Evaluation of semantic dependencies in a conceptual co-occurrence network of a medical vocabulary

The amount of medical knowledge is constantly growing thus providing new hope for people having health-relatedproblems. However a challenge is to develop flexible methods to facilitate managing and interpretinglarge medical knowledge entities. There is a need to enhance health literacy by developing personalized healthsupport tools. Furthermore there is a need to assist decision-making with decision support tools. The recent andon-going changes in everyday life both on technological and societal levels (for example adoption of smartphones and personal mobile medical tracking devices, social networking, open source and open data initiatives,fast growth of accumulated medical data, need for new self-care solutions for aging European population)motivate to invest in the development of new computerized personalized methods for knowledge management ofmedical data for diagnosis and treatment. To enable creation of new adaptive personalized health support toolswe have carried out an evaluation of semantic dependencies in a conceptual co-occurrence network covering aset of concepts of a medical vocabulary with experimental results ranging up to 2994 unique nouns, 82814unique conceptual links and 200000 traversed link steps.Peer reviewe

Enabling personalized healthcare by analyzing semantic dependencies in a conceptual co-occurrence network based on a medical vocabulary

The amount of medical knowledge is constantly growing thus providing new hope for people having health-related problems. However a challenge is to develop flexible methods to facilitate managing and interpreting large medical knowledge entities. There is a need to enhance health literacy by developing personalized health support tools. Furthermore there is a need to assist decision-making with decision support tools. The recent and on-going changes in everyday life both on technological and societal levels (for example adoption of smart phones and personal mobile medical tracking devices, social networking, open source and open data initiatives, fast growth of accumulated medical data, need for new self-care solutions for aging European population) motivate to invest in the development of new computerized personalized methods for knowledge management of medical data for diagnosis and treatment. To enable creation of new adaptive personalized health support tools we have carried out an evaluation of semantic dependencies in a conceptual co-occurrence network covering a set of concepts of a medical vocabulary with experimental results ranging up to 2994 unique nouns, 82814 unique conceptual links and 200000 traversed link steps.Peer reviewe

Finding Important Terms for Patients in Their Electronic Health Records: A Learning-to-Rank Approach Using Expert Annotations

BACKGROUND: Many health organizations allow patients to access their own electronic health record (EHR) notes through online patient portals as a way to enhance patient-centered care. However, EHR notes are typically long and contain abundant medical jargon that can be difficult for patients to understand. In addition, many medical terms in patients\u27 notes are not directly related to their health care needs. One way to help patients better comprehend their own notes is to reduce information overload and help them focus on medical terms that matter most to them. Interventions can then be developed by giving them targeted education to improve their EHR comprehension and the quality of care. OBJECTIVE: We aimed to develop a supervised natural language processing (NLP) system called Finding impOrtant medical Concepts most Useful to patientS (FOCUS) that automatically identifies and ranks medical terms in EHR notes based on their importance to the patients. METHODS: First, we built an expert-annotated corpus. For each EHR note, 2 physicians independently identified medical terms important to the patient. Using the physicians\u27 agreement as the gold standard, we developed and evaluated FOCUS. FOCUS first identifies candidate terms from each EHR note using MetaMap and then ranks the terms using a support vector machine-based learn-to-rank algorithm. We explored rich learning features, including distributed word representation, Unified Medical Language System semantic type, topic features, and features derived from consumer health vocabulary. We compared FOCUS with 2 strong baseline NLP systems. RESULTS: Physicians annotated 90 EHR notes and identified a mean of 9 (SD 5) important terms per note. The Cohen\u27s kappa annotation agreement was .51. The 10-fold cross-validation results show that FOCUS achieved an area under the receiver operating characteristic curve (AUC-ROC) of 0.940 for ranking candidate terms from EHR notes to identify important terms. When including term identification, the performance of FOCUS for identifying important terms from EHR notes was 0.866 AUC-ROC. Both performance scores significantly exceeded the corresponding baseline system scores (P \u3c .001). Rich learning features contributed to FOCUS\u27s performance substantially. CONCLUSIONS: FOCUS can automatically rank terms from EHR notes based on their importance to patients. It may help develop future interventions that improve quality of care

Ontology Learning from the Arabic Text of the Qur’an: Concepts Identification and Hierarchical Relationships Extraction

Recent developments in ontology learning have highlighted the growing role ontologies play in linguistic and computational research areas such as language teaching and natural language processing. The ever-growing availability of annotations for the Qur’an text has made the acquisition of the ontological knowledge promising. However, the availability of resources and tools for Arabic ontology is not comparable with other languages. Manual ontology development is labour-intensive, time-consuming and it requires knowledge and skills of domain experts. This thesis aims to develop new methods for Ontology learning from the Arabic text of the Qur’an, including concepts identification and hierarchical relationships extraction. The thesis presents a methodology for reducing human intervention in building ontology from Classical Arabic Language of the Qur’an text. The set of concepts, which is a crucial step in ontology learning, was generated based on a set of patterns made of lexical and inflectional information. The concepts were identified based on adapted weighting schema that exploit a combination of knowledge to learn the relevance degree of a term. Statistical, domain-specific knowledge and internal information of Multi-Word Terms (MWTs) were combined to learn the relevance of generated terms. This methodology which represents the major contribution of the thesis was experimentally investigated using different terms generation methods. As a result, we provided the Arabic Qur’anic Terms (AQT) as a training resource for machine learning based term extraction. This thesis also introduces a new approach for hierarchical relations extraction from Arabic text of the Qur’an. A set of hierarchical relations occurring between identified concepts are extracted based on hybrid methods including head-modifier, set of markers for copula construct in Arabic text, referents. We also compared a number of ontology alignment methods for matching ontological bilingual Qur’anic resources. In addition, a multi-dimensional resource named Arabic Qur’anic Database (AQD) about the Qur’an is made for Arabic computational researchers, allowing regular expression query search over the included annotations. The search tool was successfully applied to find instances for a given complex rule made of different combined resources