Fig. 5: Predictive capacity and feature contributions of disease trajectories.
From: A deep learning algorithm to predict risk of pancreatic cancer from disease trajectories
a,c, Distribution of recall (sensitivity) values at the F1 operational point (Methods) as a function of time to cancer (time between the end of a disease trajectory and cancer diagnosis). As expected, recall levels decrease with longer time to cancer, from 8% for cancer occurring about 1 year after assessment to a recall of 4% for cancer occurring about 3 years after assessment (DNPR). This suggests that the model learns not only from symptoms very close to pancreatic cancer but also from longer disease histories, albeit at lower accuracy. a, Danish system (DK), for models trained on all data (no data exclusion). c, US-VA system, for models trained on all data. b,d, Top 10 features that contribute to the cancer prediction in time-to-cancer intervals of 0–6, 6–12, 12–24 and 24–36 months for the Danish (DK) (b) and US-VA (d) systems. The features are sorted by the contribution score (Supplementary Table 5). We used an integrated gradients (IG) method to calculate the contribution score for each input feature for each trajectory and then summed over all trajectories with cancer diagnosis within the indicated time interval.
