Matches in SemOpenAlex for { <https://semopenalex.org/work/W4387035996> ?p ?o ?g. }
Showing items 1 to 71 of
71
with 100 items per page.
- W4387035996 endingPage "100430" @default.
- W4387035996 startingPage "100430" @default.
- W4387035996 abstract "Parkinson's disease (PD) is a neurodegenerative chronic disorder with multiple motor and non-motor symptoms. As PD has no ultimate cure, physicians aim to delay PD complications, especially those that degrade the patient's quality of life such as motor symptoms and dyskinesia. Patients' lack of adherence to prescribed medication is a major challenge for physicians, especially for patients suffering from chronic conditions. The Centers for Disease Control and Prevention (CDC) estimates that medication non-adherence causes 30 to 50 percent of chronic disease treatment failures and 125,000 deaths per year in the USA (U.S. Foods and Drugs Administration (FDA), 2021). In PD patients particularly, adherence varies between 10% and 67% (Straka et al., 2019). The goal of this work is to remotely determine whether PD patients have taken their medication, by analyzing gait data gathered from their smartphone sensors. Using this approach, physicians can track the level of medication adherence of their PD patients. Using data from the mPower study (Bot et al., 2016), we selected 152 PD patients who recorded at least 3 walks before and 3 after taking medications and 304 healthy controls (HC) who recorded 3 walks at minimum. We extracted each subject's gait cycle from their accelerometer and gyroscope sensors data. The sensor data corresponding to gait cycles were fed to DeePaMed; a multilayer Conventional Neural Network (CNN), crafted for patches of gait strides. DeePaMed classified 30 s of a walk as either PD patient “On” vs. “Off” medication, or if the gait data belongs to an HC. Our DeePaMed model was able to discriminate PD patients on-vs off-medication and baseline HC walk with an accuracy of 98.2%. The accuracy of our CNN model surpassed that of traditional Machine Learning methods by over 17%. We also found that our model performed best with inputs containing a minimum of 10 full gait strides. Medication non-adherence can be accurately predicted using smartphone sensing of the motor symptoms of PD, suggesting that PD patients’ medication response and non-adherence can be monitored remotely via smartphone-based measures." @default.
- W4387035996 created "2023-09-27" @default.
- W4387035996 creator A5000968117 @default.
- W4387035996 creator A5003809101 @default.
- W4387035996 creator A5009247867 @default.
- W4387035996 date "2023-12-01" @default.
- W4387035996 modified "2023-10-06" @default.
- W4387035996 title "DeepaMed: Deep learning-based medication adherence of Parkinson's disease using smartphone gait analysis" @default.
- W4387035996 cites W1425868093 @default.
- W4387035996 cites W1986453498 @default.
- W4387035996 cites W2005762357 @default.
- W4387035996 cites W2026580446 @default.
- W4387035996 cites W2111327231 @default.
- W4387035996 cites W2121356281 @default.
- W4387035996 cites W2138405305 @default.
- W4387035996 cites W2142270985 @default.
- W4387035996 cites W2149958198 @default.
- W4387035996 cites W2160219101 @default.
- W4387035996 cites W2296350496 @default.
- W4387035996 cites W2626095355 @default.
- W4387035996 cites W2908705712 @default.
- W4387035996 cites W2908875379 @default.
- W4387035996 cites W2955805844 @default.
- W4387035996 cites W3014370958 @default.
- W4387035996 cites W3095648103 @default.
- W4387035996 cites W3178461781 @default.
- W4387035996 doi "https://doi.org/10.1016/j.smhl.2023.100430" @default.
- W4387035996 hasPublicationYear "2023" @default.
- W4387035996 type Work @default.
- W4387035996 citedByCount "0" @default.
- W4387035996 crossrefType "journal-article" @default.
- W4387035996 hasAuthorship W4387035996A5000968117 @default.
- W4387035996 hasAuthorship W4387035996A5003809101 @default.
- W4387035996 hasAuthorship W4387035996A5009247867 @default.
- W4387035996 hasConcept C126322002 @default.
- W4387035996 hasConcept C151800584 @default.
- W4387035996 hasConcept C173906292 @default.
- W4387035996 hasConcept C1862650 @default.
- W4387035996 hasConcept C2779134260 @default.
- W4387035996 hasConcept C2779734285 @default.
- W4387035996 hasConcept C2780405171 @default.
- W4387035996 hasConcept C71924100 @default.
- W4387035996 hasConcept C99508421 @default.
- W4387035996 hasConceptScore W4387035996C126322002 @default.
- W4387035996 hasConceptScore W4387035996C151800584 @default.
- W4387035996 hasConceptScore W4387035996C173906292 @default.
- W4387035996 hasConceptScore W4387035996C1862650 @default.
- W4387035996 hasConceptScore W4387035996C2779134260 @default.
- W4387035996 hasConceptScore W4387035996C2779734285 @default.
- W4387035996 hasConceptScore W4387035996C2780405171 @default.
- W4387035996 hasConceptScore W4387035996C71924100 @default.
- W4387035996 hasConceptScore W4387035996C99508421 @default.
- W4387035996 hasLocation W43870359961 @default.
- W4387035996 hasOpenAccess W4387035996 @default.
- W4387035996 hasPrimaryLocation W43870359961 @default.
- W4387035996 hasRelatedWork W1989734657 @default.
- W4387035996 hasRelatedWork W2008860278 @default.
- W4387035996 hasRelatedWork W2031771476 @default.
- W4387035996 hasRelatedWork W2051854229 @default.
- W4387035996 hasRelatedWork W2133973503 @default.
- W4387035996 hasRelatedWork W2471060339 @default.
- W4387035996 hasRelatedWork W3113494795 @default.
- W4387035996 hasRelatedWork W4255104918 @default.
- W4387035996 hasRelatedWork W4321443899 @default.
- W4387035996 hasRelatedWork W2512971311 @default.
- W4387035996 hasVolume "30" @default.
- W4387035996 isParatext "false" @default.
- W4387035996 isRetracted "false" @default.
- W4387035996 workType "article" @default.