Development of a Machine Learning Model Using Limited Features to Predict 6-Month Mortality at Treatment Decision Points for Patients With Advanced Solid Tumors.

PURPOSE: Patients with advanced solid tumors may receive intensive treatments near the end of life. This study aimed to create a machine learning (ML) model using limited features to predict 6-month mortality at treatment decision points (TDPs). METHODS: We identified a cohort of adults with advanced solid tumors receiving care at a major cancer center from 2014 to 2020. We identified TDPs for new lines of therapy (LoTs) and confirmed mortality at 6 months after a TDP. Using extreme gradient boosting, ML models were developed, which used or derived features from a limited set of electronic health record data considering the literature, clinical relevance, variability, availability, and predictive importance using Shapley additive explanations scores. We predicted and observed 6-month mortality after a TDP and assessed a risk stratification strategy with different risk thresholds to support communication of chance of survival. RESULTS: Four thousand one hundred ninety-two patients were included. Patients had 7,056 TDPs, for which the 6-month mortality increased from 17.9% to 46.7% after starting first to sixth LoT, respectively. On the basis of internal validation, models using both 111 (Full) or 45 (Limited-45) features accurately predicted 6-month mortality (area under the curve ≥ 0.80). Using a 0.3 risk threshold in the Limited-45 model, the observed 6-month survival was 34% (95% CI, 28 to 40) versus 81% (95% CI, 81 to 82) among those classified with low or higher chance of survival, respectively. The positive predictive value of the Limited-45 model was 0.66 (95% CI, 0.60 to 0.72). CONCLUSION: We developed and validated a ML model using a limited set of 45 features readily derived from electronic health record data to predict 6-month prognosis in patients with advanced solid tumors. The model output may support shared decision making as patients consider the next LoT.

Introducing AI to the molecular tumor board: one direction toward the establishment of precision medicine using large-scale cancer clinical and biological information.

Since U.S. President Barack Obama announced the Precision Medicine Initiative in his New Year's State of the Union address in 2015, the establishment of a precision medicine system has been emphasized worldwide, particularly in the field of oncology. With the advent of next-generation sequencers specifically, genome analysis technology has made remarkable progress, and there are active efforts to apply genome information to diagnosis and treatment. Generally, in the process of feeding back the results of next-generation sequencing analysis to patients, a molecular tumor board (MTB), consisting of experts in clinical oncology, genetic medicine, etc., is established to discuss the results. On the other hand, an MTB currently involves a large amount of work, with humans searching through vast databases and literature, selecting the best drug candidates, and manually confirming the status of available clinical trials. In addition, as personalized medicine advances, the burden on MTB members is expected to increase in the future. Under these circumstances, introducing cutting-edge artificial intelligence (AI) technology and information and communication technology to MTBs while reducing the burden on MTB members and building a platform that enables more accurate and personalized medical care would be of great benefit to patients. In this review, we introduced the latest status of elemental technologies that have potential for AI utilization in MTB, and discussed issues that may arise in the future as we progress with AI implementation.

Resource management simulator for radiology departments.

We propose a new system to manage modalities in radiology departments. Our system introduces a novel method to accurately estimate some indexes in order to provide an analysis of resource plans within three broad categories: financial affairs, patient satisfaction, and productivity. The main idea is to simulate the operational status of modalities. Results from a preliminary hospital evaluation show that use of the system results in more efficient resource management.

Visualization method for finding critical care factors in variance analysis.

We present a novel visualization method for finding care factors in variance analysis. The analysis has two stages: first stage enables users to extract a significant variance, and second stage enables users to find out a critical care factors of the variance. The analysis has been validated by using synthetically created inpatient care processes. It was found that the method is efficient in improving clinical pathways.