Connecting and linking neurocognitive, digital phenotyping, physiologic, psychophysical, neuroimaging, genomic, & sensor data with survey data
© The Author(s) 2021..
Combining survey data with alternative data sources (e.g., wearable technology, apps, physiological, ecological monitoring, genomic, neurocognitive assessments, brain imaging, and psychophysical data) to paint a complete biobehavioral picture of trauma patients comes with many complex system challenges and solutions. Starting in emergency departments and incorporating these diverse, broad, and separate data streams presents technical, operational, and logistical challenges but allows for a greater scientific understanding of the long-term effects of trauma. Our manuscript describes incorporating and prospectively linking these multi-dimensional big data elements into a clinical, observational study at US emergency departments with the goal to understand, prevent, and predict adverse posttraumatic neuropsychiatric sequelae (APNS) that affects over 40 million Americans annually. We outline key data-driven system challenges and solutions and investigate eligibility considerations, compliance, and response rate outcomes incorporating these diverse "big data" measures using integrated data-driven cross-discipline system architecture.
| Medienart: |
E-Artikel |
|---|
| Erscheinungsjahr: |
2021 |
|---|---|
| Erschienen: |
2021 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:10 |
|---|---|
| Enthalten in: |
EPJ data science - 10(2021), 1 vom: 16., Seite 9 |
| Sprache: |
Englisch |
|---|
| Beteiligte Personen: |
Knott, Charles E [VerfasserIn] |
|---|
| Links: |
|---|
| Themen: |
Big data |
|---|
| Anmerkungen: |
Date Revised 23.02.2021 published: Print-Electronic Citation Status PubMed-not-MEDLINE |
|---|
| doi: |
10.1140/epjds/s13688-021-00264-z |
|---|
| funding: |
|
|---|---|
| Förderinstitution / Projekttitel: |
|
| PPN (Katalog-ID): |
NLM321729749 |
|---|
| LEADER | 01000naa a22002652 4500 | ||
|---|---|---|---|
| 001 | NLM321729749 | ||
| 003 | DE-627 | ||
| 005 | 20231225180514.0 | ||
| 007 | cr uuu---uuuuu | ||
| 008 | 231225s2021 xx |||||o 00| ||eng c | ||
| 024 | 7 | |a 10.1140/epjds/s13688-021-00264-z |2 doi | |
| 028 | 5 | 2 | |a pubmed24n1072.xml |
| 035 | |a (DE-627)NLM321729749 | ||
| 035 | |a (NLM)33614392 | ||
| 040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
| 041 | |a eng | ||
| 100 | 1 | |a Knott, Charles E |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Connecting and linking neurocognitive, digital phenotyping, physiologic, psychophysical, neuroimaging, genomic, & sensor data with survey data |
| 264 | 1 | |c 2021 | |
| 336 | |a Text |b txt |2 rdacontent | ||
| 337 | |a ƒaComputermedien |b c |2 rdamedia | ||
| 338 | |a ƒa Online-Ressource |b cr |2 rdacarrier | ||
| 500 | |a Date Revised 23.02.2021 | ||
| 500 | |a published: Print-Electronic | ||
| 500 | |a Citation Status PubMed-not-MEDLINE | ||
| 520 | |a © The Author(s) 2021. | ||
| 520 | |a Combining survey data with alternative data sources (e.g., wearable technology, apps, physiological, ecological monitoring, genomic, neurocognitive assessments, brain imaging, and psychophysical data) to paint a complete biobehavioral picture of trauma patients comes with many complex system challenges and solutions. Starting in emergency departments and incorporating these diverse, broad, and separate data streams presents technical, operational, and logistical challenges but allows for a greater scientific understanding of the long-term effects of trauma. Our manuscript describes incorporating and prospectively linking these multi-dimensional big data elements into a clinical, observational study at US emergency departments with the goal to understand, prevent, and predict adverse posttraumatic neuropsychiatric sequelae (APNS) that affects over 40 million Americans annually. We outline key data-driven system challenges and solutions and investigate eligibility considerations, compliance, and response rate outcomes incorporating these diverse "big data" measures using integrated data-driven cross-discipline system architecture | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Big data | |
| 650 | 4 | |a Data-driven | |
| 650 | 4 | |a Digital phenotyping | |
| 650 | 4 | |a Interconnections | |
| 650 | 4 | |a Linkage | |
| 650 | 4 | |a Neurocognitive | |
| 650 | 4 | |a Passive data | |
| 650 | 4 | |a Physiological | |
| 650 | 4 | |a Psychophysical | |
| 650 | 4 | |a Systems | |
| 650 | 4 | |a Wearable technologies | |
| 700 | 1 | |a Gomori, Stephen |e verfasserin |4 aut | |
| 700 | 1 | |a Ngyuen, Mai |e verfasserin |4 aut | |
| 700 | 1 | |a Pedrazzani, Susan |e verfasserin |4 aut | |
| 700 | 1 | |a Sattaluri, Sridevi |e verfasserin |4 aut | |
| 700 | 1 | |a Mierzwa, Frank |e verfasserin |4 aut | |
| 700 | 1 | |a Chantala, Kim |e verfasserin |4 aut | |
| 773 | 0 | 8 | |i Enthalten in |t EPJ data science |d 2015 |g 10(2021), 1 vom: 16., Seite 9 |w (DE-627)NLM262933209 |x 2193-1127 |7 nnns |
| 773 | 1 | 8 | |g volume:10 |g year:2021 |g number:1 |g day:16 |g pages:9 |
| 856 | 4 | 0 | |u http://dx.doi.org/10.1140/epjds/s13688-021-00264-z |3 Volltext |
| 912 | |a GBV_USEFLAG_A | ||
| 912 | |a GBV_NLM | ||
| 951 | |a AR | ||
| 952 | |d 10 |j 2021 |e 1 |b 16 |h 9 | ||