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Data collected by cars on driver performance—combined with machine learning—could detect elderly drivers who will soon develop mild cognitive impairment or dementia.
A new study has found that chemicals that accumulate in the vagina, potentially originating from personal care products, may contribute to preterm birth.
Noémie Elhadad, PhD, will lead the Department of Biomedical Informatics.
- August 1, 2022
Columbia researchers have developed a new algorithm to better identify safety signals of prescribed drugs in different stages of childhood.
- February 22, 2022
By applying artificial intelligence to standard-of-care imaging, Columbia cancer researchers can predict how well immunotherapy will work for patients with melanoma.
- November 11, 2021
A study of more than 500 hospitalized COVID patients found that comorbidities do not necessarily result in the worst outcomes.
- March 16, 2021
A collaboration between Observational Health Data Sciences and Informatics (OHDSI) and Health Level Seven International (HL7) will improve access and sharing of health care data among researchers.
- December 22, 2020
In children with certain autism mutations, the diversity and severity of symptoms are often related to the identity and properties of gene units, called exons, targeted by the mutations.
- December 18, 2020
A study of more than 1 million patients has found no increased risk of COVID-19 diagnosis, hospitalization, or complications for users of two common anti-hypertensive medications.
- December 2, 2020
Researchers at Columbia, UCLA, and Northeastern have begun helping the FDA in its effort to monitor the safety and effectiveness of vaccines, including COVID-19 vaccines, and other biologic products.
- October 27, 2020
Coronaviruses are adept at mimicking human immune proteins called complement, which may allow the viruses to gain a foothold in our bodies and cause disease.
- October 2, 2020
New data mining techniques are uncovering previously hidden adverse drug effects that impact women more than men.
- July 6, 2020
With high precision, a new algorithm predicts which patients treated for traumatic injuries in the emergency department will later develop post-traumatic stress disorder.