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A major requirement for Credit Scoring models is of course to provide a risk prediction that is as accurate as possible. In addition, regulators demand these models to be transparent and auditable. Thus, in Credit Scoring very simple Predictive Models such as Logistic Regression or Decision Trees are still widely used and the superior predictive power of modern Machine Learning algorithms cannot be fully leveraged. A lot of potential is therefore missed, leading to higher reserves or more credit defaults. This talk presents an overview of techniques that are able to make “black box” machine learning models transparent and demonstrate how they can be applied in Credit Scoring. We use the DALEX set of tools to compare a traditional scoring approach with state of the art Machine Learning models and asses both approaches in terms of interpretability and predictive power. Results show that a comparable degree of interpretability can be achieved while machine learning techniques keep their ability to improve predictive power.
Background
Artificial intelligence (AI) has the capability to analyze vast amounts of data and has been applied in various healthcare sectors. However, its effectiveness in aiding pharmacotherapy decision-making remains uncertain due to the intricate, patient-specific, and dynamic nature of this field.
Objective
This study sought to investigate the potential of AI in guiding pharmacotherapy decisions using clinical data such as diagnoses, laboratory results, and vital signs obtained from routine patient care.
Methods
Data of a previous study on medication therapy optimization was updated and adapted for the purpose of this study. Analysis was conducted using R software along with the tidymodels extension packages. The dataset was split into 74% for training and 26% for testing. Decision trees were selected as the primary model due to their simplicity, transparency, and interpretability. To prevent overfitting, bootstrapping techniques were employed, and hyperparameters were fine-tuned. Performance metrics such as areas under the curve and accuracies were computed.
Results
The study cohort comprised 101 elderly patients with multiple diagnoses and complex medication regimens. The AI model demonstrated prediction accuracies ranging from 38% to 100% for various cardiovascular drug classes. Laboratory data and vital signs could not be interpreted, as the effect and dependence were unclear for the model. The study revealed that the issue of AI lag time in responding to sudden changes could be addressed by manually adjusting decision trees, a task not feasible with neural networks.
Conclusion
In conclusion, the AI model exhibited promise in recommending appropriate medications for individual patients. While the study identified several obstacles during model development, most were successfully resolved. Future AI studies need to include the drug effect, not only the drug, if laboratory data is part of the decision. This could assist with interpreting their potential relationship. Human oversight and intervention remain essential for an AI-driven pharmacotherapy decision support system to ensure safe and effective patient care.
Open Source Intelligence (OSINT), deriving intelligence from public data, has gained scrutiny since the Russian invasion of Ukraine. Despite numerous attempts at standard definitions, research around technology-driven intelligence gathering and analysis remains ambiguous. This paper uses a Design Science Research (DSR) approach to categorize the technology-driven intelligence construct. Analyzing sixty studies via structured literature review, three domains were identified: maturity, Intelligence Cycle phase, and use case. The resulting framework, developed into a trend radar, was evaluated with expert interviews, revealing technological gaps in planning/direction and dissemination/integration phases. While intelligent support technologies were noted, practical implementation lags behind theory. The human factor remains central to OSINT. Findings suggest future research should develop applications for underserved phases and examine why proven applications are not widely adopted, considering legal, ethical, political, and social factors. This study contributes to technology-driven intelligence literature as a knowledge base, research gap identifier, and guide for further research.
