Locally weighted factorization machine with fuzzy partition for elderly readmission prediction

Research on Artificial-Intelligence-Assisted Medicine: A Survey on Medical Artificial Intelligence

With the improvement of economic conditions and the increase in living standards, people's attention in regard to health is also continuously increasing. They are beginning to place their hopes on machines, expecting artificial intelligence (AI) to provide a more humanized medical environment and personalized services, thus greatly expanding the supply and bridging the gap between resource supply and demand. With the development of IoT technology, the arrival of the 5G and 6G communication era, and the enhancement of computing capabilities in particular, the development and application of AI-assisted healthcare have been further promoted. Currently, research on and the application of artificial intelligence in the field of medical assistance are continuously deepening and expanding. AI holds immense economic value and has many potential applications in regard to medical institutions, patients, and healthcare professionals. It has the ability to enhance medical efficiency, reduce healthcare costs, improve the quality of healthcare services, and provide a more intelligent and humanized service experience for healthcare professionals and patients. This study elaborates on AI development history and development timelines in the medical field, types of AI technologies in healthcare informatics, the application of AI in the medical field, and opportunities and challenges of AI in the field of medicine. The combination of healthcare and artificial intelligence has a profound impact on human life, improving human health levels and quality of life and changing human lifestyles.

Taxonomy of hybrid architectures involving rule-based reasoning and machine learning in clinical decision systems: A scoping review

BACKGROUND

As the application of Artificial Intelligence (AI) technologies increases in the healthcare sector, the industry faces a need to combine medical knowledge, often expressed as clinical rules, with advances in machine learning (ML), which offer high prediction accuracy at the expense of transparency of decision making.

PURPOSE

This paper seeks to review the present literature, identify hybrid architecture patterns that incorporate rules and machine learning, and evaluate the rationale behind their selection to inform future development and research on the design of transparent and precise clinical decision systems.

METHODS

PubMed, IEEE Explore, and Google Scholar were queried in search for papers from 1992 to 2022, with the keywords: "clinical decision system", "hybrid clinical architecture", "machine learning and clinical rules". Excluded articles did not use both ML and rules or did not provide any explanation of employed architecture. A proposed taxonomy was used to organize the results, analyze them, and depict them in graphical and tabular form. Two researchers, one with expertise in rule-based systems and another in ML, reviewed identified papers and discussed the work to minimize bias, and the third one re-reviewed the work to ensure consistency of reporting.

RESULTS

The authors screened 957 papers and reviewed 71 that met their criteria. Five distinct architecture archetypes were determined: Rules are Embedded in ML architecture (REML) (most used), ML pre-processes input data for Rule-Based inference (MLRB), Rule-Based method pre-processes input data for ML prediction (RBML), Rules influence ML training (RMLT), Parallel Ensemble of Rules and ML (PERML), which was rarely observed in clinical contexts.

CONCLUSIONS

Most architectures in the reviewed literature prioritize prediction accuracy over explainability and trustworthiness, which has led to more complex embedded approaches. Alternatively, parallel (PERML) architectures may be employed, allowing for a more transparent system that is easier to explain to patients and clinicians. The potential of this approach warrants further research.

OTHER

A limitation of the study may be that it reviews scientific literature, while algorithms implemented in clinical practice may present different distributions of motivations and implementations of hybrid architectures.

Intelligent Academic Specialties Selection in Higher Education for Ukrainian Entrants: A Recommendation System

In this article, we provide an approach to solve the problem of academic specialty selection in higher educational institutions with Ukrainian entrants as our target audience. This concern affects operations at universities or other academic institutions, the labor market, and the availability of in-demand professionals. We propose a decision-making architecture for a recommendation system to assist entrants with specialty selection as a solution. The modeled database is an integral part of the system to provide an in-depth university specialties description. We consider developing an API to consume the data and return predictions to users in our future studies. The exploratory data analysis of the 2021 university admission campaign in Ukraine confirmed our assumptions and revealed valuable insights into the specifics of specialty selection among entrants. We developed a comprehension that most entrants apply for popular but not necessarily in-demand specialties at universities. Our findings on association rules mining show that entrants are able to select alternative specialties adequately. However, it does not lead to successful admission to a desired tuition-free education form in all cases. So, we find it appropriate to deliver better decision-making on specialty selection, thus increasing the likelihood of university admission and professional development based on intelligent algorithms, user behavior analytics, and consultations with academic and career orientation experts. The results will be built into an intelligent virtual entrant’s assistant as a service.

Forecasting Hospital Readmissions with Machine Learning

Hospital readmissions are regarded as a compounding economic factor for healthcare systems. In fact, the readmission rate is used in many countries as an indicator of the quality of services provided by a health institution. The ability to forecast patients’ readmissions allows for timely intervention and better post-discharge strategies, preventing future life-threatening events, and reducing medical costs to either the patient or the healthcare system. In this paper, four machine learning models are used to forecast readmissions: support vector machines with a linear kernel, support vector machines with an RBF kernel, balanced random forests, and weighted random forests. The dataset consists of 11,172 actual records of hospitalizations obtained from the General Hospital of Komotini “Sismanogleio” with a total of 24 independent variables. Each record is composed of administrative, medical-clinical, and operational variables. The experimental results indicate that the balanced random forest model outperforms the competition, reaching a sensitivity of 0.70 and an AUC value of 0.78.