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Background Machine learning tools identify patients with blood counts indicating greater likelihood of colorectal cancer and warranting colonoscopy referral. Aims To validate a machine learning colorectal cancer detection model on a US community-based insured adult population. Methods Eligible colorectal cancer cases (439 females, 461 males) with complete blood counts before diagnosis were identified from Kaiser Permanente Northwest Region’s Tumor Registry. Control patients ( n  = 9108) were randomly selected from KPNW’s population who had no cancers, received at ≥1 blood count, had continuous enrollment from 180 days prior to the blood count through 24 months after the count, and were aged 40–89. For each control, one blood count was randomly selected as the pseudo-colorectal cancer diagnosis date for matching to cases, and assigned a “calendar year” based on the count date. For each calendar year, 18 controls were randomly selected to match the general enrollment’s 10-year age groups and lengths of continuous enrollment. Prediction performance was evaluated by area under the curve, specificity, and odds ratios. Results Area under the receiver operating characteristics curve for detecting colorectal cancer was 0.80 ± 0.01. At 99% specificity, the odds ratio for association of a high-risk detection score with colorectal cancer was 34.7 (95% CI 28.9–40.4). The detection model had the highest accuracy in identifying right-sided colorectal cancers. Conclusions ColonFlag ® identifies individuals with tenfold higher risk of undiagnosed colorectal cancer at curable stages (0/I/II), flags colorectal tumors 180–360 days prior to usual clinical diagnosis, and is more accurate at identifying right-sided (compared to left-sided) colorectal cancers.

Using data from multiple health systems (2009–2010) and the largest sample to date, this study compares health services use among youth with and without an autism spectrum disorder (ASD)—including preventive services not previously studied. To examine these differences, we estimated logistic and count data models, controlling for demographic characteristics, comorbid physical health, and mental health conditions. Results indicated that youth with an ASD had greater health care use in many categories, but were less likely to receive important preventive services including flu shots and other vaccinations. An improved understanding of the overall patterns of health care use among this population could enable health systems to facilitate the receipt of appropriate and effective health care.

The article Early Colorectal Cancer Detected by Machine Learning Model Using Gender, Age, and Complete Blood Count Data, written by Mark C. Hornbrook, Ran Goshen, Eran Choman, Maureen O’Keeffe-Rosetti, Yaron Kinar, Elizabeth G. Liles, and Kristal C. Rust, was originally published Online First without open access