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  • 1.
    Gustavsson, Erik
    et al.
    Linköpings universitet, Institutionen för kultur och samhälle, Avdelningen för filosofi, historia, konst och religion. Linköpings universitet, Filosofiska fakulteten. Linköpings universitet, Institutionen för hälsa, medicin och vård, Avdelningen för samhälle och hälsa. Linköpings universitet, Medicinska fakulteten.
    Tinghög, Gustav
    Linköpings universitet, Institutionen för ekonomisk och industriell utveckling, Nationalekonomi. Linköpings universitet, Filosofiska fakulteten. Linköpings universitet, Institutionen för hälsa, medicin och vård, Avdelningen för samhälle och hälsa. Linköpings universitet, Medicinska fakulteten.
    Needs and cost-effectiveness in health care priority setting2020Inngår i: Health and Technology, ISSN 2190-7188, E-ISSN 2190-7196, Vol. 10, nr 3, s. 611-619Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    How to balance the maximization of health and concerns for the worse off remains a challenge for health care decision makers when setting priorities. In regulatory guidelines these concerns are typically specified in terms of priority setting according to needs and priority setting according to cost-effectiveness. Still, it is often unclear when and why needs and cost-effectiveness diverge or overlap as guiding priority setting principles in practice. We conduct a comparative analysis of need and cost-effectiveness in the context of health care priority setting. Based on theories of distributive justice we specify three normative interpretations of need and explicate how these relate to the normative basis for cost-effectiveness analysis. Using priority-setting dilemmas we then move on to explicate when and why need and cost-effectiveness diverge as priority-setting principles. We find that: (i) although principles of need and cost-effectiveness may recommend the same allocation of resources the underlying reason for an allocation is different; (ii) while they both may give weight to patients who are worse off they do so in different ways and to different degree; and (iii) whereas cost-effectiveness clearly implies the aggregation of benefits across individuals principles of needs give no guidance with regard to if, and if so, how needs should be aggregated. Priority setting according to needs or cost-effectiveness does not necessarily recommend different allocations of resources. Thus, the normative conflict between them, often highlighted in practice, seems exaggerated. For health policy this is important knowledge because unclear conceptions may obstruct an informed public discussion. Moreover, if decision-makers are to properly account for both principles they need to recognize the inconsistencies as well as similarities between the two.

    Fulltekst (pdf)
    fulltext
  • 2.
    Manzoor, Mirfa
    et al.
    Department of Informatics, Jönköping International Business School, Jönköping University, Jönköping, Sweden.
    Vimarlund, Vivian
    Linköpings universitet, Institutionen för datavetenskap, Interaktiva och kognitiva system. Linköpings universitet, Tekniska fakulteten. Department of Informatics, Jönköping International Business School, Jönköping University, Jönköping, Sweden.
    Digital technologies for social inclusion of individuals with disabilities2018Inngår i: Health and Technology, ISSN 2190-7188, E-ISSN 2190-7196, Vol. 8, nr 5, s. 377-390Artikkel, forskningsoversikt (Fagfellevurdert)
    Abstract [en]

    Information technology can be an important facilitator of social inclusion for people with disabilities into society. However, the goals specified in this area by organizations such as the European Commission have not yet been achieved in their totality. The aim of this paper is to explore which types of information communication technology-based applications and/or digital services have been suggested to facilitate the social integration of people who suffer from different types of disabilities. We performed a literature review that included studies published during a period of 6 years (2010-2016). The results show that, in the data we have had access to, no concrete patterns can be identified regarding the type of technology or technological trends that can be used to support the social integration of individuals with disabilities. This literature review is of relevance to the identification of further research areas and to the identification of issues which have to be considered in the context of the development and implementation of technological innovations that are aimed at promoting or facilitating social inclusion of individuals with disabilities.

    Fulltekst (pdf)
    fulltext
  • 3.
    Quinones-Espin, Alejandro Ernesto
    et al.
    Univ Cent Marta Abreu de las Villas, Cuba.
    Perez-Diaz, Marlen
    Univ Cent Marta Abreu de las Villas, Cuba.
    Espin-Coto, Rafaela Mayelin
    Hosp Clin Quirurg Docente Dr Celestino Hernandez R, Cuba.
    Rodríguez Linares, Deijany
    Linköpings universitet, Institutionen för systemteknik, Kommunikationssystem. Linköpings universitet, Tekniska fakulteten.
    Lopez-Cabrera, Jose Daniel
    Univ Cent Marta Abreu de las Villas, Cuba.
    Automatic detection of breast masses using deep learning with YOLO approach2023Inngår i: Health and Technology, ISSN 2190-7188, E-ISSN 2190-7196, Vol. 13, nr 6, s. 915-923Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    IntroductionBreast cancer is the most common malignant tumor among women. Mammography is the specific type of X-ray recommended to examine the breasts. However, they are difficult to interpret due to the size of the lesions, shape, indefinite borders, and low contrast of the masses with respect to healthy tissue, mainly in very dense breasts. Computer-aided detection (CAD) systems increase the efficiency of diagnoses and reduce the workload of specialists.PurposeA CAD system that uses artificial intelligence (AI) based on "You Only Look Once" (YOLO), with two models YOLOv5x and YOLOv5s, is tested for the detection of breast nodules from mammography.MethodTransfer learning and data augmentation techniques were applied. Image sets for training and validation were created from an international database (Vindr-Mammo). The network was trained and validated, and for the best model obtained, an external test was performed from a second database belonguing to "The Mammographic Image Analysis Society" (MIAS Database).ResultsThe best model was obtained with YOLOv5x. This reached a maximum sensitivity of 80% in internal validation and 72% with external test data.ConclusionYOLOv5x and YOLOv5s models showed potential for the task of detecting masses from mammographies.

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