Natural Language Processing to Assess Frequency of Functional Status Documentation for Patients Newly Diagnosed With Colorectal Cancer

Background: Invaluable information on patient functioning and the complex interactions that define it is recorded in free text portions of the Electronic Health Record (EHR). Leveraging this information to improve clinical decision-making and conduct research requires natural language processing (NLP) technologies to identify and organize the information recorded in clinical documentation.Methods: We used natural language processing methods to analyze information about patient functioning recorded in two collections of clinical documents pertaining to claims for federal disability benefits from the U.S. Social Security Administration (SSA). We grounded our analysis in the International Classification of Functioning, Disability, and Health (ICF), and used the Activities and Participation domain of the ICF to classify information about functioning in three key areas: mobility, self-care, and domestic life. After annotating functional status information in our datasets through expert clinical review, we trained machine learning-based NLP models to automatically assign ICF categories to mentions of functional activity.Results: We found that rich and diverse information on patient functioning was documented in the free text records. Annotation of 289 documents for Mobility information yielded 2,455 mentions of Mobility activities and 3,176 specific actions corresponding to 13 ICF-based categories. Annotation of 329 documents for Self-Care and Domestic Life information yielded 3,990 activity mentions and 4,665 specific actions corresponding to 16 ICF-based categories. NLP systems for automated ICF coding achieved over 80% macro-averaged F-measure on both datasets, indicating strong performance across all ICF categories used.Conclusions: Natural language processing can help to navigate the tradeoff between flexible and expressive clinical documentation of functioning and standardizable data for comparability and learning. The ICF has practical limitations for classifying functional status information in clinical documentation but presents a valuable framework for organizing the information recorded in health records about patient functioning. This study advances the development of robust, ICF-based NLP technologies to analyze information on patient functioning and has significant implications for NLP-powered analysis of functional status information in disability benefits management, clinical care, and research.

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Natural Language Processing for the Accurate Identification of Colorectal Cancer Mismatch Repair Status in Lynch Syndrome Screening

Clinical Gastroenterology and Hepatology ◽

10.1016/j.cgh.2020.01.040 ◽

2020 ◽

Author(s):

Dan Li ◽

Natalia Udaltsova ◽

Evan Layefsky ◽

Cecilia Doan ◽

Douglas A. Corley

Keyword(s):

Colorectal Cancer ◽

Natural Language Processing ◽

Lynch Syndrome ◽

Natural Language ◽

Mismatch Repair ◽

Language Processing ◽

Accurate Identification ◽

Mismatch Repair Status

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Linking Free Text Documentation of Functioning and Disability to the ICF with Natural Language Processing

10.1101/2021.08.19.21262308 ◽

2021 ◽

Author(s):

Denis R Newman-Griffis ◽

Jonathan Camacho Maldonado ◽

Pei-Shu Ho ◽

Maryanne Sacco ◽

Rafael Jimenez Silva ◽

...

Keyword(s):

Natural Language Processing ◽

Natural Language ◽

Functional Status ◽

Language Processing ◽

Free Text ◽

Clinical Documentation ◽

Disability Benefits ◽

Domestic Life ◽

Patient Functioning ◽

Status Information

Background: Invaluable information on patient functioning and the complex interactions that define it is recorded in free text portions of the Electronic Health Record (EHR). Leveraging this information to improve clinical decision-making and conduct research requires natural language processing (NLP) technologies to identify and organize the information recorded in clinical documentation. Methods: We used NLP methods to analyze information about patient functioning recorded in two collections of clinical documents pertaining to claims for federal disability benefits from the U.S. Social Security Administration (SSA). We grounded our analysis in the International Classification of Functioning, Disability and Health (ICF), and used the ICF's Activities and Participation domain to classify information about functioning in three key areas: Mobility, Self-Care, and Domestic Life. After annotating functional status information in our datasets through expert clinical review, we trained machine learning-based NLP models to automatically assign ICF codes to mentions of functional activity. Results: We found that rich and diverse information on patient functioning was documented in the free text records. Annotation of 289 documents for Mobility information yielded 2,455 mentions of Mobility activities and 3,176 specific actions corresponding to 13 ICF-based codes. Annotation of 329 documents for Self-Care and Domestic Life information yielded 3,990 activity mentions and 4,665 specific actions corresponding to 16 ICF-based codes. NLP systems for automated ICF coding achieved over 80% macro-averaged F-measure on both datasets, indicating strong performance across all ICF codes used. Conclusions: NLP can help to navigate the tradeoff between flexible and expressive clinical documentation of functioning and standardizable data for comparability and learning. The ICF has practical limitations for classifying functional status information in clinical documentation, but presents a valuable framework for organizing the information recorded in health records about patient functioning. This study advances the development of robust, ICF-based NLP technologies to analyze information on patient functioning, and has significant implications for NLP-powered analysis of functional status information in disability benefits management, clinical care, and research.

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Natural Language Processing Improves Identification of Colorectal Cancer Testing in the Electronic Medical Record

Medical Decision Making ◽

10.1177/0272989x11400418 ◽

2011 ◽

Vol 32 (1) ◽

pp. 188-197 ◽

Cited By ~ 33

Author(s):

Joshua C. Denny ◽

Neesha N. Choma ◽

Josh F. Peterson ◽

Randolph A. Miller ◽

Lisa Bastarache ◽

...

Keyword(s):

Colorectal Cancer ◽

Natural Language Processing ◽

Natural Language ◽

Medical Record ◽

Electronic Medical Record ◽

Language Processing

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Natural Language Processing of Large-Scale Structured Radiology Reports to Identify Oncologic Patients With or Without Splenomegaly Over a 10-Year Period

JCO Clinical Cancer Informatics ◽

10.1200/cci.21.00104 ◽

2022 ◽

Author(s):

Simon Sun ◽

Kaelan Lupton ◽

Karen Batch ◽

Huy Nguyen ◽

Lior Gazit ◽

...

Keyword(s):

Colorectal Cancer ◽

Computed Tomography ◽

Natural Language Processing ◽

Natural Language ◽

Language Processing ◽

Entire Cohort ◽

Radiology Reports ◽

Increased Risk ◽

Oncologic Patients ◽

Validation Set

PURPOSE To assess the accuracy of a natural language processing (NLP) model in extracting splenomegaly described in patients with cancer in structured computed tomography radiology reports. METHODS In this retrospective study between July 2009 and April 2019, 3,87,359 consecutive structured radiology reports for computed tomography scans of the chest, abdomen, and pelvis from 91,665 patients spanning 30 types of cancer were included. A randomized sample of 2,022 reports from patients with colorectal cancer, hepatobiliary cancer (HB), leukemia, Hodgkin lymphoma (HL), and non-HL patients was manually annotated as positive or negative for splenomegaly. NLP model training/testing was performed on 1,617/405 reports, and a new validation set of 400 reports from all cancer subtypes was used to test NLP model accuracy, precision, and recall. Overall survival was compared between the patient groups (with and without splenomegaly) using Kaplan-Meier curves. RESULTS The final cohort included 3,87,359 reports from 91,665 patients (mean age 60.8 years; 51.2% women). In the testing set, the model achieved accuracy of 92.1%, precision of 92.2%, and recall of 92.1% for splenomegaly. In the validation set, accuracy, precision, and recall were 93.8%, 92.9%, and 86.7%, respectively. In the entire cohort, splenomegaly was most frequent in patients with leukemia (32.5%), HB (17.4%), non-HL (9.1%), colorectal cancer (8.5%), and HL (5.6%). A splenomegaly label was associated with an increased risk of mortality in the entire cohort (hazard ratio 2.10; 95% CI, 1.98 to 2.22; P < .001). CONCLUSION Automated splenomegaly labeling by NLP of radiology report demonstrates good accuracy, precision, and recall. Splenomegaly is most frequently reported in patients with leukemia, followed by patients with HB.

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