FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W4378610338> ?p ?o ?g. }

• W4378610338 endingPage "A206" @default.
• W4378610338 startingPage "A206" @default.
• W4378610338 abstract "Abstract Introduction Machine Learning (ML) algorithms to predict Positive Airway Pressure (PAP) adherence may support personalized clinical management. Models were developed to predict adherence at various time-points after PAP initiation and in moving time windows. Methods Deep neural network (DNN) models were trained utilizing daily PAP data (Kaiser Permanente, Southern California). The DNN was evaluated with 10-fold cross-validation on N=21,397 patients. Algorithms developed included (a) Models 1 and 2 which utilized early usage to predict adherence at 90-days and 1-year respectively, and (b) Model 3 which utilized 14 and 30-day moving windows to predict subsequent usage. Regression analyses compared ML and Naïve (i.e., future use equals previous use) predictions versus Actual adherence. Results Model 1 predicted “% days without usage” for first 90-days based on first 7, 14, 21, 30-days of input and at 1-year (90-day window) based on first 30, 60, 90, 180-days of input. ML was superior to Naïve in predicting adherence [R 2 for ML versus Naïve compared to Actuals for different input days— 0.495-vs-0.193; 0.660-vs-0.465; 0.748-vs-0.607; 0.828-vs-0.735 at 90-days and 0.362-vs-0.104; 0.463-vs- 0.247; 0.513-vs-0.339; 0.680-vs-0.547 at 1-year; all p&lt; 0.05]. Model 2 predicted “hours/night” of use—ML did not outperform the Naïve prediction with similar R 2 ; however, when ML predicted &lt; 3 hours/night, nearly all patients had “no significant usage” at 1-year (comparatively, the naïve model had no differentiating threshold to predict this outcome.) Model 3 utilized different windows of PAP usage to predict subsequent usage. ML predictive accuracy was similar using 14 or 30-days of input [R 2 for ML vs. Actuals in predicting 7, 14, and 30-day “% days used ≥4 hours” were 0.687, 0.701, 0.699 using 14- days input and 0.582, 0.702, 0.77 using 30-days input; all p&lt; 0.05.] Conclusion ML algorithms based on PAP usage can predict future adherence, potentially supporting personalized treatment decisions and pre-emptive interventions when upcoming non-adherence is predicted. Support (if any) AASM Foundation SRA205-SR-19" @default.
• W4378610338 created "2023-05-29" @default.
• W4378610338 creator A5013520472 @default.
• W4378610338 creator A5014601454 @default.
• W4378610338 creator A5025302371 @default.
• W4378610338 creator A5038746097 @default.
• W4378610338 creator A5039310066 @default.
• W4378610338 creator A5049632112 @default.
• W4378610338 creator A5049962491 @default.
• W4378610338 creator A5053770947 @default.
• W4378610338 creator A5068468843 @default.
• W4378610338 creator A5072212993 @default.
• W4378610338 creator A5077086462 @default.
• W4378610338 creator A5078979686 @default.
• W4378610338 creator A5092040021 @default.
• W4378610338 date "2023-05-01" @default.
• W4378610338 modified "2023-09-30" @default.
• W4378610338 title "0464 Deep Learning to Predict PAP Adherence in Obstructive Sleep Apnea" @default.
• W4378610338 doi "https://doi.org/10.1093/sleep/zsad077.0464" @default.
• W4378610338 hasPublicationYear "2023" @default.
• W4378610338 type Work @default.
• W4378610338 citedByCount "0" @default.
• W4378610338 crossrefType "journal-article" @default.
• W4378610338 hasAuthorship W4378610338A5013520472 @default.
• W4378610338 hasAuthorship W4378610338A5014601454 @default.
• W4378610338 hasAuthorship W4378610338A5025302371 @default.
• W4378610338 hasAuthorship W4378610338A5038746097 @default.
• W4378610338 hasAuthorship W4378610338A5039310066 @default.
• W4378610338 hasAuthorship W4378610338A5049632112 @default.
• W4378610338 hasAuthorship W4378610338A5049962491 @default.
• W4378610338 hasAuthorship W4378610338A5053770947 @default.
• W4378610338 hasAuthorship W4378610338A5068468843 @default.
• W4378610338 hasAuthorship W4378610338A5072212993 @default.
• W4378610338 hasAuthorship W4378610338A5077086462 @default.
• W4378610338 hasAuthorship W4378610338A5078979686 @default.
• W4378610338 hasAuthorship W4378610338A5092040021 @default.
• W4378610338 hasBestOaLocation W43786103381 @default.
• W4378610338 hasConcept C119857082 @default.
• W4378610338 hasConcept C126322002 @default.
• W4378610338 hasConcept C2775867611 @default.
• W4378610338 hasConcept C2776006263 @default.
• W4378610338 hasConcept C41008148 @default.
• W4378610338 hasConcept C71924100 @default.
• W4378610338 hasConceptScore W4378610338C119857082 @default.
• W4378610338 hasConceptScore W4378610338C126322002 @default.
• W4378610338 hasConceptScore W4378610338C2775867611 @default.
• W4378610338 hasConceptScore W4378610338C2776006263 @default.
• W4378610338 hasConceptScore W4378610338C41008148 @default.
• W4378610338 hasConceptScore W4378610338C71924100 @default.
• W4378610338 hasIssue "Supplement_1" @default.
• W4378610338 hasLocation W43786103381 @default.
• W4378610338 hasOpenAccess W4378610338 @default.
• W4378610338 hasPrimaryLocation W43786103381 @default.
• W4378610338 hasRelatedWork W1977939635 @default.
• W4378610338 hasRelatedWork W2075103634 @default.
• W4378610338 hasRelatedWork W2319407515 @default.
• W4378610338 hasRelatedWork W2338486980 @default.
• W4378610338 hasRelatedWork W2358434394 @default.
• W4378610338 hasRelatedWork W2771357985 @default.
• W4378610338 hasRelatedWork W3018612691 @default.
• W4378610338 hasRelatedWork W4249862833 @default.
• W4378610338 hasRelatedWork W4249989462 @default.
• W4378610338 hasRelatedWork W4250185246 @default.
• W4378610338 hasVolume "46" @default.
• W4378610338 isParatext "false" @default.
• W4378610338 isRetracted "false" @default.
• W4378610338 workType "article" @default.