Contributions to information extraction for spanish written biomedical text

285 p.Healthcare practice and clinical research produce vast amounts of digitised, unstructured data in multiple languages that are currently underexploited, despite their potential applications in improving healthcare experiences, supporting trainee education, or enabling biomedical research, for example. To automatically transform those contents into relevant, structured information, advanced Natural Language Processing (NLP) mechanisms are required. In NLP, this task is known as Information Extraction. Our work takes place within this growing field of clinical NLP for the Spanish language, as we tackle three distinct problems. First, we compare several supervised machine learning approaches to the problem of sensitive data detection and classification. Specifically, we study the different approaches and their transferability in two corpora, one synthetic and the other authentic. Second, we present and evaluate UMLSmapper, a knowledge-intensive system for biomedical term identification based on the UMLS Metathesaurus. This system recognises and codifies terms without relying on annotated data nor external Named Entity Recognition tools. Although technically naive, it performs on par with more evolved systems, and does not exhibit a considerable deviation from other approaches that rely on oracle terms. Finally, we present and exploit a new corpus of real health records manually annotated with negation and uncertainty information: NUBes. This corpus is the basis for two sets of experiments, one on cue andscope detection, and the other on assertion classification. Throughout the thesis, we apply and compare techniques of varying levels of sophistication and novelty, which reflects the rapid advancement of the field

Ontology driven clinical decision support for early diagnostic recommendations

Diagnostic error is a significant problem in medicine and a major cause of concern for patients and clinicians and is associated with moderate to severe harm to patients. Diagnostic errors are a primary cause of clinical negligence and can result in malpractice claims. Cognitive errors caused by biases such as premature closure and confirmation bias have been identified as major cause of diagnostic error. Researchers have identified several strategies to reduce diagnostic error arising from cognitive factors. This includes considering alternatives, reducing reliance on memory, providing access to clear and well-organized information. Clinical Decision Support Systems (CDSSs) have been shown to reduce diagnostic errors. Clinical guidelines improve consistency of care and can potentially improve healthcare efficiency. They can alert clinicians to diagnostic tests and procedures that have the greatest evidence and provide the greatest benefit. Clinical guidelines can be used to streamline clinical decision making and provide the knowledge base for guideline based CDSSs and clinical alert systems. Clinical guidelines can potentially improve diagnostic decision making by improving information gathering. Argumentation is an emerging area for dealing with unstructured evidence in domains such as healthcare that are characterized by uncertainty. The knowledge needed to support decision making is expressed in the form of arguments. Argumentation has certain advantages over other decision support reasoning methods. This includes the ability to function with incomplete information, the ability to capture domain knowledge in an easy manner, using non-monotonic logic to support defeasible reasoning and providing recommendations in a manner that can be easily explained to clinicians. Argumentation is therefore a suitable method for generating early diagnostic recommendations. Argumentation-based CDSSs have been developed in a wide variety of clinical domains. However, the impact of an argumentation-based diagnostic Clinical Decision Support System (CDSS) has not been evaluated yet. The first part of this thesis evaluates the impact of guideline recommendations and an argumentation-based diagnostic CDSS on clinician information gathering and diagnostic decision making. In addition, the impact of guideline recommendations on management decision making was evaluated. The study found that argumentation is a viable method for generating diagnostic recommendations that can potentially help reduce diagnostic error. The study showed that guideline recommendations do have a positive impact on information gathering of optometrists and can potentially help optometrists in asking the right questions and performing tests as per current standards of care. Guideline recommendations were found to have a positive impact on management decision making. The CDSS is dependent on quality of data that is entered into the system. Faulty interpretation of data can lead the clinician to enter wrong data and cause the CDSS to provide wrong recommendations. Current generation argumentation-based CDSSs and other diagnostic decision support systems have problems with semantic interoperability that prevents them from using data from the web. The clinician and CDSS is limited to information collected during a clinical encounter and cannot access information on the web that could be relevant to a patient. This is due to the distributed nature of medical information and lack of semantic interoperability between healthcare systems. Current argumentation-based decision support applications require specialized tools for modelling and execution and this prevents widespread use and adoption of these tools especially when these tools require additional training and licensing arrangements. Semantic web and linked data technologies have been developed to overcome problems with semantic interoperability on the web. Ontology-based diagnostic CDSS applications have been developed using semantic web technology to overcome problems with semantic interoperability of healthcare data in decision support applications. However, these models have problems with expressiveness, requiring specialized software and algorithms for generating diagnostic recommendations. The second part of this thesis describes the development of an argumentation-based ontology driven diagnostic model and CDSS that can execute this model to generate ranked diagnostic recommendations. This novel model called the Disease-Symptom Model combines strengths of argumentation with strengths of semantic web technology. The model allows the domain expert to model arguments favouring and negating a diagnosis using OWL/RDF language. The model uses a simple weighting scheme that represents the degree of support of each argument within the model. The model uses SPARQL to sum weights and produce a ranked diagnostic recommendation. The model can provide justifications for each recommendation in a manner that clinicians can easily understand. CDSS prototypes that can execute this ontology model to generate diagnostic recommendations were developed. The decision support prototypes demonstrated the ability to use a wide variety of data and access remote data sources using linked data technologies to generate recommendations. The thesis was able to demonstrate the development of an argumentation-based ontology driven diagnostic decision support model and decision support system that can integrate information from a variety of sources to generate diagnostic recommendations. This decision support application was developed without the use of specialized software and tools for modelling and execution, while using a simple modelling method. The third part of this thesis details evaluation of the Disease-Symptom model across all stages of a clinical encounter by comparing the performance of the model with clinicians. The evaluation showed that the Disease-Symptom Model can provide a ranked diagnostic recommendation in early stages of the clinical encounter that is comparable to clinicians. The diagnostic performance can be improved in the early stages using linked data technologies to incorporate more information into the decision making. With limited information, depending on the type of case, the performance of the Disease-Symptom Model will vary. As more information is collected during the clinical encounter the decision support application can provide recommendations that is comparable to clinicians recruited for the study. The evaluation showed that even with a simple weighting and summation method used in the Disease- Symptom Model the diagnostic ranking was comparable to dentists. With limited information in the early stages of the clinical encounter the Disease-Symptom Model was able to provide an accurately ranked diagnostic recommendation validating the model and methods used in this thesis