Semantic Academic Profiler (SAP): a framework for researcher assessment based on semantic topic modeling.

Recent efforts have focused on identifying multidisciplinary teams and detecting co-Authorship Networks based on exploring topic modeling to identify researchers' expertise. Though promising, none of these efforts perform a real-life evaluation of the quality of the built topics. This paper proposes a Semantic Academic Profiler (SAP) framework that allows summarizing articles written by researchers to automatically build research profiles and perform online evaluations regarding these built profiles. SAP exploits and extends state-of-the-art Topic Modeling strategies based on Cluwords considering n-grams and introduces a new visual interface able to highlight the main topics related to articles, researchers and institutions. To evaluate SAP's capability of summarizing the profile of such entities as well as its usefulness for supporting online assessments of the topics' quality, we perform and contrast two types of evaluation, considering an extensive repository of Brazilian curricula vitae: (1) an offline evaluation, in which we exploit a traditional metric (NPMI) to measure the quality of several data representations strategies including (i) TFIDF, (ii) TFIDF with Bi-grams, (iii) Cluwords, and (iv) CluWords with Bi-grams; and (2) an online evaluation through an A/B test where researchers evaluate their own built profiles. We also perform an online assessment of SAP user interface through a usability test following the SUS methodology. Our experiments indicate that the CluWords with Bi-grams is the best solution and the SAP interface is very useful. We also observed essential differences in the online and offline assessments, indicating that using both together is very important for a comprehensive quality evaluation. Such type of study is scarce in the literature and our findings open space for new lines of investigation in the Topic Modeling area.

Design of experiments (DoE) to develop and to optimize nanoparticles as drug delivery systems.

Nanotechnology has been widely applied to develop drug delivery systems to improve therapeutic performance. The effectiveness of these systems is intrinsically related to their physicochemical properties, so their biological responses are highly susceptible to factors such as the type and quantity of each material that is employed in their synthesis and to the method that is used to produce them. In this context, quality-oriented manufacturing of nanoparticles has been an important strategy to understand and to optimize the factors involved in their production. For this purpose, Design of Experiment (DoE) tools have been applied to obtain enough knowledge about the process and hence achieve high-quality products. This review aims to set up the bases to implement DoE as a strategy to improve the manufacture of nanocarriers and to discuss the main factors involved in the production of the most common nanocarriers employed in the pharmaceutical field.

Stroke Outcome Measurements From Electronic Medical Records: Cross-sectional Study on the Effectiveness of Neural and Nonneural Classifiers.

BACKGROUND: With the rapid adoption of electronic medical records (EMRs), there is an ever-increasing opportunity to collect data and extract knowledge from EMRs to support patient-centered stroke management. OBJECTIVE: This study aims to compare the effectiveness of state-of-the-art automatic text classification methods in classifying data to support the prediction of clinical patient outcomes and the extraction of patient characteristics from EMRs. METHODS: Our study addressed the computational problems of information extraction and automatic text classification. We identified essential tasks to be considered in an ischemic stroke value-based program. The 30 selected tasks were classified (manually labeled by specialists) according to the following value agenda: tier 1 (achieved health care status), tier 2 (recovery process), care related (clinical management and risk scores), and baseline characteristics. The analyzed data set was retrospectively extracted from the EMRs of patients with stroke from a private Brazilian hospital between 2018 and 2019. A total of 44,206 sentences from free-text medical records in Portuguese were used to train and develop 10 supervised computational machine learning methods, including state-of-the-art neural and nonneural methods, along with ontological rules. As an experimental protocol, we used a 5-fold cross-validation procedure repeated 6 times, along with subject-wise sampling. A heatmap was used to display comparative result analyses according to the best algorithmic effectiveness (F1 score), supported by statistical significance tests. A feature importance analysis was conducted to provide insights into the results. RESULTS: The top-performing models were support vector machines trained with lexical and semantic textual features, showing the importance of dealing with noise in EMR textual representations. The support vector machine models produced statistically superior results in 71% (17/24) of tasks, with an F1 score >80% regarding care-related tasks (patient treatment location, fall risk, thrombolytic therapy, and pressure ulcer risk), the process of recovery (ability to feed orally or ambulate and communicate), health care status achieved (mortality), and baseline characteristics (diabetes, obesity, dyslipidemia, and smoking status). Neural methods were largely outperformed by more traditional nonneural methods, given the characteristics of the data set. Ontological rules were also effective in tasks such as baseline characteristics (alcoholism, atrial fibrillation, and coronary artery disease) and the Rankin scale. The complementarity in effectiveness among models suggests that a combination of models could enhance the results and cover more tasks in the future. CONCLUSIONS: Advances in information technology capacity are essential for scalability and agility in measuring health status outcomes. This study allowed us to measure effectiveness and identify opportunities for automating the classification of outcomes of specific tasks related to clinical conditions of stroke victims, and thus ultimately assess the possibility of proactively using these machine learning techniques in real-world situations.