Language impairment in adults with end-stage liver disease: application of natural language processing towards patient-generated health records

Abstract

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.

Artificial Intelligence and Its Application to Minimal Hepatic Encephalopathy Diagnosis

“…Most interestingly, Dickerson et al used natural language processing (a branch of artificial intelligence used by Amazon's Alexa, Apple's Siri, and Google Assistant), and a paired Wilcoxon signed-rank test to find that patients with MELD ≥ 30 (model for endstage liver disease score) had decreased word length and fewer 6+ letters words before liver transplant compared to after liver transplant. Similarly, the sentence length decreased following liver transplant [56].…”

“…As mentioned above, artificial intelligence can explore such changes in multidimensional data obtained either by the recording of handwriting or speech [56,58]. The first step of the proposed algorithm would be extracting features characterizing the underlying patterns of the speech or handwriting signals using signal processing algorithms.…”

Application of Artificial Intelligence for the Diagnosis and Treatment of Liver Diseases

“…( 61 ) Dickerson et al conducted an interesting study of using NLP to analyze electronic messages from patients with cirrhosis, and suggested that longer sentences with shorter words predicted the presence of HE. ( 62 )…”

Current recommendations regarding evaluation of cognitive functioning in organ transplant candidates

“…The app, developed by Bajaj et al [40,65], has shown good sensitivity and specificity in detecting minimal hepatic encephalopathy compared with established measures. Other recent work has piloted examining National Language Processing from patient communications within their electronic health records in relation to transplant outcomes [66]. Although significantly more work is needed in validating these approaches, they do provide innovative spaces for future research into identifying those at risk for cognitive impairments and associated negative outcomes.…”