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General General

Argument-based human-AI collaboration for supporting behavior change to improve health.

In Frontiers in artificial intelligence

This article presents an empirical requirement elicitation study for an argumentation-based digital companion for supporting behavior change, whose ultimate goal is the promotion and facilitation of healthy behavior. The study was conducted with non-expert users as well as with health experts and was in part supported by the development of prototypes. It focuses on human-centric aspects, in particular user motivations, as well as on expectations and perceptions regarding the role and interaction behavior of a digital companion. Based on the results of the study, a framework for person tailoring the agent's roles and behaviors, and argumentation schemes are proposed. The results indicate that the extent to which a digital companion argumentatively challenges or supports a user's attitudes and chosen behavior and how assertive and provocative the companion is may have a substantial and individualized effect on user acceptance, as well as on the effects of interacting with the digital companion. More broadly, the results shed some initial light on the perception of users and domain experts of "soft," meta-level aspects of argumentative dialogue, indicating potential for future research.

Kilic Kaan, Weck Saskia, Kampik Timotheus, Lindgren Helena

2023

Human-Centered Artificial Intelligence, argumentation schemes, behavior change, digital companion, formal argumentation dialogues, health promotion, user-modeling, value-based argumentation

General General

Data driven contagion risk management in low-income countries using machine learning applications with COVID-19 in South Asia.

In Scientific reports ; h5-index 158.0

In the absence of real-time surveillance data, it is difficult to derive an early warning system and potential outbreak locations with the existing epidemiological models, especially in resource-constrained countries. We proposed a contagion risk index (CR-Index)-based on publicly available national statistics-founded on communicable disease spreadability vectors. Utilizing the daily COVID-19 data (positive cases and deaths) from 2020 to 2022, we developed country-specific and sub-national CR-Index for South Asia (India, Pakistan, and Bangladesh) and identified potential infection hotspots-aiding policymakers with efficient mitigation planning. Across the study period, the week-by-week and fixed-effects regression estimates demonstrate a strong correlation between the proposed CR-Index and sub-national (district-level) COVID-19 statistics. We validated the CR-Index using machine learning methods by evaluating the out-of-sample predictive performance. Machine learning driven validation showed that the CR-Index can correctly predict districts with high incidents of COVID-19 cases and deaths more than 85% of the time. This proposed CR-Index is a simple, replicable, and easily interpretable tool that can help low-income countries prioritize resource mobilization to contain the disease spread and associated crisis management with global relevance and applicability. This index can also help to contain future pandemics (and epidemics) and manage their far-reaching adverse consequences.

Shonchoy Abu S, Mahzab Moogdho M, Mahmood Towhid I, Ali Manhal

2023-Mar-06

General General

[Individualized treatment in anesthesiology and intensive care medicine].

In Die Anaesthesiologie

BACKGROUND : Individualized medicine uses data on biological characteristics of individual patients in order to tailor treatment planning to their unique constitution. With respect to the practice of anesthesiology and intensive care medicine, it bears the potential to systematize the often complex medical care of critically ill patients and to improve outcomes.

OBJECTIVE : The aim of this narrative review is to provide an overview of the possible applications of the principles of individualized medicine in anesthesiology and intensive care medicine.

MATERIAL AND METHODS : Based on a search in MEDLINE, CENTRAL and Google Scholar, the results of previous studies and systematic reviews are narratively synthesized and the implications for the scientific and clinical practice are presented.

RESULTS AND DISCUSSION : There are possibilities for individualization and an increase in precision of patient care in most if not all problems in anesthesiology and symptoms in intensive medical care. Even now, all practicing physicians can initiate measures to individualize treatment at different timepoints throughout the course of treatment. Individualized medicine can supplement and be integrated into protocols. Plans for future applications of individualized medicine interventions should consider the feasibility in a real-world setting. Clinical studies should contain process evaluations in order to create ideal preconditions for a successful implementation. Quality management, audits and feedback should become a standard procedure to ensure sustainability. In the long run, individualization of care, especially in the critically ill, should be enshrined in guidelines and become an integral part of clinical practice.

Sadjadi Mahan, Meersch-Dini Melanie

2023-Mar-06

Acute kidney injury, Artificial intelligence, Biomarkers, Respiratory diseases, Sepsis

General General

Exploring the use of association rules in random forest for predicting heart disease.

In Computer methods in biomechanics and biomedical engineering

Heart disease is one of the most dangerous diseases in the world. People with these diseases, most of them end up losing their lives. Therefore, machine learning algorithms have proven to be useful in this sense to help decision-making and prediction from the large amount of data generated by the healthcare sector. In this work, we have proposed a novel method that allows increasing the performance of the classical random forest technique so that this technique can be used for the prediction of heart disease with its better performance. We used in this study other classifiers such as classical random forest, support vector machine, decision tree, Naïve Bayes, and XGBoost. This work was done in the heart dataset Cleveland. According to the experimental results, the accuracy of the proposed model is better than that of other classifiers with 83.5%.This study contributed to the optimization of the random forest technique as well as gave solid knowledge of the formation of this technique.

Barry Khalidou Abdoulaye, Manzali Youness, Flouchi Rachid, Balouki Youssef, Chelhi Khadija, Elfar Mohamed

2023-Mar-06

Heart disease, Naïve Bayes, XGBoost, association rules, decision tree, random forest, support vector machine

General General

KomaMRI.jl: An open-source framework for general MRI simulations with GPU acceleration.

In Magnetic resonance in medicine ; h5-index 66.0

PURPOSE : To develop an open-source, high-performance, easy-to-use, extensible, cross-platform, and general MRI simulation framework (Koma).

METHODS : Koma was developed using the Julia programming language. Like other MRI simulators, it solves the Bloch equations with CPU and GPU parallelization. The inputs are the scanner parameters, the phantom, and the pulse sequence that is Pulseq-compatible. The raw data is stored in the ISMRMRD format. For the reconstruction, MRIReco.jl is used. A graphical user interface utilizing web technologies was also designed. Two types of experiments were performed: one to compare the quality of the results and the execution speed, and the second to compare its usability. Finally, the use of Koma in quantitative imaging was demonstrated by simulating Magnetic Resonance Fingerprinting (MRF) acquisitions.

RESULTS : Koma was compared to two well-known open-source MRI simulators, JEMRIS and MRiLab. Highly accurate results (with mean absolute differences below 0.1% compared to JEMRIS) and better GPU performance than MRiLab were demonstrated. In an experiment with students, Koma was proved to be easy to use, eight times faster on personal computers than JEMRIS, and 65% of test subjects recommended it. The potential for designing acquisition and reconstruction techniques was also shown through the simulation of MRF acquisitions, with conclusions that agree with the literature.

CONCLUSIONS : Koma's speed and flexibility have the potential to make simulations more accessible for education and research. Koma is expected to be used for designing and testing novel pulse sequences before implementing them in the scanner with Pulseq files, and for creating synthetic data to train machine learning models.

Castillo-Passi Carlos, Coronado Ronal, Varela-Mattatall Gabriel, Alberola-López Carlos, Botnar René, Irarrazaval Pablo

2023-Mar-06

Bloch equations, GPU, GUI, Julia, open source, simulation

General General

Complex faces and naïve machines A commentary on facial perceptions of age, gender, and leader preferences in the age of AI.

In Politics and the life sciences : the journal of the Association for Politics and the Life Sciences

Tasks driven by artificial intelligence (AI), such as evaluating video job interviews, rely on facial recognition systems for decision-making. Therefore, it is extremely important that the science behind this technology is continually advancing. If not, visual stereotypes, such as those associated with facial age and gender, will lead to dangerous misapplications of AI.

Spisak Brian R

2023-Mar

age, face perception, facial recognition systems, gender, leadership, voting