Natural Language Processing of Clinical Notes on Chronic Diseases: Systematic Review

Novel approaches that complement and go beyond evidence-based medicine are required in the domain of chronic diseases, given the growing incidence of such conditions on the worldwide population. A promising avenue is the secondary use of electronic health records (EHRs), where patient data are analyzed to conduct clinical and translational research. Methods based on machine learning to process EHRs are resulting in improved understanding of patient clinical trajectories and chronic disease risk prediction, creating a unique opportunity to derive previously unknown clinical insights. However, a wealth of clinical histories remains locked behind clinical narratives in free-form text. Consequently, unlocking the full potential of EHR data is contingent on the development of natural language processing (NLP) methods to automatically transform clinical text into structured clinical data that can guide clinical decisions and potentially delay or prevent disease onset

Infected chronic ischemic wound topically treated with a multi-strain probiotic formulation: A novel tailored treatment strategy

Background A wide debate is ongoing regarding the role of cutaneous dysbiosis in the pathogenesis and evolution of difficult-to-treat chronic wounds. Nowadays, probiotic treatment considered as an useful tool to counteract dysbiosis but the evidence in regard to their therapeutic use in the setting of difficult-to-treat cutaneous ulcers is still poor. Aim: clinical report An 83-year-old woman suffering a critical limb ischemia and an infected difficult-to-treat ulcerated cutaneous lesion of the right leg, was complementary treated with local application of a mixture of probiotic bacteria. Methods Microbiological and metabolomic analysis were conducted on wound swabs obtained before and after bacteriotherapy. Results During the treatment course, a progressive healing of the lesion was observed with microbiological resolution of the polymicrobial infection of the wound. Metabolomic analysis showed a significant difference in the local concentration of propionate, 2-hydroxyisovalerate, 2-oxoisocaproate, 2,3-butanediol, putrescine, thymine, and trimethylamine before and after bacteriotherapy. Conclusion The microbiological and metabolomic results seem to confirm the usefulness of complementary probiotic treatment in difficult-to-treat infected wounds. Further investigations are needed to confirm these preliminary findings

Infected chronic ischemic wound topically treated with a multi-strain probiotic formulation: A novel tailored treatment strategy

Background A wide debate is ongoing regarding the role of cutaneous dysbiosis in the pathogenesis and evolution of difficult-to-treat chronic wounds. Nowadays, probiotic treatment considered as an useful tool to counteract dysbiosis but the evidence in regard to their therapeutic use in the setting of difficult-to-treat cutaneous ulcers is still poor. Aim: clinical report An 83-year-old woman suffering a critical limb ischemia and an infected difficult-to-treat ulcerated cutaneous lesion of the right leg, was complementary treated with local application of a mixture of probiotic bacteria. Methods Microbiological and metabolomic analysis were conducted on wound swabs obtained before and after bacteriotherapy. Results During the treatment course, a progressive healing of the lesion was observed with microbiological resolution of the polymicrobial infection of the wound. Metabolomic analysis showed a significant difference in the local concentration of propionate, 2-hydroxyisovalerate, 2-oxoisocaproate, 2,3-butanediol, putrescine, thymine, and trimethylamine before and after bacteriotherapy. Conclusion The microbiological and metabolomic results seem to confirm the usefulness of complementary probiotic treatment in difficult-to-treat infected wounds. Further investigations are needed to confirm these preliminary findings

Int J Med Inform

Objective:Electronic health record (EHR) systems contain structured data (such as diagnostic codes) and unstructured data (clinical documentation). Clinical insights can be derived from analyzing both. The use of natural language processing (NLP) algorithms to effectively analyze unstructured data has been well demonstrated. Here we examine the utility of NLP for the identification of patients with non-alcoholic fatty liver disease, assess patterns of disease progression, and identify gaps in care related to breakdown in communication among providers.Materials and Methods:All clinical notes available on the 38,575 patients enrolled in the Mount Sinai BioMe cohort were loaded into the NLP system. We compared analysis of structured and unstructured EHR data using NLP, free-text search, and diagnostic codes with validation against expert adjudication. We then used the NLP findings to measure physician impression of progression from early-stage NAFLD to NASH or cirrhosis. Similarly, we used the same NLP findings to identify mentions of NAFLD in radiology reports that did not persist into clinical notes.Results:Out of 38,575 patients, we identified 2,281 patients with NAFLD. From the remainder, 10,653 patients with similar data density were selected as a control group. NLP outperformed ICD and text search in both sensitivity (NLP: 0.93, ICD: 0.28, text search: 0.81) and F2 score (NLP: 0.92, ICD: 0.34, text search: 0.81). Of 2281 NAFLD patients, 673 (29.5%) were believed to have progressed to NASH or cirrhosis. Among 176 where NAFLD was noted prior to NASH, the average progression time was 410 days. 619 (27.1%) NAFLD patients had it documented only in radiology notes and not acknowledged in other forms of clinical documentation. Of these, 170 (28.4%) were later identified as having likely developed NASH or cirrhosis after a median 1057.3 days.Discussion:NLP-based approaches were more accurate at identifying NAFLD within the EHR than ICD/text search-based approaches. Suspected NAFLD on imaging is often not acknowledged in subsequent clinical documentation. Many such patients are later found to have more advanced liver disease. Analysis of information flows demonstrated loss of key information that could have been used to help prevent the progression of early NAFLD (NAFL) to NASH or cirrhosis.Conclusion:For identification of NAFLD, NLP performed better than alternative selection modalities and facilitated. It then facilitated analysis of knowledge flow between physician and enabled the identification of breakdowns where key information was lost that could have slowed or prevented later disease progression.20192020-09-01T00:00:00ZR01 HL085757/HL/NHLBI NIH HHS/United StatesU01 HG009610/HG/NHGRI NIH HHS/United StatesU01 DK106962/DK/NIDDK NIH HHS/United StatesR01 DK096549/DK/NIDDK NIH HHS/United StatesU01 HG007278/HG/NHGRI NIH HHS/United StatesR01 GM106085/GM/NIGMS NIH HHS/United StatesR01 DK108803/DK/NIDDK NIH HHS/United StatesT32 DK007757/DK/NIDDK NIH HHS/United StatesU01 OH011326/OH/NIOSH CDC HHS/United StatesU01 DK116100/DK/NIDDK NIH HHS/United StatesR01 DK112258/DK/NIDDK NIH HHS/United StatesK23 DK107908/DK/NIDDK NIH HHS/United States31445275PMC6717556841

Evaluation of Natural Language Processing for the Identification of Crohn Disease-Related Variables in Spanish Electronic Health Records:A Validation Study for the PREMONITION-CD Project

Background: The exploration of clinically relevant information in the free text of electronic health records (EHRs) holds the potential to positively impact clinical practice as well as knowledge regarding Crohn disease (CD), an inflammatory bowel disease that may affect any segment of the gastrointestinal tract. The EHRead technology, a clinical natural language processing (cNLP) system, was designed to detect and extract clinical information from narratives in the clinical notes contained in EHRs. Objective: The aim of this study is to validate the performance of the EHRead technology in identifying information of patients with CD. Methods: We used the EHRead technology to explore and extract CD-related clinical information from EHRs. To validate this tool, we compared the output of the EHRead technology with a manually curated gold standard to assess the quality of our cNLP system in detecting records containing any reference to CD and its related variables. Results: The validation metrics for the main variable (CD) were a precision of 0.88, a recall of 0.98, and an F1 score of 0.93. Regarding the secondary variables, we obtained a precision of 0.91, a recall of 0.71, and an F1 score of 0.80 for CD flare, while for the variable vedolizumab (treatment), a precision, recall, and F1 score of 0.86, 0.94, and 0.90 were obtained, respectively. Conclusions: This evaluation demonstrates the ability of the EHRead technology to identify patients with CD and their related variables from the free text of EHRs. To the best of our knowledge, this study is the first to use a cNLP system for the identification of CD in EHRs written in Spanish. © 2022 JMIR Medical Informatics. All rights reserved

Future Directions for Cardiovascular Disease Comparative Effectiveness Research Report of a Workshop Sponsored by the National Heart, Lung, and Blood Institute

Comparative effectiveness research (CER) aims to provide decision makers with the evidence needed to evaluate the benefits and harms of alternative clinical management strategies. CER has become a national priority, with considerable new research funding allocated. Cardiovascular disease is a priority area for CER. This workshop report provides an overview of CER methods, with an emphasis on practical clinical trials and observational treatment comparisons. The report also details recommendations to the National Heart, Lung, and Blood Institute for a new framework for evidence development to foster cardiovascular CER, and specific studies to address 8 clinical issues identified by the Institute of Medicine as high priorities for cardiovascular CER

SemClinBr -- a multi institutional and multi specialty semantically annotated corpus for Portuguese clinical NLP tasks

The high volume of research focusing on extracting patient's information from electronic health records (EHR) has led to an increase in the demand for annotated corpora, which are a very valuable resource for both the development and evaluation of natural language processing (NLP) algorithms. The absence of a multi-purpose clinical corpus outside the scope of the English language, especially in Brazilian Portuguese, is glaring and severely impacts scientific progress in the biomedical NLP field. In this study, we developed a semantically annotated corpus using clinical texts from multiple medical specialties, document types, and institutions. We present the following: (1) a survey listing common aspects and lessons learned from previous research, (2) a fine-grained annotation schema which could be replicated and guide other annotation initiatives, (3) a web-based annotation tool focusing on an annotation suggestion feature, and (4) both intrinsic and extrinsic evaluation of the annotations. The result of this work is the SemClinBr, a corpus that has 1,000 clinical notes, labeled with 65,117 entities and 11,263 relations, and can support a variety of clinical NLP tasks and boost the EHR's secondary use for the Portuguese language