Machine Learning to Predict Mortality and Critical Events in a Cohort of Patients With COVID-19 in New York City: Model Development and Validation.
Akhil VaidSulaiman S SomaniAdam J RussakJessica K De FreitasFayzan F ChaudhryIshan ParanjpeKipp W JohnsonSamuel J LeeRiccardo MiottoFelix RichterShan P ZhaoNoam D BeckmannNidhi NaikArash KiaPrem TimsinaAnuradha LalaManish D ParanjpeEddye A GoldenMatteo DanielettoManbir SinghDara MeyerPaul F O'ReillyLaura M HuckinsPatricia KovatchJoseph FinkelsteinRobert M FreemanEdgar ArgulianAndrew KasarskisBethany L PerchaJudith A AbergEmilia BagiellaCarol R HorowitzBarbara MurphyEric J NestlerEric E SchadtJudy H ChoCarlos Cordon-CardoValentin FusterDennis S CharneyDavid L ReichErwin P BöttingerMatthew A LevinJagat NarulaZahi Adel FayadAllan Carpenter JustAlexander W CharneyGirish Nitin NadkarniBenjamin Scott GlicksbergPublished in: Journal of medical Internet research (2020)
We externally and prospectively trained and validated machine learning models for mortality and critical events for patients with COVID-19 at different time horizons. These models identified at-risk patients and uncovered underlying relationships that predicted outcomes.