Logo des Repositoriums
 

Continuous support for rehabilitation using machine learning

dc.contributor.authorPhilipp, Patrick
dc.contributor.authorMerkle, Nicole
dc.contributor.authorGand, Kai
dc.contributor.authorGißke, Carola
dc.date.accessioned2021-06-21T12:20:11Z
dc.date.available2021-06-21T12:20:11Z
dc.date.issued2019
dc.description.abstractProviding a suitable rehabilitation at home after an acute episode or a chronic disease is a major issue as it helps people to live independently and enhance their quality of life. However, as the rehabilitation period usually lasts some months, the continuity of care is often interrupted in the transition from the hospital to the home. Relieving the healthcare system and personalizing the care or even bringing care to the patients’ home to a greater extent is, in consequence, the superior need. This is why we propose to make use of information technology to come to participatory design driven by users needs and the personalisation of the care pathways enabled by technology. To allow this, patient rehabilitation at home needs to be supported by automatic decision-making, as physicians cannot constantly supervise the rehabilitation process. Thus, we need computer-assisted patient rehabilitation, which monitors the fitness of the current patient plan to detect sub-optimality, proposes personalised changes for a patient and eventually generalizes over patients and proposes better initial plans. Therefore, we will explain the use case of patient rehabilitation at home, the basic challenges in this field and machine learning applications that could address these challenges by technical means.en
dc.identifier.doi10.1515/itit-2019-0022
dc.identifier.pissn2196-7032
dc.identifier.urihttps://dl.gi.de/handle/20.500.12116/36671
dc.language.isoen
dc.publisherDe Gruyter
dc.relation.ispartofit - Information Technology: Vol. 61, No. 5-6
dc.subjectAutomated Patient Rehabilitation
dc.subjectMachine Learning
dc.subjectReinforcement Learning
dc.subjectSemantic Web
dc.titleContinuous support for rehabilitation using machine learningen
dc.typeText/Journal Article
gi.citation.endPage284
gi.citation.publisherPlaceBerlin
gi.citation.startPage273

Dateien