Feature-Based Learning in Drug Prescription System for Medical Clinics

Designing the User Interface of a Nitroglycerin Dose Titration Decision Support System: User-Centered Design Study

BACKGROUND

 Nurses adjust intravenous nitroglycerin infusions to provide acute relief for angina by manually increasing or decreasing the dosage. However, titration can pose challenges, as excessively high doses can lead to hypotension, and low doses may result in inadequate pain relief. Clinical decision support systems (CDSSs) that predict changes in blood pressure for nitroglycerin dose adjustments may assist nurses with titration.

OBJECTIVE

 This study aimed to design a user interface for a CDSS for nitroglycerin dose titration (Nitroglycerin Dose Titration Decision Support System [nitro DSS]).

METHODS

 A user-centered design (UCD) approach, consisting of an initial qualitative study with semistructured interviews to identify design specifications for prototype development, was used. This was followed by three iterative rounds of usability testing. Nurses with experience titrating nitroglycerin infusions in coronary care units participated.

RESULTS

 A total of 20 nurses participated, including 7 during the qualitative study and 15 during usability testing (2 nurses participated in both phases). Analysis of the qualitative data revealed four themes for the interface design to be (1) clear and consistent, (2) vigilant, (3) interoperable, and (4) reliable. The major elements of the final prototype included a feature for viewing the predicted and actual blood pressure over time to determine the reliability of the predictions, a drop-down option to report patient side effects, a feature to report reasons for not accepting the prediction, and a visual alert indicating any systolic blood pressure predictions below 90 mm Hg. Nurses' ratings on the questionnaires indicated excellent usability and acceptability of the final nitro DSS prototype.

CONCLUSION

 This study successfully applied a UCD approach to collaborate with nurses in developing a user interface for the nitro DSS that supports the clinical decision-making of nurses titrating nitroglycerin.

Non-destructive monitoring of forming quality of self-piercing riveting via a lightweight deep learning

Self-piercing riveting (SPR) has been widely used in automobile body jointing. However, the riveting process is prone to various forming quality failures, such as empty riveting, repeated riveting, substrate cracking, and other riveting defects. This paper combines deep learning algorithms to achieve non-contact monitoring of SPR forming quality. And a lightweight convolutional neural network with higher accuracy and less computational effort is designed. The ablation and comparative experiments results show that the lightweight convolutional neural network proposed in this paper achieves improved accuracy and reduced computational complexity. Compared with the original algorithm, the algorithm's accuracy in this paper is increased by 4.5[Formula: see text], and the recall is increased by 1.4[Formula: see text]. In addition, the amount of redundant parameters is reduced by 86.5[Formula: see text], and the amount of computation is reduced by 47.33[Formula: see text]. This method can effectively overcome the limitations of low efficiency, high work intensity, and easy leakage of manual visual inspection methods and provide a more efficient solution for monitoring the quality of SPR forming quality.

Extraction of Data on Parent Compounds and Their Metabolites from Texts of Scientific Abstracts

The growing amount of experimental data on chemical objects includes properties of small molecules, results of studies of their interaction with human and animal proteins, and methods of synthesis of organic compounds (OCs). The data obtained can be used to identify the names of OCs automatically, including all possible synonyms and relevant data on the molecular properties and biological activity. Utilization of different synonymic names of chemical compounds allows researchers to increase the completeness of data on their properties available from publications. Enrichment of the data on the names of chemical compounds by information about their possible metabolites can help estimate the biological effects of parent compounds and their metabolites more thoroughly. Therefore, an attempt at automated extraction of the names of parent compounds and their metabolites from the texts is a rather important task. In our study, we aimed at developing a method that provides the extraction of the named entities (NEs) of parent compounds and their metabolites from abstracts of scientific publications. Based on the application of the conditional random fields' algorithm, we extracted the NEs of chemical compounds. We developed a set of rules allowing identification of parent compound NEs and their metabolites in the texts. We evaluated the possibility of extracting the names of potential metabolites based on cosine similarity between strings representing names of parent compounds and all other chemical NEs found in the text. Additionally, we used conditional random fields to fetch the names of parent compounds and their metabolites from the texts based on the corpus of texts labeled manually. Our computational experiments showed that usage of rules in combination with cosine similarity could increase the accuracy of recognition of the names of metabolites compared to the rule-based algorithm and application of a machine-learning algorithm (conditional random fields).