Description of a rule-based system for the i2b2 challenge in natural language processing for clinical data.

The Obesity Challenge, sponsored by Informatics for Integrating Biology and the Bedside (i2b2), a National Center for Biomedical Computing, asked participants to build software systems that could "read" a patient's clinical discharge summary and replicate the judgments of physicians in evaluating presence or absence of obesity and 15 comorbidities. The authors describe their methodology and discuss the results of applying Lockheed Martin's rule-based natural language processing (NLP) capability, ClinREAD. We tailored ClinREAD with medical domain expertise to create assigned default judgments based on the most probable results as defined in the ground truth. It then used rules to collect evidence similar to the evidence that the human judges likely relied upon, and applied a logic module to weigh the strength of all evidence collected to arrive at final judgments. The Challenge results suggest that rule-based systems guided by human medical expertise are capable of solving complex problems in machine processing of medical text.

Longitudinal analysis of pain in patients with metastatic prostate cancer using natural language processing of medical record text.

OBJECTIVES: To test the feasibility of using text mining to depict meaningfully the experience of pain in patients with metastatic prostate cancer, to identify novel pain phenotypes, and to propose methods for longitudinal visualization of pain status. MATERIALS AND METHODS: Text from 4409 clinical encounters for 33 men enrolled in a 15-year longitudinal clinical/molecular autopsy study of metastatic prostate cancer (Project to ELIminate lethal CANcer) was subjected to natural language processing (NLP) using Unified Medical Language System-based terms. A four-tiered pain scale was developed, and logistic regression analysis identified factors that correlated with experience of severe pain during each month. RESULTS: NLP identified 6387 pain and 13 827 drug mentions in the text. Graphical displays revealed the pain 'landscape' described in the textual records and confirmed dramatically increasing levels of pain in the last years of life in all but two patients, all of whom died from metastatic cancer. Severe pain was associated with receipt of opioids (OR=6.6, p<0.0001) and palliative radiation (OR=3.4, p=0.0002). Surprisingly, no severe or controlled pain was detected in two of 33 subjects' clinical records. Additionally, the NLP algorithm proved generalizable in an evaluation using a separate data source (889 Informatics for Integrating Biology and the Bedside (i2b2) discharge summaries). DISCUSSION: Patterns in the pain experience, undetectable without the use of NLP to mine the longitudinal clinical record, were consistent with clinical expectations, suggesting that meaningful NLP-based pain status monitoring is feasible. Findings in this initial cohort suggest that 'outlier' pain phenotypes useful for probing the molecular basis of cancer pain may exist. LIMITATIONS: The results are limited by a small cohort size and use of proprietary NLP software. CONCLUSIONS: We have established the feasibility of tracking longitudinal patterns of pain by text mining of free text clinical records. These methods may be useful for monitoring pain management and identifying novel cancer phenotypes.