Exploration of Artificial Intelligence Use with ARIES in Multiple Myeloma Research

Natural Language Processing Algorithm to Extract Multiple Myeloma Stage From Oncology Notes in the Veterans Affairs Healthcare System

PURPOSE

Stage in multiple myeloma (MM) is an essential measure of disease risk, but its measurement in large databases is often lacking. We aimed to develop and validate a natural language processing (NLP) algorithm to extract oncologists' documentation of stage in the national Veterans Affairs (VA) Healthcare System.

METHODS

Using nationwide electronic health record (EHR) and cancer registry data from the VA Corporate Data Warehouse, we developed and validated a rule-based NLP algorithm to extract oncologist-determined MM stage. To that end, a clinician annotated MM stage within over 5,000 short snippets of clinical notes, and annotated MM stage at MM treatment initiation for 200 patients. These were allocated into snippet- and patient-level development and validation sets. We developed MM stage extraction and roll-up algorithms within the development sets. After the algorithms were finalized, we validated them using standard measures in held-out validation sets.

RESULTS

We developed algorithms for three different MM staging systems that have been in widespread use (Revised International Staging System [R-ISS], International Staging System [ISS], and Durie-Salmon [DS]) and for stage reported without a clearly defined system. Precision and recall were uniformly high for MM stage at the snippet level, ranging from 0.92 to 0.99 for the different MM staging systems. Performance in identifying for MM stage at treatment initiation at the patient level was also excellent, with precision of 0.92, 0.96, 0.90, and 0.86 and recall of 0.99, 0.98, 0.94, and 0.92 for R-ISS, ISS, DS, and unclear stage, respectively.

CONCLUSION

Our MM stage extraction algorithm uses rule-based NLP and data aggregation to accurately measure MM stage documented in oncology notes and pathology reports in VA's national EHR system. It may be adapted to other systems where MM stage is recorded in clinical notes.

Applying Natural Language Processing to Textual Data From Clinical Data Warehouses: Systematic Review

BACKGROUND

In recent years, health data collected during the clinical care process have been often repurposed for secondary use through clinical data warehouses (CDWs), which interconnect disparate data from different sources. A large amount of information of high clinical value is stored in unstructured text format. Natural language processing (NLP), which implements algorithms that can operate on massive unstructured textual data, has the potential to structure the data and make clinical information more accessible.

OBJECTIVE

The aim of this review was to provide an overview of studies applying NLP to textual data from CDWs. It focuses on identifying the (1) NLP tasks applied to data from CDWs and (2) NLP methods used to tackle these tasks.

METHODS

This review was performed according to the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. We searched for relevant articles in 3 bibliographic databases: PubMed, Google Scholar, and ACL Anthology. We reviewed the titles and abstracts and included articles according to the following inclusion criteria: (1) focus on NLP applied to textual data from CDWs, (2) articles published between 1995 and 2021, and (3) written in English.

RESULTS

We identified 1353 articles, of which 194 (14.34%) met the inclusion criteria. Among all identified NLP tasks in the included papers, information extraction from clinical text (112/194, 57.7%) and the identification of patients (51/194, 26.3%) were the most frequent tasks. To address the various tasks, symbolic methods were the most common NLP methods (124/232, 53.4%), showing that some tasks can be partially achieved with classical NLP techniques, such as regular expressions or pattern matching that exploit specialized lexica, such as drug lists and terminologies. Machine learning (70/232, 30.2%) and deep learning (38/232, 16.4%) have been increasingly used in recent years, including the most recent approaches based on transformers. NLP methods were mostly applied to English language data (153/194, 78.9%).

CONCLUSIONS

CDWs are central to the secondary use of clinical texts for research purposes. Although the use of NLP on data from CDWs is growing, there remain challenges in this field, especially with regard to languages other than English. Clinical NLP is an effective strategy for accessing, extracting, and transforming data from CDWs. Information retrieved with NLP can assist in clinical research and have an impact on clinical practice.

Does medication-related osteonecrosis of the jaw affect survival of patients with Multiple Myeloma?: Exploring a large single center database using artificial intelligence

In addition to randomized clinical trials, consideration of Real-World Evidence is necessary for mirroring clinical reality. However, processing such evidence for large numbers of patients often requires considerable time and effort. This is particularly true for rare tumor diseases such as multiple myeloma (MM) or for adverse effects that occur even more rarely. In such cases, artificial intelligence is able to efficiently detect patients with rare conditions. One of these rare adverse events, and the most discussed, following bone protective treatment in MM is medication-related osteonecrosis of the jaw (MRONJ). The association of bone protective treatment to MM outcome has been intensively studied. However, the impact of MRONJ resulting from such treatment on MM prognosis and outcome is poorly understood. In this retrospective study, we therefore investigated the long-term effects of MRONJ. We used natural language processing (NLP) to screen individual data of 2389 MM patients to find 50 out of 52 patients with MRONJ matching our inclusion criteria. To further improve data quality, we then performed propensity score matching. In comparison to MM patients without MRONJ, we found a significantly longer overall survival (median 126 vs. 86 months) despite slightly worse clinical features.

Treatment characteristics among patients with binge-eating disorder: an electronic health records analysis

OBJECTIVES

Treatment for adults diagnosed with binge-eating disorder (BED) includes psychotherapy and/or pharmacotherapy and aims to reduce the frequency of binge-eating episodes and disordered eating, improve metabolic-related issues and reduce weight, and address mood symptoms. Data describing real-world treatment patterns are lacking; therefore, this study aims to characterize real-world treatment patterns among patients with BED.

METHODS

This retrospective study identified adult patients with BED using natural language processing of clinical notes from the Optum electronic health record database from 2009 to 2015. Treatment patterns were examined during the 12 months preceding the BED recognition date and during a follow-up period after BED recognition (1-3 years for most patients).

RESULTS

Among 1042 patients, 384 were categorized as the BED cohort and 658, who met less stringent criteria, were categorized as probable BED. In the BED cohort, mean ± SD age was 45.2 ± 13.4 years and 81.8% were women (probable BED, 45.9 ± 12.8 years, 80.2%). A greater percentage of patients in the BED cohort were prescribed pharmacotherapy (70.6% [probable BED, 66.9%]) than received/discussed psychotherapy (53.1% [probable BED, 39.2%]) at baseline. In the BED cohort, 54.4% of patients were prescribed antidepressants (probable BED, 52.4%), 25.3% stimulants (probable BED, 20.1%), and 34.4% nonspecific psychotherapy (probable BED, 24.6%) at baseline, with no substantive differences observed during follow-up. Low percentages of patients in the BED cohort received/discussed cognitive behavioral therapy at baseline (12.5% [probable BED, 9.0%) or during follow-up (13.0% [probable BED, 8.8%). Among patients with ≥1 psychotherapy visit, the mean ± SD number of visits in the BED cohort was 1.2 ± 5.9 at baseline (probable BED, 1.7 ± 7.3) and 2.2 ± 7.7 during follow-up (probable BED, 2.6 ± 7.7).

CONCLUSION

This cohort of patients with BED was treated more frequently with pharmacotherapy than psychotherapy. These data may help inform strategies for reducing differences between real-world treatment patterns and evidence-based recommendations.

The Urgent Need for Precision Medicine in Cancer and Its Microenvironment: The Paradigmatic Case of Multiple Myeloma

Precision medicine is particularly relevant for cancer and microenvironment deconvolution for therapeutic purposes in hematological and non-hematological malignancies [...].

The Route of the Malignant Plasma Cell in Its Survival Niche: Exploring “Multiple Myelomas”

Growing evidence points to multiple myeloma (MM) and its stromal microenvironment using several mechanisms to subvert effective immune and anti-tumor responses. Recent advances have uncovered the tumor-stromal cell influence in regulating the immune-microenvironment and have envisioned targeting these suppressive pathways to improve therapeutic outcomes. Nevertheless, some subgroups of patients include those with particularly unfavorable prognoses. Biological stratification can be used to categorize patient-, disease- or therapy-related factors, or alternatively, these biological determinants can be included in a dynamic model that customizes a given treatment to a specific patient. Genetic heterogeneity and current knowledge enforce a systematic and comprehensive bench-to-bedside approach. Given the increasing role of cancer stem cells (CSCs) in better characterizing the pathogenesis of solid and hematological malignancies, disease relapse, and drug resistance, identifying and describing CSCs is of paramount importance in the management of MM. Even though the function of CSCs is well-known in other cancer types, their role in MM remains elusive. With this review, we aim to provide an update on MM homing and resilience in the bone marrow micro milieu. These data are particularly interesting for clinicians facing unmet medical needs while designing novel treatment approaches for MM.

Harnessing Real-World Evidence for the Development of Novel Cancer Therapies

Resourcing real-world evidence (RWE) is becoming an increasingly important asset in developing novel therapies for cancer. In this article, an overview of the benefits and challenges of using these data is provided. Through several case examples we highlight future applications and potential.