Machine learning in human olfactory research

J Lötsch, D Kringel, T Hummel - Chemical senses, 2019 - academic.oup.com
The complexity of the human sense of smell is increasingly reflected in complex and high-
dimensional data, which opens opportunities for data-driven approaches that complement …

Explainable ai (xai) applied in machine learning for pain modeling: A review

R Madanu, MF Abbod, FJ Hsiao, WT Chen, JS Shieh - Technologies, 2022 - mdpi.com
Pain is a complex term that describes various sensations that create discomfort in various
ways or types inside the human body. Generally, pain has consequences that range from …

Uncertainty quantification in neural-network based pain intensity estimation

B Ozek, Z Lu, S Radhakrishnan, S Kamarthi - PloS one, 2024 - journals.plos.org
Improper pain management leads to severe physical or mental consequences, including
suffering, a negative impact on quality of life, and an increased risk of opioid dependency …

Experimental exploration of multilevel human pain assessment using blood volume pulse (bvp) signals

MU Khan, S Aziz, N Hirachan, C Joseph, J Li… - Sensors, 2023 - mdpi.com
Critically ill patients often lack cognitive or communicative functions, making it challenging to
assess their pain levels using self-reporting mechanisms. There is an urgent need for an …

Machine learning study of the extended drug–target interaction network informed by pain related voltage-gated sodium channels

L Chen, J Jiang, B Dou, H Feng, J Liu, Y Zhu, B Zhang… - Pain, 2024 - journals.lww.com
Pain is a significant global health issue, and the current treatment options for pain
management have limitations in terms of effectiveness, side effects, and potential for …

Machine learning and biological validation identify sphingolipids as potential mediators of paclitaxel-induced neuropathy in cancer patients

J Lötsch, K Gasimli, S Malkusch, L Hahnefeld… - Elife, 2024 - elifesciences.org
Background: Chemotherapy-induced peripheral neuropathy (CIPN) is a serious therapy-
limiting side effect of commonly used anticancer drugs. Previous studies suggest that lipids …

Chronic back pain sub-grouped via psychosocial, brain and physical factors using machine learning

SD Tagliaferri, T Wilkin, M Angelova, BM Fitzgibbon… - Scientific reports, 2022 - nature.com
Chronic back pain (CBP) is heterogenous and identifying sub-groups could improve clinical
decision making. Machine learning can build upon prior sub-grouping approaches by using …

Machine learning in chronic pain research: a scoping review

MDK Jenssen, PA Bakkevoll, PD Ngo, A Budrionis… - Applied Sciences, 2021 - mdpi.com
Given the high prevalence and associated cost of chronic pain, it has a significant impact on
individuals and society. Improvements in the treatment and management of chronic pain …

Identifying predictive factors for neuropathic pain after breast cancer surgery using machine learning

L Juwara, N Arora, M Gornitsky… - International journal of …, 2020 - Elsevier
Introduction Neuropathic pain (NP) remains a major debilitating condition affecting more
than 26% of breast cancer survivors worldwide. NP is diagnosed using a validated 10-items …

[HTML][HTML] Use of mobile health apps and wearable technology to assess changes and predict pain during treatment of acute pain in sickle cell disease: feasibility study

A Johnson, F Yang, S Gollarahalli… - JMIR mHealth and …, 2019 - mhealth.jmir.org
Background Sickle cell disease (SCD) is an inherited red blood cell disorder affecting
millions worldwide, and it results in many potential medical complications throughout the life …