Development of a predictive model for retention in HIV care using natural language processing of clinical notes

Oliwa, Tomasz; Furner, Brian; Schmitt, Jessica; Schneider, John A.; Ridgway, Jessica P

doi:10.1093/jamia/ocaa220

Cited by 13 publications

(5 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Afshar et al [31] used NLP of clinical notes to identify patients with alcohol misuse, demonstrating greater accuracy than EMR-based billing codes; however, this study was performed among hospitalized trauma patients rather than with outpatients living with HIV. Oliwa et al [32] used NLP of clinical notes to identify phrases associated with improved engagement in HIV care. Their study identified NLP phrases related to substance use and mental health among people living with HIV, but they did not compare their findings with documentation in structured EMR fields.…”

Section: Introductionmentioning

confidence: 99%

Natural Language Processing of Clinical Notes to Identify Mental Illness and Substance Use Among People Living with HIV: Retrospective Cohort Study

Ridgway¹,

Uvin²,

Schmitt³

et al. 2021

JMIR Med Inform

Self Cite

View full text Add to dashboard Cite

Background Mental illness and substance use are prevalent among people living with HIV and often lead to poor health outcomes. Electronic medical record (EMR) data are increasingly being utilized for HIV-related clinical research and care, but mental illness and substance use are often underdocumented in structured EMR fields. Natural language processing (NLP) of unstructured text of clinical notes in the EMR may more accurately identify mental illness and substance use among people living with HIV than structured EMR fields alone. Objective The aim of this study was to utilize NLP of clinical notes to detect mental illness and substance use among people living with HIV and to determine how often these factors are documented in structured EMR fields. Methods We collected both structured EMR data (diagnosis codes, social history, Problem List) as well as the unstructured text of clinical HIV care notes for adults living with HIV. We developed NLP algorithms to identify words and phrases associated with mental illness and substance use in the clinical notes. The algorithms were validated based on chart review. We compared numbers of patients with documentation of mental illness or substance use identified by structured EMR fields with those identified by the NLP algorithms. Results The NLP algorithm for detecting mental illness had a positive predictive value (PPV) of 98% and a negative predictive value (NPV) of 98%. The NLP algorithm for detecting substance use had a PPV of 92% and an NPV of 98%. The NLP algorithm for mental illness identified 54.0% (420/778) of patients as having documentation of mental illness in the text of clinical notes. Among the patients with mental illness detected by NLP, 58.6% (246/420) had documentation of mental illness in at least one structured EMR field. Sixty-three patients had documentation of mental illness in structured EMR fields that was not detected by NLP of clinical notes. The NLP algorithm for substance use detected substance use in the text of clinical notes in 18.1% (141/778) of patients. Among patients with substance use detected by NLP, 73.8% (104/141) had documentation of substance use in at least one structured EMR field. Seventy-six patients had documentation of substance use in structured EMR fields that was not detected by NLP of clinical notes. Conclusions Among patients in an urban HIV care clinic, NLP of clinical notes identified high rates of mental illness and substance use that were often not documented in structured EMR fields. This finding has important implications for epidemiologic research and clinical care for people living with HIV.

show abstract

Section: Introductionmentioning

confidence: 99%

Natural Language Processing of Clinical Notes to Identify Mental Illness and Substance Use Among People Living with HIV: Retrospective Cohort Study

Ridgway¹,

Uvin²,

Schmitt³

et al. 2021

JMIR Med Inform

Self Cite

View full text Add to dashboard Cite

show abstract

“…• Oliwa et al [41] devised a system to analyze the presence or absence of lost to follow-up (LTFU) practices by HIV-positive patients. This is necessary because the poor response to appointments may negatively affect the patient's mortality and decrease their quality of life.…”

Section: Miscellaneous Practicesmentioning

confidence: 99%

Demystifying the Role of Natural Language Processing (NLP) in Smart City Applications: Background, Motivation, Recent Advances, and Future Research Directions

Tyagi

Bhushan

2023

Wireless Pers Commun

View full text Add to dashboard Cite

Smart cities provide an efficient infrastructure for the enhancement of the quality of life of the people by aiding in fast urbanization and resource management through sustainable and scalable innovative solutions. The penetration of Information and Communication Technology (ICT) in smart cities has been a major contributor to keeping up with the agility and pace of their development. In this paper, we have explored Natural Language Processing (NLP) which is one such technical discipline that has great potential in optimizing ICT processes and has so far been kept away from the limelight. Through this study, we have established the various roles that NLP plays in building smart cities after thoroughly analyzing its architecture, background, and scope. Subsequently, we present a detailed description of NLP's recent applications in the domain of smart healthcare, smart business, and industry, smart community, smart media, smart research, and development as well as smart education accompanied by NLP's open challenges at the very end. This work aims to throw light on the potential of NLP as one of the pillars in assisting the technical advancement and realization of smart cities.

show abstract

“…ARDSFlag uses machine learning (ML) and natural language processing (NLP) techniques to evaluate Berlin criteria. ML and NLP have been proven to offer strong potential for identifying and predicting complex medical conditions by incorporating EHR data [14][15][16][17] . We also develop a visualization that integrates all components of the Berlin criteria in one graph.…”

Section: Introductionmentioning

confidence: 99%

ARDSFlag: An NLP/Machine Learning Algorithm to Visualize and Detect High-Probability ARDS Admissions Independent of Provider Recognition and Billing Codes

Gandomi

Wu²,

Clement

et al. 2022

Preprint

View full text Add to dashboard Cite

Acute respiratory distress syndrome (ARDS) is a type of respiratory failure characterized by bilateral pulmonary infiltrates that cannot be explained entirely by cardiogenic pulmonary edema. ARDS is the primary cause of mortality in COVID-19 patients and one of the leading causes of morbidity and mortality in ICUs. Despite its significance and prevalence, the detection of ARDS remains highly variable and inconsistent. In this work, we develop a tool to automate the diagnosis of ARDS based on the Berlin definition to increase the accuracy of ARDS detection using electronic health record (EHR) fields. ARDSFlag applies machine learning (ML) and natural language processing (NLP) techniques to evaluate Berlin criteria by incorporating structured and unstructured data. The output is the ARDS diagnosis, onset time, and severity. We have also developed a visualization that helps clinicians efficiently assess ARDS criteria retrospectively and in real time. The method includes separate text classifiers trained using large training sets to find evidence of bilateral infiltrates in radiology reports (accuracy of 91.9%+-0.5%) and heart failure/fluid overload in radiology reports (accuracy 86.1%+-0.5%) and echocardiogram notes (accuracy 98.4%+-0.3%). A holdout set of 300 cases, which was blindly and independently labeled for ARDS by two groups of clinicians, shows that the algorithm generates an overall accuracy of 89.0%, with a specificity of 91.7%, recall of 80.3%, and precision of 75.0%. Compared with two other ARDS identification methods used in the literature, ARDSFlag shows higher performance in all accuracy measures (an increase of 25.5% in overall accuracy, 6.5% in specificity, 44.2% in recall, 31.7% in precision, and 38.20% in F1-score over the best of the two detection methods).

show abstract

Development of a predictive model for retention in HIV care using natural language processing of clinical notes

Cited by 13 publications

References 30 publications

Natural Language Processing of Clinical Notes to Identify Mental Illness and Substance Use Among People Living with HIV: Retrospective Cohort Study

Natural Language Processing of Clinical Notes to Identify Mental Illness and Substance Use Among People Living with HIV: Retrospective Cohort Study

Demystifying the Role of Natural Language Processing (NLP) in Smart City Applications: Background, Motivation, Recent Advances, and Future Research Directions

ARDSFlag: An NLP/Machine Learning Algorithm to Visualize and Detect High-Probability ARDS Admissions Independent of Provider Recognition and Billing Codes

Contact Info

Product

Resources

About