Informing clinical assessment by contextualizing post-hoc explanations of risk prediction models in type-2 diabetes.

Medical experts may use Artificial Intelligence (AI) systems with greater trust if these are supported by 'contextual explanations' that let the practitioner connect system inferences to their context of use. However, their importance in improving model usage and understanding has not been extensively studied. Hence, we consider a comorbidity risk prediction scenario and focus on contexts regarding the patients' clinical state, AI predictions about their risk of complications, and algorithmic explanations supporting the predictions. We explore how relevant information for such dimensions can be extracted from Medical guidelines to answer typical questions from clinical practitioners. We identify this as a question answering (QA) task and employ several state-of-the-art Large Language Models (LLM) to present contexts around risk prediction model inferences and evaluate their acceptability. Finally, we study the benefits of contextual explanations by building an end-to-end AI pipeline including data cohorting, AI risk modeling, post-hoc model explanations, and prototyped a visual dashboard to present the combined insights from different context dimensions and data sources, while predicting and identifying the drivers of risk of Chronic Kidney Disease (CKD) - a common type-2 diabetes (T2DM) comorbidity. All of these steps were performed in deep engagement with medical experts, including a final evaluation of the dashboard results by an expert medical panel. We show that LLMs, in particular BERT and SciBERT, can be readily deployed to extract some relevant explanations to support clinical usage. To understand the value-add of the contextual explanations, the expert panel evaluated these regarding actionable insights in the relevant clinical setting. Overall, our paper is one of the first end-to-end analyses identifying the feasibility and benefits of contextual explanations in a real-world clinical use case. Our findings can help improve clinicians' usage of AI models.

Impact of the COVID-19 pandemic on treatment for mental health needs: a perspective on service use patterns and expenditures from commercial medical claims data.

OBJECTIVE: To examine changes in use patterns, cost of healthcare services before and after the outbreak of the COVID-19 pandemic, and their impacts on expenditures for patients receiving treatment for depression, anxiety, eating disorders, and substance use. METHODS: This cross-sectional study employed statistical tests to analyze claims in MarketScan® Commercial Database in March 2020-February 2021 and quarterly from March 2020 to August 2021, compared to respective pre-pandemic periods. The analysis is based on medical episodes created by the Merative™ Medical Episode Grouper (MEG). MEG is a methodology that groups medical and prescription drug claims to create clinically relevant episodes of care. RESULTS: Comparing year-over-year changes, proportion of patients receiving anxiety treatment among all individuals obtaining healthcare services grew 13.7% in the first year of the pandemic (3/2020-2/2021) versus 10.0% in the year before the pandemic (3/2019-2/2020). This, along with a higher growth in price per episode (5.5% versus 4.3%) resulted in a greater increase in per claimant expenditure ($0.61 versus $0.41 per month). In the same periods, proportion of patients receiving treatment for depression grew 3.7% versus 6.9%, but per claimant expenditure grew by same amount due to an increase in price per episode (4.8%). Proportion of patients receiving treatment for anorexia started to increase 21.1% or more in the fall of 2020. Patient proportion of alcohol use in age group 18-34 decreased 17.9% during the pandemic but price per episode increased 26.3%. Patient proportion of opioid use increased 11.5% in March-May 2020 but decreased or had no significant changes in subsequent periods. CONCLUSIONS: We investigated the changes in use patterns and expenditures of mental health patients before and after the outbreak of the COVID-19 pandemic using claims data in MarketScan®. We found that the changes and their financial impacts vary across mental health conditions, age groups, and periods of the pandemic. Some changes are unexpected from previously reported prevalence increases among the general population and could underlie unmet treatment needs. Therefore, mental health providers should anticipate the use pattern changes in services with similar COVID-19 pandemic disruptions and payers should anticipate cost increases due, in part, to increased price and/or service use.

Artificial Intelligence Clinical Evidence Engine for Automatic Identification, Prioritization, and Extraction of Relevant Clinical Oncology Research.

PURPOSE: We developed a system to automate analysis of the clinical oncology scientific literature from bibliographic databases and match articles to specific patient cohorts to answer specific questions regarding the efficacy of a treatment. The approach attempts to replicate a clinician's mental processes when reviewing published literature in the context of a patient case. We describe the system and evaluate its performance. METHODS: We developed separate ground truth data sets for each of the tasks described in the paper. The first ground truth was used to measure the natural language processing (NLP) accuracy from approximately 1,300 papers covering approximately 3,100 statements and approximately 25 concepts; performance was evaluated using a standard F1 score. The ground truth for the expert classifier model was generated by dividing papers cited in clinical guidelines into a training set and a test set in an 80:20 ratio, and performance was evaluated for accuracy, sensitivity, and specificity. RESULTS: The NLP models were able to identify individual attributes with a 0.7-0.9 F1 score, depending on the attribute of interest. The expert classifier machine learning model was able to classify the individual records with a 0.93 accuracy (95% CI, 0.9 to 0.96, P < .0001), and sensitivity and specificity of 0.95 and 0.91, respectively. Using a decision boundary of 0.5 for the positive (expert) label, the classifier demonstrated an F1 score of 0.92. CONCLUSION: The system identified and extracted evidence from the oncology literature with a high degree of accuracy, sensitivity, and specificity. This tool enables timely access to the most relevant biomedical literature, providing critical support to evidence-based practice in areas of rapidly evolving science.

Interrogation of Functional Cell-Surface Markers Identifies CD151 Dependency in High-Grade Serous Ovarian Cancer.

The degree of genetic aberrations characteristic of high-grade serous ovarian cancer (HGSC) makes identification of the molecular features that drive tumor progression difficult. Here, we perform genome-wide RNAi screens and comprehensive expression analysis of cell-surface markers in a panel of HGSC cell lines to identify genes that are critical to their survival. We report that the tetraspanin CD151 contributes to survival of a subset of HGSC cell lines associated with a ZEB transcriptional program and supports the growth of HGSC tumors. Moreover, we show that high CD151 expression is prognostic of poor clinical outcome. This study reveals cell-surface vulnerabilities associated with HGSC, provides a framework for identifying therapeutic targets, and reports a role for CD151 in HGSC.

Quality of life beyond 6 months after diagnosis in older adults with acute myeloid leukemia.

Although intensive chemotherapy may improve survival in older people with acute myeloid leukemia (AML) without adverse cytogenetics, its impact on quality of life (QOL) is mixed and most patients complain of fatigue up to 6 months after diagnosis. Little information is available on longer-term QOL outcomes. We prospectively followed 20 patients age 60 or older with AML who provided QOL data more than 6 months after diagnosis. Over the first 6 months, there were clinically important improvements in global health, role function, social function, and emotional function. Physical function and cognitive function were stable over time. Over the next 6 months, social function and fatigue improved, and other domains remained stable. Achievement of complete remission appeared to be associated with improvements in global health, physical function, and role function without negatively affecting other health domains. This information may aid discussions with patients about treatment.

A novel inhibitor of glucose uptake sensitizes cells to FAS-induced cell death.

Evasion of death receptor ligand-induced apoptosis is an important contributor to cancer development and progression. Therefore, molecules that restore sensitivity to death receptor stimuli would be important tools to better understand this biological pathway and potential leads for therapeutic adjuncts. Previously, the small-molecule N-[4-chloro-3-(trifluoromethyl)phenyl]-3-oxobutanamide (fasentin) was identified as a chemical sensitizer to the death receptor stimuli FAS and tumor necrosis factor apoptosis-inducing ligand, but its mechanism of action was unknown. Here, we determined that fasentin alters expression of genes associated with nutrient and glucose deprivation. Consistent with this finding, culturing cells in low-glucose medium recapitulated the effects of fasentin and sensitized cells to FAS. Moreover, we showed that fasentin inhibited glucose uptake. Using virtual docking studies with a homology model of the glucose transport protein GLUT1, fasentin interacted with a unique site in the intracellular channel of this protein. Additional chemical studies with other GLUT inhibitors and analogues of fasentin supported a role for partial inhibition of glucose transport as a mechanism to sensitize cells to death receptor stimuli. Thus, fasentin is a novel inhibitor of glucose transport that blocks glucose uptake and highlights a new mechanism to sensitize cells to death ligands.