AI-Driven Longitudinal Characterization of Neonatal Health and Morbidity
Abstract While prematurity is the single largest cause of death in children under 5 years of age, the current definition of prematurity, based on gestational age, lacks the precision needed for guiding care decisions. Here we propose a longitudinal risk assessment for adverse neonatal outcomes in newborns based on a multi-task deep learning model that uses electronic health records (EHRs) to predict a wide range of outcomes over a period starting shortly after the time of conception and ending months after birth. By linking the EHRs of the Lucile Packard Children’s Hospital and the Stanford Healthcare Adult Hospital, we developed a cohort of 22,104 mother-newborn dyads delivered between 2014 and 2018. This enabled a unique linkage between long-term maternal information and newborn outcomes. Maternal and newborn EHRs were extracted and used to train a multi-input multi-task deep learning model, featuring a long short-term memory neural network, to predict 24 different neonatal outcomes. An additional set of 10,250 mother-newborn dyads delivered at the same Stanford Hospitals from 2019 to September 2020 was used to independently validate the model, followed by a separate analysis of 12,256 mothers-newborn dyads at the University of California, San Francisco. Moreover, comprehensive association analysis identified multiple known and new associations between various maternal and neonatal features and specific neonatal outcomes. To date, this is the largest study utilizing linked EHRs from mother-newborn dyads and would serve as an important resource for the investigation and prediction of neonatal outcomes. An interactive website is available for independent investigators to leverage this unique dataset:<jats:ext-link xmlns:xlink="http://www.w3.org/1999/xlink" ext-link-type="uri" xlink:href="https://maternal-child-health-associations.shinyapps.io/shiny_app/">https://maternal-child-health-associations.shinyapps.io/shiny_app/</jats:ext-link>..
| Medienart: |
|
|---|
Preprint| Erscheinungsjahr: |
2023 |
|---|---|
| Erschienen: |
2023 |
| Enthalten in: |
bioRxiv.org - (2023) vom: 31. Jan. Zur Gesamtaufnahme - year:2023 |
|---|
| Sprache: |
Englisch |
|---|
| Links: |
Volltext [kostenfrei] |
|---|
| Themen: |
|---|
| doi: |
10.1101/2022.03.31.22273233 |
|---|
| funding: |
|
|---|---|
| Förderinstitution / Projekttitel: |
|
| PPN (Katalog-ID): |
XBI035679522 |
|---|
| LEADER | 01000caa a22002652 4500 | ||
|---|---|---|---|
| 001 | XBI035679522 | ||
| 003 | DE-627 | ||
| 005 | 20230429084503.0 | ||
| 007 | cr uuu---uuuuu | ||
| 008 | 220406s2023 xx |||||o 00| ||eng c | ||
| 024 | 7 | |a 10.1101/2022.03.31.22273233 |2 doi | |
| 035 | |a (DE-627)XBI035679522 | ||
| 035 | |a (biorXiv)10.1101/2022.03.31.22273233 | ||
| 040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
| 041 | |a eng | ||
| 100 | 1 | |a De Francesco, Davide |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a AI-Driven Longitudinal Characterization of Neonatal Health and Morbidity |
| 264 | 1 | |c 2023 | |
| 336 | |a Text |b txt |2 rdacontent | ||
| 337 | |a Computermedien |b c |2 rdamedia | ||
| 338 | |a Online-Ressource |b cr |2 rdacarrier | ||
| 520 | |a Abstract While prematurity is the single largest cause of death in children under 5 years of age, the current definition of prematurity, based on gestational age, lacks the precision needed for guiding care decisions. Here we propose a longitudinal risk assessment for adverse neonatal outcomes in newborns based on a multi-task deep learning model that uses electronic health records (EHRs) to predict a wide range of outcomes over a period starting shortly after the time of conception and ending months after birth. By linking the EHRs of the Lucile Packard Children’s Hospital and the Stanford Healthcare Adult Hospital, we developed a cohort of 22,104 mother-newborn dyads delivered between 2014 and 2018. This enabled a unique linkage between long-term maternal information and newborn outcomes. Maternal and newborn EHRs were extracted and used to train a multi-input multi-task deep learning model, featuring a long short-term memory neural network, to predict 24 different neonatal outcomes. An additional set of 10,250 mother-newborn dyads delivered at the same Stanford Hospitals from 2019 to September 2020 was used to independently validate the model, followed by a separate analysis of 12,256 mothers-newborn dyads at the University of California, San Francisco. Moreover, comprehensive association analysis identified multiple known and new associations between various maternal and neonatal features and specific neonatal outcomes. To date, this is the largest study utilizing linked EHRs from mother-newborn dyads and would serve as an important resource for the investigation and prediction of neonatal outcomes. An interactive website is available for independent investigators to leverage this unique dataset:<jats:ext-link xmlns:xlink="http://www.w3.org/1999/xlink" ext-link-type="uri" xlink:href="https://maternal-child-health-associations.shinyapps.io/shiny_app/">https://maternal-child-health-associations.shinyapps.io/shiny_app/</jats:ext-link>. | ||
| 650 | 4 | |a Biology |7 (dpeaa)DE-84 | |
| 650 | 4 | |a 570 |7 (dpeaa)DE-84 | |
| 700 | 1 | |a Reiss, Jonathan D. |e verfasserin |4 aut | |
| 700 | 1 | |a Roger, Jacquelyn |e verfasserin |4 aut | |
| 700 | 1 | |a Tang, Alice S. |e verfasserin |4 aut | |
| 700 | 1 | |a Chang, Alan L. |e verfasserin |4 aut | |
| 700 | 1 | |a Becker, Martin |e verfasserin |4 aut | |
| 700 | 1 | |a Phongpreecha, Thanaphong |e verfasserin |4 aut | |
| 700 | 1 | |a Espinosa, Camilo |e verfasserin |4 aut | |
| 700 | 1 | |a Morin, Susanna |e verfasserin |4 aut | |
| 700 | 1 | |a Berson, Eloïse |e verfasserin |4 aut | |
| 700 | 1 | |a Thuraiappah, Melan |e verfasserin |4 aut | |
| 700 | 1 | |a Le, Brian L. |e verfasserin |4 aut | |
| 700 | 1 | |a Ravindra, Neal G. |e verfasserin |4 aut | |
| 700 | 1 | |a Payrovnaziri, Seyedeh Neelufar |e verfasserin |4 aut | |
| 700 | 1 | |a Mataraso, Samson |e verfasserin |4 aut | |
| 700 | 1 | |a Kim, Yeasul |e verfasserin |4 aut | |
| 700 | 1 | |a Xue, Lei |e verfasserin |4 aut | |
| 700 | 1 | |a Rosenstein, Melissa |e verfasserin |4 aut | |
| 700 | 1 | |a Oskotsky, Tomiko |e verfasserin |4 aut | |
| 700 | 1 | |a Marić, Ivana |e verfasserin |4 aut | |
| 700 | 1 | |a Gaudilliere, Brice |e verfasserin |4 aut | |
| 700 | 1 | |a Carvalho, Brendan |e verfasserin |4 aut | |
| 700 | 1 | |a Bateman, Brian T. |e verfasserin |4 aut | |
| 700 | 1 | |a Angst, Martin S. |e verfasserin |4 aut | |
| 700 | 1 | |a Prince, Lawrence S. |e verfasserin |4 aut | |
| 700 | 1 | |a Blumenfeld, Yair J. |e verfasserin |4 aut | |
| 700 | 1 | |a Benitz, William E |e verfasserin |4 aut | |
| 700 | 1 | |a Fuerch, Janene H. |e verfasserin |4 aut | |
| 700 | 1 | |a Shaw, Gary M. |e verfasserin |4 aut | |
| 700 | 1 | |a Sylvester, Karl G. |e verfasserin |4 aut | |
| 700 | 1 | |a Stevenson, David K. |e verfasserin |4 aut | |
| 700 | 1 | |a Sirota, Marina |e verfasserin |4 aut | |
| 700 | 1 | |a Aghaeepour, Nima |e verfasserin |4 aut | |
| 773 | 0 | 8 | |i Enthalten in |t bioRxiv.org |g (2023) vom: 31. Jan. |
| 773 | 1 | 8 | |g year:2023 |g day:31 |g month:01 |
| 856 | 4 | 0 | |u http://dx.doi.org/10.1101/2022.03.31.22273233 |z kostenfrei |3 Volltext |
| 912 | |a GBV_XBI | ||
| 912 | |a SSG-OLC-PHA | ||
| 951 | |a AR | ||
| 952 | |j 2023 |b 31 |c 01 | ||