Machine learning for detection of heterogeneous effects of Medicaid coverage on depression.

In 2008, Oregon expanded its Medicaid program using a lottery, creating a rare opportunity to study the effects of Medicaid coverage using a randomized controlled design (Oregon Health Insurance Experiment). Analysis showed that Medicaid coverage lowered the risk of depression. However, this effect may vary between individuals, and the identification of individuals likely to benefit the most has the potential to improve the effectiveness and efficiency of the Medicaid program. By applying the machine learning causal forest to data from this experiment, we found substantial heterogeneity in the effect of Medicaid coverage on depression; individuals with high predicted benefit were older and had more physical or mental health conditions at baseline. Expanding coverage to individuals with high predicted benefit generated greater reduction in depression prevalence than expanding to all eligible individuals (21.5 vs 8.8 percentage-point reduction; adjusted difference = +12.7 [95% CI, +4.6 to +20.8]; P = 0.003), at substantially lower cost per case prevented ($16 627 vs $36 048; adjusted difference = -$18 598 [95% CI, -156 953 to -3120]; P = 0.04). Medicaid coverage reduces depression substantially more in a subset of the population than others, in ways that are predictable in advance. Targeting coverage on those most likely to benefit could improve the effectiveness and efficiency of insurance expansion. This article is part of a Special Collection on Mental Health.

Clinfo.ai: An Open-Source Retrieval-Augmented Large Language Model System for Answering Medical Questions using Scientific Literature.

The quickly-expanding nature of published medical literature makes it challenging for clinicians and researchers to keep up with and summarize recent, relevant findings in a timely manner. While several closed-source summarization tools based on large language models (LLMs) now exist, rigorous and systematic evaluations of their outputs are lacking. Furthermore, there is a paucity of high-quality datasets and appropriate benchmark tasks with which to evaluate these tools. We address these issues with four contributions: we release Clinfo.ai, an open-source WebApp that answers clinical questions based on dynamically retrieved scientific literature; we specify an information retrieval and abstractive summarization task to evaluate the performance of such retrieval-augmented LLM systems; we release a dataset of 200 questions and corresponding answers derived from published systematic reviews, which we name PubMed Retrieval and Synthesis (PubMedRS-200); and report benchmark results for Clinfo.ai and other publicly available OpenQA systems on PubMedRS-200.

Intrinsic Connectivity and Family Dynamics: Striatolimbic Markers of Risk and Resilience in Youth at Familial Risk for Mood Disorders.

BACKGROUND: Few studies to date have characterized functional connectivity (FC) within emotion and reward networks in relation to family dynamics in youth at high familial risk for bipolar disorder (HR-BD) and major depressive disorder (HR-MDD) relative to low-risk youth (LR). Such characterization may advance our understanding of the neural underpinnings of mood disorders and lead to more effective interventions. METHODS: A total of 139 youth (43 HR-BD, 46 HR-MDD, and 50 LR) aged 12.9 ± 2.7 years were longitudinally followed for 4.5 ± 2.4 years. We characterized differences in striatolimbic FC that distinguished between HR-BD, HR-MDD, and LR and between resilience and conversion to psychopathology. We then examined whether risk status moderated FC-family dynamic associations. Finally, we examined whether baseline between-group FC differences predicted resilence versus conversion to psychopathology. RESULTS: HR-BD had greater amygdala-middle frontal gyrus and dorsal striatum-middle frontal gyrus FC relative to HR-MDD and LR, and HR-MDD had lower amygdala-fusiform gyrus and dorsal striatum-precentral gyrus FC relative to HR-BD and LR (voxel-level p < .001, cluster-level false discovery rate-corrected p < .05). Resilient youth had greater amygdala-orbitofrontal cortex and ventral striatum-dorsal anterior cingulate cortex FC relative to youth with conversion to psychopathology (voxel-level p < .001, cluster-level false discovery rate-corrected p < .05). Greater family rigidity was inversely associated with amygdala-fusiform gyrus FC across all groups (false discovery rate-corrected p = .017), with a moderating effect of bipolar risk status (HR-BD vs. HR-MDD p < .001; HR-BD vs. LR p = .005). Baseline FC differences did not predict resilence versus conversion to psychopathology. CONCLUSIONS: Findings represent neural signatures of risk and resilience in emotion and reward processing networks in youth at familial risk for mood disorders that may be targets for novel interventions tailored to the family context.

Transdiagnostic Symptom Subtypes to Predict Response to Therapeutic Transcranial Magnetic Stimulation in Major Depressive Disorder and Posttraumatic Stress Disorder.

The diagnostic categories in psychiatry often encompass heterogeneous symptom profiles associated with differences in the underlying etiology, pathogenesis and prognosis. Prior work demonstrated that some of this heterogeneity can be quantified though dimensional analysis of the Depression Anxiety Stress Scale (DASS), yielding unique transdiagnostic symptom subtypes. This study investigated whether classifying patients according to these symptom profiles would have prognostic value for the treatment response to therapeutic transcranial magnetic stimulation (TMS) in comorbid major depressive disorder (MDD) and posttraumatic stress disorder (PTSD). A linear discriminant model was constructed using a simulation dataset to classify 35 participants into one of the following six pre-defined symptom profiles: Normative Mood, Tension, Anxious Arousal, Generalized Anxiety, Anhedonia and Melancholia. Clinical outcomes with TMS across MDD and PTSD were assessed. All six symptom profiles were present. After TMS, participants with anxious arousal were less likely to achieve MDD remission compared to other subtypes (FET, odds ratio 0.16, p = 0.034), exhibited poorer PTSD symptom reduction (21% vs. 46%; t (33) = 2.025, p = 0.051) and were less likely to complete TMS (FET, odds ratio 0.066, p = 0.011). These results offer preliminary evidence that classifying individuals according to these transdiagnostic symptom profiles may offer a simple method to inform TMS treatment decisions.

Mapping Neural Circuit Biotypes to Symptoms and Behavioral Dimensions of Depression and Anxiety.

BACKGROUND: Despite tremendous advances in characterizing human neural circuits that govern emotional and cognitive functions impaired in depression and anxiety, we lack a circuit-based taxonomy for depression and anxiety that captures transdiagnostic heterogeneity and informs clinical decision making. METHODS: We developed and tested a novel system for quantifying 6 brain circuits reproducibly and at the individual patient level. We implemented standardized circuit definitions relative to a healthy reference sample and algorithms to generate circuit clinical scores for the overall circuit and its constituent regions. RESULTS: In new data from primary and generalizability samples of depression and anxiety (N = 250), we demonstrated that overall disconnections within task-free salience and default mode circuits map onto symptoms of anxious avoidance, loss of pleasure, threat dysregulation, and negative emotional biases-core characteristics that transcend diagnoses-and poorer daily function. Regional dysfunctions within task-evoked cognitive control and affective circuits may implicate symptoms of cognitive and valence-congruent emotional functions. Circuit dysfunction scores also distinguished response to antidepressant and behavioral intervention treatments in an independent sample (n = 205). CONCLUSIONS: Our findings articulate circuit dimensions that relate to transdiagnostic symptoms across mood and anxiety disorders. Our novel system offers a foundation for deploying standardized circuit assessments across research groups, trials, and clinics to advance more precise classifications and treatment targets for psychiatry.

A computational approach to measure the linguistic characteristics of psychotherapy timing, responsiveness, and consistency.

Although individual psychotherapy is generally effective for a range of mental health conditions, little is known about the moment-to-moment language use of effective therapists. Increased access to computational power, coupled with a rise in computer-mediated communication (telehealth), makes feasible the large-scale analyses of language use during psychotherapy. Transparent methodological approaches are lacking, however. Here we present novel methods to increase the efficiency of efforts to examine language use in psychotherapy. We evaluate three important aspects of therapist language use - timing, responsiveness, and consistency - across five clinically relevant language domains: pronouns, time orientation, emotional polarity, therapist tactics, and paralinguistic style. We find therapist language is dynamic within sessions, responds to patient language, and relates to patient symptom diagnosis but not symptom severity. Our results demonstrate that analyzing therapist language at scale is feasible and may help answer longstanding questions about specific behaviors of effective therapists.

Ontology-driven weak supervision for clinical entity classification in electronic health records.

In the electronic health record, using clinical notes to identify entities such as disorders and their temporality (e.g. the order of an event relative to a time index) can inform many important analyses. However, creating training data for clinical entity tasks is time consuming and sharing labeled data is challenging due to privacy concerns. The information needs of the COVID-19 pandemic highlight the need for agile methods of training machine learning models for clinical notes. We present Trove, a framework for weakly supervised entity classification using medical ontologies and expert-generated rules. Our approach, unlike hand-labeled notes, is easy to share and modify, while offering performance comparable to learning from manually labeled training data. In this work, we validate our framework on six benchmark tasks and demonstrate Trove's ability to analyze the records of patients visiting the emergency department at Stanford Health Care for COVID-19 presenting symptoms and risk factors.

Intrinsic reward circuit connectivity profiles underlying symptom and quality of life outcomes following antidepressant medication: a report from the iSPOT-D trial.

There is a critical need to better understand the neural basis of antidepressant medication (ADM) response with respect to both symptom alleviation and quality of life (QoL) in major depressive disorder (MDD). Reward neurocircuitry has been implicated in QoL, the neural basis of MDD, and the mechanisms of ADM response. Yet, we do not know whether change in reward neurocircuitry as a function of ADM is associated with change in symptoms and QoL. To address this gap in knowledge, we analyzed data from 128 patients with MDD who participated in the iSPOT-D trial and were assessed with functional neuroimaging pre- and post-ADM treatment (randomized to sertraline, venlafaxine-XR, or escitalopram). 58 matched healthy controls were scanned at the same time points. We quantified functional connectivity (FC) of reward neurocircuitry using nucleus accumbens (NAc) seed regions of interest, and then characterized how changes in FC relate to symptom response (primary outcome) and QoL response (secondary outcome). Symptom responders showed an increase in NAc-dorsal anterior cingulate cortex (ACC) FC relative to non-responders (p < 0.001) which was associated with improvement in physical QoL (p < 0.0003), and a decrease in NAc-inferior parietal lobule FC relative to controls (p < 0.001). QoL response was characterized by increases in FC between NAc-ventral ACC for environmental, NAc-thalamus for physical, and NAc-paracingulate gyrus for social domains (p < 0.001). Symptom responders to sertraline were distinguished by a decrease in NAc-insula FC (p < 0.001) and to venlafaxine-XR by an increase in NAc-inferior temporal gyrus FC (p < 0.005). Findings suggest that change in reward neurocircuitry may underlie differential ADM response profiles with respect to symptoms and QoL in depression.

Test-retest reliability of the human functional connectome over consecutive days: identifying highly reliable portions and assessing the impact of methodological choices.

Countless studies have advanced our understanding of the human brain and its organization by using functional magnetic resonance imaging (fMRI) to derive network representations of human brain function. However, we do not know to what extent these "functional connectomes" are reliable over time. In a large public sample of healthy participants (N = 833) scanned on two consecutive days, we assessed the test-retest reliability of fMRI functional connectivity and the consequences on reliability of three common sources of variation in analysis workflows: atlas choice, global signal regression, and thresholding. By adopting the intraclass correlation coefficient as a metric, we demonstrate that only a small portion of the functional connectome is characterized by good (6-8%) to excellent (0.08-0.14%) reliability. Connectivity between prefrontal, parietal, and temporal areas is especially reliable, but also average connectivity within known networks has good reliability. In general, while unreliable edges are weak, reliable edges are not necessarily strong. Methodologically, reliability of edges varies between atlases, global signal regression decreases reliability for networks and most edges (but increases it for some), and thresholding based on connection strength reduces reliability. Focusing on the reliable portion of the connectome could help quantify brain trait-like features and investigate individual differences using functional neuroimaging.

Ontology-driven weak supervision for clinical entity classification in electronic health records.

In the electronic health record, using clinical notes to identify entities such as disorders and their temporality (e.g. the order of an event relative to a time index) can inform many important analyses. However, creating training data for clinical entity tasks is time consuming and sharing labeled data is challenging due to privacy concerns. The information needs of the COVID-19 pandemic highlight the need for agile methods of training machine learning models for clinical notes. We present Trove, a framework for weakly supervised entity classification using medical ontologies and expert-generated rules. Our approach, unlike hand-labeled notes, is easy to share and modify, while offering performance comparable to learning from manually labeled training data. In this work, we validate our framework on six benchmark tasks and demonstrate Trove's ability to analyze the records of patients visiting the emergency department at Stanford Health Care for COVID-19 presenting symptoms and risk factors.

Assessing the accuracy of automatic speech recognition for psychotherapy.

Accurate transcription of audio recordings in psychotherapy would improve therapy effectiveness, clinician training, and safety monitoring. Although automatic speech recognition software is commercially available, its accuracy in mental health settings has not been well described. It is unclear which metrics and thresholds are appropriate for different clinical use cases, which may range from population descriptions to individual safety monitoring. Here we show that automatic speech recognition is feasible in psychotherapy, but further improvements in accuracy are needed before widespread use. Our HIPAA-compliant automatic speech recognition system demonstrated a transcription word error rate of 25%. For depression-related utterances, sensitivity was 80% and positive predictive value was 83%. For clinician-identified harm-related sentences, the word error rate was 34%. These results suggest that automatic speech recognition may support understanding of language patterns and subgroup variation in existing treatments but may not be ready for individual-level safety surveillance.

Automated Classification of Radiographic Knee Osteoarthritis Severity Using Deep Neural Networks.

PURPOSE: To develop an automated model for staging knee osteoarthritis severity from radiographs and to compare its performance to that of musculoskeletal radiologists. MATERIALS AND METHODS: Radiographs from the Osteoarthritis Initiative staged by a radiologist committee using the Kellgren-Lawrence (KL) system were used. Before using the images as input to a convolutional neural network model, they were standardized and augmented automatically. The model was trained with 32 116 images, tuned with 4074 images, evaluated with a 4090-image test set, and compared to two individual radiologists using a 50-image test subset. Saliency maps were generated to reveal features used by the model to determine KL grades. RESULTS: With committee scores used as ground truth, the model had an average F1 score of 0.70 and an accuracy of 0.71 for the full test set. For the 50-image subset, the best individual radiologist had an average F1 score of 0.60 and an accuracy of 0.60; the model had an average F1 score of 0.64 and an accuracy of 0.66. Cohen weighted κ between the committee and model was 0.86, comparable to intraexpert repeatability. Saliency maps identified sites of osteophyte formation as influential to predictions. CONCLUSION: An end-to-end interpretable model that takes full radiographs as input and predicts KL scores with state-of-the-art accuracy, performs as well as musculoskeletal radiologists, and does not require manual image preprocessing was developed. Saliency maps suggest the model's predictions were based on clinically relevant information. Supplemental material is available for this article. © RSNA, 2020.