Evaluation of machine learning algorithms for Health and Wellness applications: a tutorial

Research on decision support applications in healthcare, such as those related to diagnosis, prediction, treatment planning, etc., have seen enormously increased interest recently. This development is thanks to the increase in data availability as well as advances in artificial intelligence and machine learning research. Highly promising research examples are published daily. However, at the same time, there are some unrealistic expectations with regards to the requirements for reliable development and objective validation that is needed in healthcare settings. These expectations may lead to unmet schedules and disappointments (or non-uptake) at the end-user side. It is the aim of this tutorial to provide practical guidance on how to assess performance reliably and efficiently and avoid common traps. Instead of giving a list of do's and don't s, this tutorial tries to build a better understanding behind these do's and don't s and presents both the most relevant performance evaluation criteria as well as how to compute them. Along the way, we will indicate common mistakes and provide references discussing various topics more in-depth.Comment: To be published in Computers in Biology and Medicin

ENVISION – Improvement of intensive care of COVID-19 patients with artificial intelligence

The Envision project aims at developing artificial intelligence-based tools for supporting the treatment of critically ill COVID-19 patients in the intensive care unit. Twelve European hospitals participate in the collection of patient data for the development and validation of the artificial intelligence tools. Ten potential use cases have been identified as development targets. Data analysis and results from expert interviews are applied to define the clinically most relevant parameters and functional use cases to be used in providing decision support for the clinicians in the intensive care units for this patient group. The resulting artificial intelligence-based tool may be beneficial in the management of the next similar epidemics, as well.The Envision project aims at developing artificial intelligence-based tools for supporting the treatment of critically ill COVID-19 patients in the intensive care unit. Twelve European hospitals participate in the collection of patient data for the development and validation of the artificial intelligence tools. Ten potential use cases have been identified as development targets. Data analysis and results from expert interviews are applied to define the clinically most relevant parameters and functional use cases to be used in providing decision support for the clinicians in the intensive care units for this patient group. The resulting artificial intelligence-based tool may be beneficial in the management of the next similar epidemics, as well

A Comprehensive User Modeling Framework and a Recommender System for Personalizing Well-Being Related Behavior Change Interventions: Development and Evaluation

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Use of home telemonitoring to support multidisciplinary care of heart failure patients in Finland : Randomized controlled trial

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Pharmacogenetics of anticoagulation and clinical events in warfarin-treated patients : A register-based cohort study with biobank data and national health registries in Finland

Publisher Copyright: © 2021 Vuorinen et al.Purpose: To assess the association between VKORC1 and CYP2C9 variants and the incidence of adverse drug reactions in warfarin-treated patients in a real-world setting. Materials and Methods: This was a register-based cohort study (PreMed) linking data from Finnish biobanks, national health registries and patient records between January 1st 2007 and June 30th 2018. The inclusion criteria were: 1) >= 18 years of age, 2) CYP2C9 and VKORC1 genotype information available, 3) a diagnosis of a cardiovascular disease, 4) at least one warfarin purchase, 5) regular INR tests. Eligible individuals were divided into two warfarin sensitivity groups; normal responders, and sensitive and highly sensitive responders based on their VKORC1 and CYP2C9 genotypes. The incidences of clinical events were compared between the groups using Cox regression models. Results: The cohort consisted of 2508 participants (45% women, mean age of 69 years), of whom 65% were categorized as normal responders and 35% sensitive or highly sensitive responders. Compared to normal responders, sensitive and highly sensitive responders had fewer INR tests below 2 (median: 33.3% vs 43.8%, 95% CI: - 13.3%, - 10.0%) and more above 3 (median: 18.2% vs 6.7%, 95% Cl: 8.3%, 10.8%). The incidence (per 100 patient-years) of bleeding outcomes was 5.4 for normal responders and 5.6 for the sensitive and highly sensitive responder group (HR=1.03, 95% CI: 0.74, 1.44). The incidence of thromboembolic outcomes was 4.9 and 7.8, respectively (HR=1.48, 95% CI: 1.08, 2.03). Conclusion: In a real-world setting, genetically sensitive and highly sensitive responders to warfarin had more high INR tests and required a lower daily dose of warfarin than normal responders. However, the risk for bleeding events was not increased in sensitive and highly sensitive responders. Interestingly, the risk of thromboembolic outcomes was lower in normal responders compared to the sensitive and highly sensitive responders.Peer reviewe

Artificial intelligence research in the COVend COVID-19 clinical trial project

The COVend project aims at delivering a new effective therapy, FX06, against the SARS-CoV-2 virus infection for the management of the COVID-19 disease in hospitals. Nine of the 17 partners of the project consortium are hospitals responsible for collecting study subjects and administering the FX06 therapy to the patients. Although the clinical trial (IXION) has the main role in the project, the project has also a work package which develops and applies artificial intelligence (AI) methods to the data collected during the 28-day study period from the patients receiving the therapy. The AI work package applies exploratory data analysis methods to find patterns and profiles of the patients. Combined with the data about treatment methods and patient outcomes, the aim is to provide decision support for the therapy intervention in the later stage of the project.publishedVersionNon peer reviewe

Currículo, formación integral y calidad de vida en el contexto del caribe colombiano.

This paper is a reflection from the perspective of curriculum-oriented human development; from understanding the problems of the Colombian Caribbean are opportunities to rebuild the social and collective people in the context of their own realities and historical changes in the dynamics cultural. So that regional identity is constructed from the contributions of communities that build the educational curriculum aimed at training people full, proactive with leadership and management towards sustainable development in a changing worldEl presente artículo es una reflexión desde la perspectiva del currículo orientado al desarrollo humano integral, a partir de comprender que los problemas del caribe colombiano son oportunidades que permiten reconstruir al sujeto social y colectivo en el contexto de sus propias realidades y transformaciones, en la dinámica histórico cultural, de manera que se reconstruya la identidad regional a partir de los aportes de las comunidades educativas que edifican el currículo encaminado a formar personas plenas, proactivas, con capacidad de liderazgo y gestión hacia el desarrollo sostenible en un mundo en constante cambio