End-stage liver disease (ESLD) is associated with cognitive impairment ranging from subtle alterations in attention to overt hepatic encephalopathy that resolves after transplant. Natural language processing (NLP) may provide a useful method to assess cognitive status in this population. We identified 81 liver transplant recipients with ESLD (4/2013-2/2018) who sent at least one patient-to-provider electronic message pre-transplant and post-transplant, and matched them 1:1 to "healthy" controls-who had similar disease, but had not been evaluated for liver transplant-by age, gender, race/ethnicity, and liver disease. Messages written by patients pre-transplant and post-transplant and controls was compared across 19 NLP measures using paired Wilcoxon signed-rank tests. While there was no difference overall in word length, patients with Model for End-Stage Liver Disease Score (MELD) ≥ 30 (n = 31) had decreased word length in pre-transplant messages (3.95 [interquartile range (IQR) 3.79, 4.14]) compared to post-transplant (4.13 [3.96, 4.28], p = 0.01) and controls (4.2 [4.0, 4.4], p = 0.01); there was no difference between post-transplant and controls (p = 0.4). Patients with MELD ≥ 30 had fewer 6+ letter words in pre-transplant messages (19.5% [16.4, 25.9] compared to post-transplant (23.4% [20.0, 26.7] p = 0.02) and controls (25.0% [19.2, 29.4]; p = 0.01). Overall, patients had increased sentence length pre-transplant (12.0 [9.8, 13.7]) compared to post-transplant (11.0 [9.2, 13.3]; p = 0.046); the same was seen for MELD ≥ 30 (12.3 [9.8, 13.7] pre-transplant vs. 10.8 [9.6, 13.0] post-transplant; p = 0.050). Application of NLP to patient-generated messages identified language differences-longer sentences with shorter words-that resolved after transplant. NLP may provide opportunities to detect cognitive impairment in ESLD.
Keywords: Language; Liver diseases; Machine learning; Neurological manifestations.
© The Author(s) 2019.