Suicide is a leading cause of death in the US, particularly for young people, veterans and Native Americans. An early warning system can be an effective way to identify those in need before a crisis is upon them. See how AI and machine learning on longitudinal health records can be used to identify those at risk – up to 60+ times as likely to attempt a suicide in a future 1 year period.
BACKGROUND: Desmoid tumours are locally aggressive tumours associated with substantial morbidity. No systemic treatments are approved for this disease, with methotrexate-vinblastine the only chemotherapy regimen assessed in a clinical trial setting to date. VEGF overexpression is a common feature in aggressive desmoid tumours.
Human protein biomarker discovery relies heavily on pre-clinical models, in particular established cell lines and patient-derived xenografts, but confirmation studies in primary tissue are essential to demonstrate clinical relevance. We describe in this study the process that was followed to clinically translate a 5-protein response signature predictive for the activity of an anti-HER3 monoclonal
Most KRAS G12C mutant non-small cell lung cancer (NSCLC) patients experience clinical benefit from selective KRAS G12C inhibition, while patients with colorectal cancer (CRC) bearing the same mutation rarely respond. To investigate the cause of the limited efficacy of KRAS G12C inhibitors in CRC, we examined the effects of AMG510 in KRAS G12C CRC cell lines. Unlike NSCLC cell lines, KRAS G12C CRC
PURPOSE: Understanding the molecular landscape of glioblastoma (GBM) is increasingly important in the age of targeted therapy. O-6-Methylguanine-DNA methyltransferase (MGMT) promoter methylation and EGFR amplification are markers that may play a role in prognostication, treatment, and/or clinical trial eligibility. Quantification of MGMT and EGFR protein expression may offer an alternative stra
Lung cancer is the leading cause of cancer death worldwide. Early detection of individuals at risk of lung cancer is critical to reduce the mortality rate. Using data from individual patient electronic health records (EHR’s), we retrospectively developed and prospectively validated an accurate risk prediction model of new incident lung cancer occurring in the next 1 year. Through statistical lear
Falls in our elder population are a major cause of injury, hospitalization and mortality worldwide, and a major source of medical costs. Current risk screening and assessment methods are often manual, include a limited number of intrinsic factors, exclude environmental factors and/or lack discriminatory capabilities that a robust machine-learned model can provide. See how machine learning can prov
Protein biomarkers are widely used in cancer diagnosis, prognosis, and prediction of treatment response. Here we introduce the use of targeted multiplex proteomics (TMP) as a tool to simultaneously measure a panel of 54 proteins involved in oncogenic, tumour suppression, drug metabolism and resistance, in patients with metastatic colorectal cancer (mCRC). TMP provided valuable diagnostic informati
The rapid deterioration observed in the condition of some hospitalized patients can be attributed to either disease progression or imperfect triage and level of care assignment after their admission. In this study, data collected from a system-wide electronic medical record (EMR) were exposed to multiple machine learning methods. The prospectively validated algorithm scored patients’ daily and lon