Using Natural Language Processing of Free-Text Radiology Reports to Identify Type 1 Modic Endplate Changes

Huhdanpaa, Hannu; Tan, Wang-Chiew; Rundell, Sean D.; Suri, Pradeep; Chokshi, Falgun H.; Comstock, Bryan A.; Heagerty, Patrick J.; James, Kathryn; Avins, Andrew L.; Nedeljković, Srdjan S.; Nerenz, David R.; Kallmes, David F.; Luetmer, Patrick H.; Sherman, Karen J.; Organ, Nancy; Griffith, Brent; Langlotz, Curtis P.; Carrell, David; Hassanpour, Saeed; Jarvik, Jeffrey G.

doi:10.1007/s10278-017-0013-3

Cited by 34 publications

(25 citation statements)

References 17 publications

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“…Prior work for finding medication mentions has focused on written clinical reports. [47][48][49][50][51] Our error analysis suggests that baseline approaches, which rely on dictionaries, struggle with patient-clinician conversational text because of language like filler words (for example, "aha" and "hmm") matching with abbreviations for medications, and the fact that common conversational words are often used as medication names.…”

Section: Discussionmentioning

confidence: 99%

“…These methods have been used to predict hospital readmissions, [43] future radiology utilizations, [44] and characterization of significance, change, and urgency of clinical findings in medical records. [45][46][47][48][49] As such, we plan to develop a recording system for patients that applies NLP methods to unstructured clinic visit recordings. [50] In this paper, we describe an approach to extract mentions of medication names in transcripts of clinic visit audio recordings.…”

Section: Background and Significancementioning

confidence: 99%

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Natural Language Processing for Automated Annotation of Medication Mentions in Primary Care Visit Conversations

Ganoe

Barr

et al. 2021

Preprint

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Objectives: The objective of this study is to build and evaluate a natural language processing approach to identify medication mentions in primary care visit conversations between patients and physicians. Materials and Methods: Eight clinicians contributed to a dataset of 85 clinic visit transcripts, and ten transcripts were randomly selected from this dataset as a development set. Our approach utilizes Apache cTAKES and Unified Medical Language System (UMLS) controlled vocabulary to generate a list of medication candidates in the transcribed text, and then performs multiple customized filters to exclude common false positives from this list while including some additional common mentions of the supplements and immunizations. Results: Sixty-five transcripts with 1,121 medication mentions were randomly selected as an evaluation set. Our proposed method achieved an F-score of 85.0% for identifying the medication mentions in the test set, significantly outperforming existing medication information extraction systems for medical records with F-scores ranging from 42.9% to 68.9%. Discussion: Our medication information extraction approach for primary care visit conversations showed promising results, extracting about 27% more medication mentions from our evaluation set while eliminating many false positives in comparison to existing baseline systems. We made our approach publicly available on the web as an open-source software. Conclusion: Integration of our annotation system with clinical recording applications has the potential to improve patients' understanding and recall of key information from their clinic visits, and, in turn, to positively impact health outcomes.

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Section: Discussionmentioning

confidence: 99%

Section: Background and Significancementioning

confidence: 99%

Natural Language Processing for Automated Annotation of Medication Mentions in Primary Care Visit Conversations

Ganoe

Barr

et al. 2021

Preprint

Self Cite

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“…We found that DL using a document-level approach was a better method for predicting the prognosis of AIS patients using brain MRI free text reports than using word- or sentence-level approaches. Several NLP-based ML studies have shown good classification performance in differentiating certain disease phenotypes from the corresponding free text MRI reports with a word-level approach alone [ 22 , 23 , 24 , 25 ]. However, the studies did not implement DL algorithms to predict their own targets.…”

Section: Discussionmentioning

confidence: 99%

Prediction of Stroke Outcome Using Natural Language Processing-Based Machine Learning of Radiology Report of Brain MRI

Heo

Kim

Choi

et al. 2020

JPM

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Brain magnetic resonance imaging (MRI) is useful for predicting the outcome of patients with acute ischemic stroke (AIS). Although deep learning (DL) using brain MRI with certain image biomarkers has shown satisfactory results in predicting poor outcomes, no study has assessed the usefulness of natural language processing (NLP)-based machine learning (ML) algorithms using brain MRI free-text reports of AIS patients. Therefore, we aimed to assess whether NLP-based ML algorithms using brain MRI text reports could predict poor outcomes in AIS patients. This study included only English text reports of brain MRIs examined during admission of AIS patients. Poor outcome was defined as a modified Rankin Scale score of 3–6, and the data were captured by trained nurses and physicians. We only included MRI text report of the first MRI scan during the admission. The text dataset was randomly divided into a training and test dataset with a 7:3 ratio. Text was vectorized to word, sentence, and document levels. In the word level approach, which did not consider the sequence of words, and the “bag-of-words” model was used to reflect the number of repetitions of text token. The “sent2vec” method was used in the sensation-level approach considering the sequence of words, and the word embedding was used in the document level approach. In addition to conventional ML algorithms, DL algorithms such as the convolutional neural network (CNN), long short-term memory, and multilayer perceptron were used to predict poor outcomes using 5-fold cross-validation and grid search techniques. The performance of each ML classifier was compared with the area under the receiver operating characteristic (AUROC) curve. Among 1840 subjects with AIS, 645 patients (35.1%) had a poor outcome 3 months after the stroke onset. Random forest was the best classifier (0.782 of AUROC) using a word-level approach. Overall, the document-level approach exhibited better performance than did the word- or sentence-level approaches. Among all the ML classifiers, the multi-CNN algorithm demonstrated the best classification performance (0.805), followed by the CNN (0.799) algorithm. When predicting future clinical outcomes using NLP-based ML of radiology free-text reports of brain MRI, DL algorithms showed superior performance over the other ML algorithms. In particular, the prediction of poor outcomes in document-level NLP DL was improved more by multi-CNN and CNN than by recurrent neural network-based algorithms. NLP-based DL algorithms can be used as an important digital marker for unstructured electronic health record data DL prediction.

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“…70,71 Such structured reports maximize objectivity and reduce variability of prose text most often used in reports and enhancing the link between radiology findings and treatment suggestions. 72 Structured radiology reports may either be automatically generated from unstructured human radiology reports using advanced language processing, 73,74 which does not require any cultural changes in clinical radiologic practice at the institutions, or they could be achieved by the more difficult cultural transition to structured reporting by radiologists. Structured reports generated by the Bionic Radiologist described above would also facilitate the linkage of the results to the wording of evidence-based and personalized treatment recommendations.…”

Section: Impact Of the Bionic Radiologistmentioning

confidence: 99%

The Bionic Radiologist: avoiding blurry pictures and providing greater insights

Dewey

Wilkens

2019

npj Digit. Med.

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Radiology images and reports have long been digitalized. However, the potential of the more than 3.6 billion radiology examinations performed annually worldwide has largely gone unused in the effort to digitally transform health care. The Bionic Radiologist is a concept that combines humanity and digitalization for better health care integration of radiology. At a practical level, this concept will achieve critical goals: (1) testing decisions being made scientifically on the basis of disease probabilities and patient preferences; (2) image analysis done consistently at any time and at any site; and (3) treatment suggestions that are closely linked to imaging results and are seamlessly integrated with other information. The Bionic Radiologist will thus help avoiding missed care opportunities, will provide continuous learning in the work process, and will also allow more time for radiologists’ primary roles: interacting with patients and referring physicians. To achieve that potential, one has to cope with many implementation barriers at both the individual and institutional levels. These include: reluctance to delegate decision making, a possible decrease in image interpretation knowledge and the perception that patient safety and trust are at stake. To facilitate implementation of the Bionic Radiologist the following will be helpful: uncertainty quantifications for suggestions, shared decision making, changes in organizational culture and leadership style, maintained expertise through continuous learning systems for training, and role development of the involved experts. With the support of the Bionic Radiologist , disparities are reduced and the delivery of care is provided in a humane and personalized fashion.

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Using Natural Language Processing of Free-Text Radiology Reports to Identify Type 1 Modic Endplate Changes

Cited by 34 publications

References 17 publications

Natural Language Processing for Automated Annotation of Medication Mentions in Primary Care Visit Conversations

Natural Language Processing for Automated Annotation of Medication Mentions in Primary Care Visit Conversations

Prediction of Stroke Outcome Using Natural Language Processing-Based Machine Learning of Radiology Report of Brain MRI

The Bionic Radiologist: avoiding blurry pictures and providing greater insights

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