An introduction to machine learning for clinicians: How can machine learning augment knowledge in geriatric oncology?

Accuracy of machine learning to predict the outcomes of shoulder arthroplasty: a systematic review

BACKGROUND

Artificial intelligence (AI) uses computer systems to simulate cognitive capacities to accomplish goals like problem-solving and decision-making. Machine learning (ML), a branch of AI, makes algorithms find connections between preset variables, thereby producing prediction models. ML can aid shoulder surgeons in determining which patients may be susceptible to worse outcomes and complications following shoulder arthroplasty (SA) and align patient expectations following SA. However, limited literature is available on ML utilization in total shoulder arthroplasty (TSA) and reverse TSA.

METHODS

A systematic literature review in accordance with PRISMA guidelines was performed to identify primary research articles evaluating ML's ability to predict SA outcomes. With duplicates removed, the initial query yielded 327 articles, and after applying inclusion and exclusion criteria, 12 articles that had at least 1 month follow-up time were included.

RESULTS

ML can predict 30-day postoperative complications with a 90% accuracy, postoperative range of motion with a higher-than-85% accuracy, and clinical improvement in patient-reported outcome measures above minimal clinically important differences with a 93%-99% accuracy. ML can predict length of stay, operative time, discharge disposition, and hospitalization costs.

CONCLUSION

ML can accurately predict outcomes and complications following SA and healthcare utilization. Outcomes are highly dependent on the type of algorithms used, data input, and features selected for the model.

LEVEL OF EVIDENCE

III.

Supervised learning applied to classifying fallers versus non-fallers among older adults with cancer

INTRODUCTION

Supervised machine learning approaches are increasingly used to analyze clinical data, including in geriatric oncology. This study presents a machine learning approach to understand falls in a cohort of older adults with advanced cancer starting chemotherapy, including fall prediction and identification of contributing factors.

MATERIALS AND METHODS

This secondary analysis of prospectively collected data from the GAP 70+ Trial (NCT02054741; PI: Mohile) enrolled patients aged ≥70 with advanced cancer and ≥ 1 geriatric assessment domain impairment who planned to start a new cancer treatment regimen. Of ≥2000 baseline variables ("features") collected, 73 were selected based on clinical judgment. Machine learning models to predict falls at three months were developed, optimized, and tested using data from 522 patients. A custom data preprocessing pipeline was implemented to prepare data for analysis. Both undersampling and oversampling techniques were applied to balance the outcome measure. Ensemble feature selection was applied to identify and select the most relevant features. Four models (logistic regression [LR], k-nearest neighbor [kNN], random forest [RF], and MultiLayer Perceptron [MLP]) were trained and subsequently tested on a holdout set. Receiver operating characteristic (ROC) curves were generated and area under the curve (AUC) was calculated for each model. SHapley Additive exPlanations (SHAP) values were utilized to further understand individual feature contributions to observed predictions.

RESULTS

Based on the ensemble feature selection algorithm, the top eight features were selected for inclusion in the final models. Selected features aligned with clinical intuition and prior literature. The LR, kNN, and RF models performed equivalently well in predicting falls in the test set, with AUC values 0.66-0.67, and the MLP model showed AUC 0.75. Ensemble feature selection resulted in improved AUC values compared to using LASSO alone. SHAP values, a model-agnostic technique, revealed logical associations between selected features and model predictions.

DISCUSSION

Machine learning techniques can augment hypothesis-driven research, including in older adults for whom randomized trial data are limited. Interpretable machine learning is particularly important, as understanding which features impact predictions is a critical aspect of decision-making and intervention. Clinicians should understand the philosophy, strengths, and limitations of a machine learning approach applied to patient data.

An Unsupervised Machine Learning Approach to Evaluating the Association of Symptom Clusters With Adverse Outcomes Among Older Adults With Advanced Cancer: A Secondary Analysis of a Randomized Clinical Trial

Importance

Older adults with advanced cancer who have high pretreatment symptom severity often experience adverse events during cancer treatments. Unsupervised machine learning may help stratify patients into different risk groups.

Objective

To evaluate whether clusters identified from baseline patient-reported symptom severity were associated with adverse outcomes.

Design, Setting, and Participants

This secondary analysis of the Geriatric Assessment Intervention for Reducing Toxicity in Older Patients With Advanced Cancer (GAP70+) Trial (2014-2019) included patients who completed the National Cancer Institute Patient-Reported Outcomes version of the Common Terminology Criteria for Adverse Events (PRO-CTCAE) before starting a new cancer treatment regimen and received care at community oncology sites across the United States. An unsupervised machine learning algorithm (k-means with Euclidean distance) clustered patients based on similarities of baseline symptom severities. Clustering variables included severity items of 24 PRO-CTCAE symptoms (range, 0-4; corresponding to none, mild, moderate, severe, and very severe). Total severity score was calculated as the sum of 24 items (range, 0-96). Whether the clusters were associated with unplanned hospitalization, death, and toxic effects was then examined. Analyses were conducted in January and February 2022.

Exposures

Symptom severity.

Main Outcomes and Measures

Unplanned hospitalization over 3 months (primary), all-cause mortality over 1 year, and any clinician-rated grade 3 to 5 toxic effect over 3 months.

Results

Of 718 enrolled patients, 706 completed baseline PRO-CTCAE and were included (mean [SD] age, 77.2 [5.5] years, 401 [56.8%] male patients; 51 [7.2%] Black and 619 [87.8%] non-Hispanic White patients; 245 [34.7%] with gastrointestinal cancer; 175 [24.8%] with lung cancer; mean [SD] impaired Geriatric Assessment domains, 4.5 [1.6]). The algorithm classified 310 (43.9%), 295 (41.8%), and 101 (14.3%) into low-, medium-, and high-severity clusters (within-cluster mean [SD] severity scores: low, 6.3 [3.4]; moderate, 16.6 [4.3]; high, 29.8 [7.8]; P < .001). Controlling for sociodemographic variables, clinical factors, study group, and practice site, compared with patients in the low-severity cluster, those in the moderate-severity cluster were more likely to experience hospitalization (risk ratio, 1.36; 95% CI, 1.01-1.84; P = .046). Moderate- and high-severity clusters were associated with a higher risk of death (moderate: hazard ratio, 1.31; 95% CI, 1.01-1.69; P = .04; high: hazard ratio, 2.00; 95% CI, 1.43-2.78; P < .001), but not toxic effects.

Conclusions and Relevance

In this study, unsupervised machine learning partitioned patients into distinct symptom severity clusters; patients with higher pretreatment severity were more likely to experience hospitalization and death.

Trial Registration

ClinicalTrials.gov Identifier: NCT02054741.

Associating persistent self-reported cognitive decline with neurocognitive decline in older breast cancer survivors using machine learning: The Thinking and Living with Cancer study

INTRODUCTION

Many cancer survivors report cognitive problems following diagnosis and treatment. However, the clinical significance of patient-reported cognitive symptoms early in survivorship can be unclear. We used a machine learning approach to determine the association of persistent self-reported cognitive symptoms two years after diagnosis and neurocognitive test performance in a prospective cohort of older breast cancer survivors.

MATERIALS AND METHODS

We enrolled breast cancer survivors with non-metastatic disease (n = 435) and age- and education-matched non-cancer controls (n = 441) between August 2010 and December 2017 and followed until January 2020; we excluded women with neurological disease and all women passed a cognitive screen at enrollment. Women completed the FACT-Cog Perceived Cognitive Impairment (PCI) scale and neurocognitive tests of attention, processing speed, executive function, learning, memory and visuospatial ability, and timed activities of daily living assessments at enrollment (pre-systemic treatment) and annually to 24 months, for a total of 59 individual neurocognitive measures. We defined persistent self-reported cognitive decline as clinically meaningful decline (3.7+ points) on the PCI scale from enrollment to twelve months with persistence to 24 months. Analysis used four machine learning models based on data for change scores (baseline to twelve months) on the 59 neurocognitive measures and measures of depression, anxiety, and fatigue to determine a set of variables that distinguished the 24-month persistent cognitive decline group from non-cancer controls or from survivors without decline.

RESULTS

The sample of survivors and controls ranged in age from were ages 60-89. Thirty-three percent of survivors had self-reported cognitive decline at twelve months and two-thirds continued to have persistent decline to 24 months (n = 60). Least Absolute Shrinkage and Selection Operator (LASSO) models distinguished survivors with persistent self-reported declines from controls (AUC = 0.736) and survivors without decline (n = 147; AUC = 0.744). The variables that separated groups were predominantly neurocognitive test performance change scores, including declines in list learning, verbal fluency, and attention measures.

DISCUSSION

Machine learning may be useful to further our understanding of cancer-related cognitive decline. Our results suggest that persistent self-reported cognitive problems among older women with breast cancer are associated with a constellation of mild neurocognitive changes warranting clinical attention.

Global Aging and Cancer: Advancing Care Through Innovation

The oncology field, like many others, is digitalizing rapidly, a phenomenon that may have been accelerated by the COVID-19 pandemic. This movement is creating opportunities and challenges. Another rapidly developing change is the aging of the global population; because cancer is a disease of aging, there is a need for health systems to adapt to taking care of such patients. In this article, we address how these innovative technologies can be leveraged to improve the care of older patients with cancer beyond academic centers, such as in underserved areas and low- and middle-income countries. We review how digital technologies can be used to enhance the follow-up of patients in low- and middle-income countries. We also tackle the issue of training a global workforce to treat cancer in an aging population and how to leverage innovations in this matter. Finally, we review opportunities to expand the usefulness of big data and machine learning beyond academic centers to support private practices and underserved areas.

Health Services Research in Anesthesia: A Brief Overview of Common Methodologies

The use of large data sources such as registries and claims-based data sets to perform health services research in anesthesia has increased considerably, ultimately informing clinical decisions, supporting evaluation of policy or intervention changes, and guiding further research. These observational data sources come with limitations that must be addressed to effectively examine all aspects of health care services and generate new individual- and population-level knowledge. Several statistical methods are growing in popularity to address these limitations, with the goal of mitigating confounding and other biases. In this article, we provide a brief overview of common statistical methods used in health services research when using observational data sources, guidance on their interpretation, and examples of how they have been applied to anesthesia-related health services research. Methods described involve regression, propensity scoring, instrumental variables, difference-in-differences, interrupted time series, and machine learning.