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

Heintzelman NH, Taylor RJ, Simonsen L, Lustig R, Anderko D, Haythornthwaite JA, Childs LC, Bova GS - J Am Med Inform Assoc (2012)

Bottom Line: Severe pain was associated with receipt of opioids (OR=6.6, p<0.0001) and palliative radiation (OR=3.4, p=0.0002).The results are limited by a small cohort size and use of proprietary NLP software.We have established the feasibility of tracking longitudinal patterns of pain by text mining of free text clinical records.

View Article: PubMed Central - PubMed

Affiliation: Information Systems and Global Solutions, Lockheed Martin Corporation, Valley Forge, Pennsylvania, USA.

ABSTRACT

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.

Show MeSH

Related in: MedlinePlus

Natural language processing algorithm. CUI, concept unique identifier; UMLS, Unified Medical Language System.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC3756253&req=5

AMIAJNL2012001076F1: Natural language processing algorithm. CUI, concept unique identifier; UMLS, Unified Medical Language System.

Mentions: We combined the vocabulary terms with context patterns in order to recognize internal dates, negatives, conditionals, and pain severity. These context patterns were developed manually. ClinREAD, like MedLEE,33 is rule-based. Each clinical concept (‘sign or symptom’, ‘finding’, ‘injury or poisoning’, ‘disease or syndrome’, or ‘neoplastic process’) is associated with a date and a body location; see online appendix for further detail. The system resolved incomplete dates (eg, ‘in July’) based on the date of the encounter, and resolved relative dates (eg, ‘four days prior to admission’) based on the previous date mention. Each resolved date is represented as a range (startdate, enddate). This date resolution component was based on the development team's previous work34–37 and is described in the online appendix. When dates were missing, the date of the clinical encounter was used as the default. Date associations were used to normalize the clinical concept to the number of days before death, for each individual study subject. This calculation is enabled through the conversion of the midpoint of absolute date ranges to the modified Julian format.38 Each mention of pain was associated with a severity level from the four-tiered pain scale. A subset of 637 strings from semantic type ‘sign or symptom’ were identified as indicating pain, listed in online appendix table 5. The NLP algorithm used for the study is summarized in figure 1 and as follows.


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

Heintzelman NH, Taylor RJ, Simonsen L, Lustig R, Anderko D, Haythornthwaite JA, Childs LC, Bova GS - J Am Med Inform Assoc (2012)

Natural language processing algorithm. CUI, concept unique identifier; UMLS, Unified Medical Language System.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3756253&req=5

AMIAJNL2012001076F1: Natural language processing algorithm. CUI, concept unique identifier; UMLS, Unified Medical Language System.
Mentions: We combined the vocabulary terms with context patterns in order to recognize internal dates, negatives, conditionals, and pain severity. These context patterns were developed manually. ClinREAD, like MedLEE,33 is rule-based. Each clinical concept (‘sign or symptom’, ‘finding’, ‘injury or poisoning’, ‘disease or syndrome’, or ‘neoplastic process’) is associated with a date and a body location; see online appendix for further detail. The system resolved incomplete dates (eg, ‘in July’) based on the date of the encounter, and resolved relative dates (eg, ‘four days prior to admission’) based on the previous date mention. Each resolved date is represented as a range (startdate, enddate). This date resolution component was based on the development team's previous work34–37 and is described in the online appendix. When dates were missing, the date of the clinical encounter was used as the default. Date associations were used to normalize the clinical concept to the number of days before death, for each individual study subject. This calculation is enabled through the conversion of the midpoint of absolute date ranges to the modified Julian format.38 Each mention of pain was associated with a severity level from the four-tiered pain scale. A subset of 637 strings from semantic type ‘sign or symptom’ were identified as indicating pain, listed in online appendix table 5. The NLP algorithm used for the study is summarized in figure 1 and as follows.

Bottom Line: Severe pain was associated with receipt of opioids (OR=6.6, p<0.0001) and palliative radiation (OR=3.4, p=0.0002).The results are limited by a small cohort size and use of proprietary NLP software.We have established the feasibility of tracking longitudinal patterns of pain by text mining of free text clinical records.

View Article: PubMed Central - PubMed

Affiliation: Information Systems and Global Solutions, Lockheed Martin Corporation, Valley Forge, Pennsylvania, USA.

ABSTRACT

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.

Show MeSH
Related in: MedlinePlus