Sonia E. Benítez

Nanoinformatics: developing new computing applications for nanomedicine

Nanoinformatics has recently emerged to address the need of computing applications at the nano level. In this regard, the authors have participated in various initiatives to identify its concepts, foundations and challenges. While nanomaterials open up the possibility for developing new devices in many industrial and scientific areas, they also offer breakthrough perspectives for the prevention, diagnosis and treatment of diseases. In this paper, we analyze the different aspects of nanoinformatics and suggest five research topics to help catalyze new research and development in the area, particularly focused on nanomedicine. We also encompass the use of informatics to further the biological and clinical applications of basic research in nanoscience and nanotechnology, and the related concept of an extended “nanotype” to coalesce information related to nanoparticles. We suggest how nanoinformatics could accelerate developments in nanomedicine, similarly to what happened with the Human Genome and other -omics projects, on issues like exchanging modeling and simulation methods and tools, linking toxicity information to clinical and personal databases or developing new approaches for scientific ontologies, among many others.

A Machine Learning Approach to Clinical Terms Normalization.

We propose a machine learning approach for semantic recognition and normalization of clinical term descriptions. Clinical terms considered here are noisy descriptions in Spanish language written by health care professionals in our electronic health record system. These description terms contain clinical findings, family history, suspected disease, among other categories of concepts. Descriptions are usually very short texts presenting high lexical variability containing synonymy, acronyms, abbreviations and typographical errors. Mapping description terms to normalized descriptions requires medical expertise which makes it difficult to develop a rule-based knowledge engineering approach. In order to build a training dataset we use those descriptions that have been previously matched by terminologists to the hospital thesaurus database. We generate a set of feature vectors based on pairs of descriptions involving their individual and joint characteristics. We propose an unsupervised learning approach to discover term equivalence classes including synonyms, abbreviations, acronyms and frequent typographical errors. We evaluate different combinations of features to train MaxEnt and XGBoost models. Our system achieves an F1 score of 89% on the Hospital Italiano de Buenos Aires (HIBA) problem list.

Patient-Rated Wrist Evaluation: Spanish Version and Evaluation of Its Psychometric Properties in Patients with Acute Distal Radius Fracture

Purpose The purpose of this paper is to validate a Spanish version of patient‐rated wrist evaluation (PRWE) and evaluate its psychometric attributes in a population of patients with distal radius fracture. Material and Methods A translation and cross‐cultural adaptation of the PRWE to Spanish (PRWE‐S) was performed according to standardized guidelines. A total of 50 patients with a distal radius fracture were included during the different steps of the study. The reliability of the new instrument was assessed in terms of construct validity with the Quick DASH (Disability of the Arm, Shoulder, and Hand). Internal consistency and test‐retest stability were also examined. Results The Spearmans correlation test for analysis of the criterion validity (0.75) indicates a strong positive correlation between the PRWE‐S and the Quick DASH. The internal consistency according to Cronbachs &agr; was 0.96, and the intraclass correlation coefficient was moderate (0.46). Conclusion A Spanish version of the PRWE indicated good validity and reliability in distal radius fracture patients. This valuable tool can be used in Spanish‐speaking countries to evaluate various aspects as pain and function in patients with injuries of the wrist.