Patient-based Literature Retrieval and Integration: A Use Case for Diabetes and arterial Hypertension

Specialized search engines such as PubMed, MedScape or Cochrane have increased dramatically the visibility of biomedical scientific results. These web-based tools allow physicians to access scientific papers instantly. However, this decisive improvement had not a proportional impact in clinical practice due to the lack of advanced search methods. Even queries highly specified for a concrete pathology frequently retrieve too many information, with publications related to patients treated by the physician beyond the scope of the results examined. In this work we present a new method to improve scientific article search using patient information. Two pathologies have been used within the project to retrieve relevant literature to patient data and to be integrated with other sources. Promising results suggest the suitability of the approach, highlighting publications dealing with patient features and facilitating literature search to physicians.

A New Method to Retrieve, Cluster And Annotate Clinical Literature Related To Electronic Health Records

The access to medical literature collections such as PubMed, MedScape or Cochrane has been increased notably in the last years by the web-based tools that provide instant access to the information. However, more sophisticated methodologies are needed to exploit efficiently all that information. The lack of advanced search methods in clinical domain produce that even using well-defined questions for a particular disease, clinicians receive too many results. Since no information analysis is applied afterwards, some relevant results which are not presented in the top of the resultant collection could be ignored by the expert causing an important loose of information. In this work we present a new method to improve scientific article search using patient information for query generation. Using federated search strategy, it is able to simultaneously search in different resources and present a unique relevant literature collection. And applying NLP techniques it presents semantically similar publications together, facilitating the identification of relevant information to clinicians. This method aims to be the foundation of a collaborative environment for sharing clinical knowledge related to patients and scientific publications.

Dysfunctional 3D model based on structural and neuropsychological information

Acquired brain injury (ABI) 1-2 refers to any brain damage occurring after birth. It usually causes certain damage to portions of the brain. ABI may result in a significant impairment of an individuals physical, cognitive and/or psychosocial functioning. The main causes are traumatic brain injury (TBI), cerebrovascular accident (CVA) and brain tumors. The main consequence of ABI is a dramatic change in the individuals daily life. This change involves a disruption of the family, a loss of future income capacity and an increase of lifetime cost. One of the main challenges in neurorehabilitation is to obtain a dysfunctional profile of each patient in order to personalize the treatment. This paper proposes a system to generate a patient s dysfunctional profile by integrating theoretical, structural and neuropsychological information on a 3D brain imaging-based model. The main goal of this dysfunctional profile is to help therapists design the most suitable treatment for each patient. At the same time, the results obtained are a source of clinical evidence to improve the accuracy and quality of our rehabilitation system. Figure 1 shows the diagram of the system. This system is composed of four main modules: image-based extraction of parameters, theoretical modeling, classification and co-registration and visualization module.