodal una vez que la funcionalidad se ha adaptado por las interacciones de usuario. El NDCG ha mostrado una mejoría entre -2,92% y 2,81% en función de los métodos utilizados. Hemos considerado tres características para clasificar los enfoques: (i) el algoritmo de clasificación; (ii) las características de la consulta; y (iii) las puntuaciones para el cálculo del orden de los motores de recuperación. El mejor resultado se obtiene utilizando el algoritmo de clasificación basado en probabilidades, las puntuaciones para los motores de recuperación basados en la media de la posición del primer resultado relevante y el modo, el tipo, la longitud y las entidades de la consulta. Su valor de NDCG es 81,54%.Programa Oficial de Doctorado en Ciencia y Tecnología InformáticaPresidente: Ana García Serrano.- Secretario: María Belén Ruiz Mezcua.- Vocal: Davide Buscald