Sökning: "förutsägelse av sjukdom"
Visar resultat 1 - 5 av 7 uppsatser innehållade orden förutsägelse av sjukdom.
1. Parkinson’s disease tremor assessment: Leveragingsmartphones for symptom measurement
M1-uppsats, Malmö universitet/Institutionen för datavetenskap och medieteknik (DVMT)Sammanfattning : Parkinson's disease (PD) is a progressive, chronic neurodegenerative disorder that impacts patients' quality of life. Hand tremor is a hallmark motor symptom of PD. However, current clinical tremor assessment methods are time-consuming and expensive and may not capture the full extent of tremor fluctuations. LÄS MER
2. Predictive MR Image Generation for Alzheimer’s Disease and Normal Aging Using Diffeomorphic Registration
Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)Sammanfattning : Alzheimer´s Disease (AD) is the most prevalent cause of dementia, signifying a progressive and degenerative brain disorder that causes cognitive function deterioration including memory loss, communication difficulties, impaired judgment, and changes in behavior and personality. Compared to normal aging, AD introduces more profound cognitive impairments and brain morphology changes. LÄS MER
3. Vitiligo image classification using pre-trained Convolutional Neural Network Architectures, and its economic impact on health care
M1-uppsats, KTH/Hälsoinformatik och logistikSammanfattning : Vitiligo is a skin disease where the pigment cells that produce melanin die or stop functioning, which causes white patches to appear on the body. Although vitiligo is not considered a serious disease, there is a risk that something is wrong with a person's immune system. LÄS MER
4. Privacy Preserving Survival Prediction With Graph Neural Networks
Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)Sammanfattning : In the development process of novel cancer drugs, one important aspect is to identify patient populations with a high risk of early death so that resources can be focused on patients with the highest medical unmet need. Many cancer types are heterogeneous and there is a need to identify patients with aggressive diseases, meaning a high risk of early death, compared to patients with indolent diseases, meaning a low risk of early death. LÄS MER
5. Encoding Temporal Healthcare Data for Machine Learning
Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)Sammanfattning : This thesis contains a review of previous work in the fields of encoding sequential healthcare data and predicting graft- versus- host disease, a medical condition, based on patient history using machine learning. A new encoding of such data is proposed for machine learning purposes. LÄS MER