Year: 2023
Type: preprint
Abstract: Recently developed survival analysis methods improve upon existing approaches by predicting the probability of event occurrence in each of a number pre-specified (discrete) time intervals. By avoiding placing strong parametric assumptions on the event density, this approach tends to improve prediction performance, particularly when data are plentiful. However, in clinical settings with limited available data, it is often preferable to judiciously partition the event time space into a limited number of intervals well suited to the prediction task at hand. In this work, we develop a method to learn from data a set of cut points defining such a partition. We show that in two simulated datasets, we are able to recover intervals that match the underlying generative model. We then demonstrate improved prediction performance on three real-world observational datasets, including a large, newly harmonized stroke risk prediction dataset. Finally, we argue that our approach facilitates clinical decision-making by suggesting time intervals that are most appropriate for each task, in the sense that they facilitate more accurate risk prediction.
Source: arXiv (Cornell University)
Language: English
Subfield: Statistics and Probability
Field: Mathematics
Domain: Physical Sciences
Open Access status: green
Funders: U.S. Department of Health and Human Services, Georgia Clinical and Translational Science Alliance, National Institutes of Health, National Institute on Aging, National Heart, Lung, and Blood Institute, National Institute of Mental Health, National Institute of Neurological Disorders and Stroke
Awards: G109384441, G1114307781, G1200636938, G1225203148, G1261793039, G1276754516, G1304508731, SUBCLINICAL CARDIOVASCULAR DISEASE STUDY-FIELD CENTER, G1373328648, G1522370804, G1558725192, G1640378483, G1740741465, G1788514665, G1823732541, G1982334678, G2130630498, G2140171847, G2144938473, G2169623015, G22458031, G2258234602, G2262505034, G2326437104, G250621538, G2568281471, G2607172802, G2733059257, G2779429302, G278200998, G2784128823, G2828349578, G2837638605, G2905571579, G290884426, G291913881, G296458788, G3093995152, G3161396180, G3205625555, G3252259982, G33312777, SUBCLINICAL CARDIOVASCULAR DISEASE STUDY--FIELD CENTER, G3744596180, G3819938352, G3973572274, G3990748671, G4014733286, G4036023366, G4186273061, G4222455271, G4312169524, SUBCLINICAL CARDIOVASCULAR DISEASE STUDY-ULTRASOUND, G4597579782, G4677375346, G4688346656, G4739409156, G4765588100, G4790942633, G4845182219, G5023962947, G509631191, G5234713383, G5281659474, G5352314984, G55907201, SUBCLINICAL CARDIOVASCULAR DISEASE STUDY--FIELD CENTER, G562502316, G5654179670, SUBCLINICAL CARDIOVASCULAR DISEASE STUDY--FIELD CENTER, G5755700318, G5770678025, G5999060983, G6060132884, G6594067713, G66341962, G6739173425, G6837735701, SUBCLINICAL CARDIOVASCULAR DISEASE STUDY, G706815212, G7132376052, SUBCLINICAL CARDIOVASCULAR DISEASE STUDY--FIELD CENTER, G7495086035, G7808459314, G78770644, G7924587209, SUBCLINICAL CARDIOVASCULAR DISEASE STUDY--EBCT READING, G8492131798, SUBCLINICAL CARDIOVASCULAR DISEASE STUDY-FIELD CENTER, G8683204365, G9000004549, G9000004784, G9000006353, G9000008877, G9000017360, G9000019818, G9000025517, G9000031215, G9000063950, G9000065698, G9000087307, G9000124674, G9000191827, Machine Learning Methods to Develop and Deploy Real-Time Risk Surveillance for Autism Spectrum Disorder and Attention Deficit Hyperactivity Disorder from the Electronic Health Record, G939786002, G991334795
OpenAlex ID: W4387947456