Weekly Digest
Weekly ToC Digest, week of 2026-09-21
New papers on suicidality, intensive longitudinal data, and computational methods, scanned automatically each Monday and ranked against the interests that drive this digest. Scores are a language model’s judgement from the title and abstract only, so read them as triage and not as appraisal.
1 of 7 triage batches failed, so about 40 items went unscored this week.
| Section | Kept | Threshold |
|---|---|---|
| Suicide & self-harm | 20 | ≥ 0.55 |
| Intensive longitudinal & sensing | 11 | ≥ 0.60 |
| ML & dynamical systems methods | 15 | ≥ 0.65 |
| Adjacent mental health, genetics & neurobiology | 0 | ≥ 0.62 |
46 kept from 280 scored, out of 1144 gathered across 42 journal feeds and 11 PubMed queries in the last 7 days. Spanning 27 sources; 32 of 46 include an abstract.
Suicide & self-harm
Heterogeneity in non-suicidal self-injury ideation and treatment response among inpatient female adolescents with mood disorders: Insights from ecological momentary assessment
medRxiv Psychiatry · 0.92 · 2026-09-16
NSSI adolescent EMA machine learning latent profile
Uses EMA and latent profile analysis to identify NSSI ideation subtypes in inpatient female adolescents, then builds ML models to predict treatment response from dynamic features.
Abstract snippet
Non-suicidal self-injury (NSSI) is highly prevalent and exhibits substantial fluctuations among adolescents with mood disorders, yet the heterogeneity and differential treatment responses accompanied by such fluctuations remain unclear. The current study employed ecological momentary assessment (EMA) and latent profile analysis to identify latent subtypes of NSSI ideation via temporal features, and constructed machine-learning models to validate the predictive value of dynamic features for…
A Smartphone-Based Acoustic Machine Learning Pipeline for Detecting Suicidal Ideation: Case-Control Model Development and Validation Study.
JMIR Form Res (PubMed) · 0.92 · 2026-09-15
suicidal ideation machine learning smartphone acoustic validation
Develops smartphone acoustic ML pipeline to detect suicidal ideation in university students, validating in case-control design.
Abstract snippet
BACKGROUND: Suicidal ideation (SI) among university students is a growing public health concern. Self-report screening can be limited by concealment and delayed disclosure. We evaluated a leakage-resistant, proof-of-concept pipeline to detect SI from standardized smartphone-recorded speech. OBJECTIVE: This study aimed to extract acoustic markers from brief smartphone-based reading tasks and develop machine learning models for suicide risk prediction in university students, enabling low-cost,…
Characterizing Family Abuse in Suicidal Ideation Posts on Reddit: Large Language Model-Assisted Content Analysis.
JMIR Infodemiology (PubMed) · 0.90 · 2026-09-14
suicidal ideation LLM content analysis Reddit family abuse
Uses LLMs to characterize family abuse themes in Reddit posts expressing suicidal ideation.
Abstract snippet
BACKGROUND: Suicidal ideation is a significant global public health concern, with family abuse recognized as a key risk factor. Although clinical studies have examined the link between family abuse and mental health, its representation in social media discussions-particularly in relation to suicidal ideation-remains largely unexplored. OBJECTIVE: This study aimed to examine how family abuse manifests in suicidal ideation narratives on social media, focusing on the demographic characteristics of…
Using Automated Coding of Nonverbal Behavior During a Suicide Assessment to Inform Risk Detection: Mixed Cross-Sectional and Exploratory Prospective Study.
JMIR Form Res (PubMed) · 0.90 · 2026-09-16
suicide nonverbal behavior automated coding machine learning prospective
Uses automated coding of facial action and head motion during suicide assessments to inform risk detection in young adults.
Abstract snippet
BACKGROUND: Suicide assessments have historically privileged verbal report by the patient, despite the fact that nonverbal behaviors of patients and their clinicians may convey important affective and interpersonal information about suicide risk. Recent advances in computational science enable efficient characterization of rich nonverbal data. OBJECTIVE: This study aimed to use automated coding to test whether facial action and head motion exhibited by young adults and their clinical…
Multilevel dynamic abnormalities of suicidal ideation in major depressive disorder from regional dynamics to network states
J Affective Disorders · 0.90
suicide suicidal ideation multilevel dynamical systems network states
Models multilevel dynamic abnormalities of suicidal ideation in MDD, linking regional dynamics to network states.
Feasibility and acceptability of smartphone-based ecological momentary assessment for monitoring suicidal ideation among gay, bisexual, and other men who have sex with men in Nepal.
J Ment Health (PubMed) · 0.88 · 2026-09-18
suicide EMA feasibility GBMSM Nepal
Evaluates smartphone-based EMA for monitoring suicidal ideation among GBMSM in Nepal over 30 days.
Abstract snippet
BACKGROUND: Ecological momentary assessment (EMA) measures suicidal thoughts and behaviors in real-time. This study evaluated the feasibility and acceptability of smartphone-based EMA for suicide prevention among gay, bisexual, and other men who have sex with men (GBMSM) in Nepal. METHODS: Participants with prior suicidal ideation or moderate-to-severe depression completed three daily smartphone EMAs for 30 days. Feasibility was measured through recruitment, retention, and compliance, and…
Predictive linguistic markers of suicidal ideation in autobiographical memory narratives
Psychiatry Research · 0.80
suicide linguistic markers autobiographical memory prediction NLP
Extracts predictive linguistic markers from autobiographical memory narratives to indicate suicidal ideation.
Not in Education Employment or Training (NEET) Trajectories Between Adolescence and Early Adulthood and Subsequent Mental Health
medRxiv Psychiatry · 0.78 · 2026-09-15
suicide attempt NEET longitudinal adolescent mental health
Examines how NEET trajectories from adolescence to early adulthood predict suicide attempt at age 33.
Abstract snippet
ImportanceNot in Education Employment or Training (NEET) in youth is associated with poorer mental health and is increasing. Prior work has treated NEET as a single status, obscuring whether repeated disengagement carries cumulative later mental health risk. ObjectiveEstimate associations between the number of NEET occasions between ages 16 and 33 years and depression, anxiety, wellbeing and a recent suicide attempt at age 33. Secondary analysis explored associations between age of first NEET…
Modulating factors for suicide reattempt among adolescents: A network analysis.
Eur Psychiatry (PubMed) · 0.78 · 2026-09-14
suicide reattempt adolescent network analysis clinical
Uses network analysis to identify modulating factors for suicide reattempt in a clinical adolescent sample.
Abstract snippet
BACKGROUND: Suicide is a serious public health issue and the leading non-accidental cause of death among adolescents in Spain. Although a previous suicide attempt is the strongest risk factor for a new attempt, studies focused specifically on adolescent reattempters remain scarce. This study aims to identify modulating factors for suicide reattempts in a clinical adolescent population following an index attempt, using network analysis. METHODS: We recruited a total of 287 adolescents aged 12 to…
Impact of adverse childhood experiences on mental health challenges and suicidal thoughts among adolescents living with HIV: a multi-center study in Vietnam.
AIDS Care (PubMed) · 0.78 · 2026-09-16
suicidal thoughts ACEs adolescent HIV Vietnam
Examines ACEs association with suicidal thoughts in HIV-positive adolescents across Vietnamese centers.
Abstract snippet
Adolescents living with HIV (ALHIV) face considerable mental health challenges, frequently worsened by adverse childhood experiences (ACEs). Our study evaluated the prevalence of psychological disorders and their associations with ACEs among ALHIV in Ho Chi Minh City, Vietnam. A multi-center study was conducted from January to April 2022 among 349 adolescents, aged 12-18. Adverse Childhood Experience Scale and the Depression Anxiety Stress Scale-21 were used, along with self-reported suicidal…
Adolescent suicidal distress and adult mental health: findings from a 25-year population based cohort.
Eur Child Adolesc Psychiatry (PubMed) · 0.75 · 2026-09-16
suicide adolescent longitudinal cohort mental health
Examines adolescent suicidal ideation and attempt predicting adult mental health in a 25-year prospective cohort from Quebec.
Abstract snippet
Suicidal distress is common during adolescence, yet its descriptive associations with adult mental health are not well characterized. Using 25 years of prospective data, we examined how adolescent suicidal ideation (and characteristics-recurrence, seriousness, onset) and suicide attempt are associated with self-reported and clinically diagnosed adulthood mental health. Participants were drawn from the Quebec Longitudinal Study of Child Development, a population-based cohort followed from birth…
Stratified Stepped-Care for Reducing Suicide Attempts and Self-Harm in Youth: A Randomized Clinical Trial
JAACAP · 0.75
suicide attempt self-harm youth RCT stepped-care
RCT testing stepped-care intervention to reduce suicide attempts and self-harm in youth.
Predicting the First Onset of Suicidal Thoughts and Behaviors in Adolescents Using Multimodal Risk Factors: A 4-Year Longitudinal Study
JAACAP · 0.75
suicide adolescents prediction longitudinal multimodal
Predicts first onset of suicidal thoughts and behaviors in adolescents over four years using multimodal risk factors.
Nocturnal Autonomic Dysregulation and Admission-Window Clinical Suicide-Risk Assessment in Hospitalized Children and Adolescents.
J Clin Med (PubMed) · 0.72 · 2026-08-29
suicide risk autonomic dysregulation adolescents biosensor hospitalization
Examines nocturnal autonomic dysregulation in hospitalized youth and its association with clinical suicide-risk assessment.
Abstract snippet
Background/Objectives: Suicide-risk assessment during child and adolescent psychiatric hospitalization draws on patient report, clinical history, and professional observation. Whether nocturnal autonomic physiology is concurrently associated with a structured admission-window assessment after accounting for depressive symptoms and self-reported suicidal ideation remains uncertain. Methods: We analyzed 212 hospitalized children and adolescents receiving inpatient care for a major depressive…
Strengthening Youth Crisis Reporting and Response: Development of a Practice-Based Systems Maturity Model.
Health Promot Pract (PubMed) · 0.72 · 2026-09-18
youth suicide crisis response systems model public health
Introduces a practice-based systems maturity model for youth crisis reporting and response.
Abstract snippet
Youth suicide prevention and crisis response require coordinated action across public health, behavioral health, education, health care, law enforcement, and community systems. However, local jurisdictions often lack practical tools to assess system readiness and prioritize system improvements across fragmented service systems. This article introduces the Youth Crisis Reporting and Response Maturity Model, a practice-based framework developed through a multicounty pilot initiative in…
Victimisation, depression and suicidal ideation: understanding interlinkages among youth in North India through structural equation modelling
medRxiv Psychiatry · 0.72 · 2026-09-15
suicidal ideation victimisation depression SEM youth
Uses SEM to model links between victimisation, depression, and suicidal ideation in North Indian youth.
Abstract snippet
BackgroundSuicide and violence are major contributors to youth mortality. Effective prevention strategies require understanding of their psychological underpinnings. We therefore aimed to determine the interconnections among victimisation, physical violence, depression and suicidal ideation and attempts, among young adults in a North India city. MethodsWe used data from a survey of young adults aged 18-22 years, recruited across six colleges by random sampling. Our theory-based structural…
Mental disorders and self-harm among Brazilian children, adolescents, and young adults: prevalence, disability, and mortality estimates from the Global Burden of Disease Study.
Lancet Reg Health Am (PubMed) · 0.72 · 2026-08-05
self-harm adolescent Brazil GBD prevalence
Provides GBD estimates of mental disorders and self-harm in Brazilian children and adolescents.
Abstract snippet
BACKGROUND: The period from childhood to young adulthood involves heightened susceptibility to mental health problems, requiring specific health policies. We present the prevalence and burden associated with mental disorders, substance use disorders (SUDs), and self-harm in Brazilian children, adolescents, and young adults. METHODS: Using Global Burden of Disease Study (GBD) data, prevalence and years lived with disability (YLDs) were described for mental disorders and SUDs in ages 5-29 years.…
Disrupted structural-functional coupling in adolescents and young adults with major depressive disorder and non-suicidal self-injury.
Prog Neuropsychopharmacol Biol Psychiatry (PubMed) · 0.72 · 2026-09-20
NSSI adolescent neuroimaging MDD structural-functional coupling
Shows altered brain structure-function coupling in MDD youth with NSSI.
Abstract snippet
BACKGROUND: Major depressive disorder (MDD) is consistently associated with an elevated risk of suicidal behaviours, including non-suicidal self-injury (NSSI). Neuroimaging evidence suggests that MDD patients with NSSI exhibit alterations in both brain structure and function, warranting further investigation into the underlying mechanisms linking these changes. METHODS: A total of 291 participants were recruited for this study: 141 drug-naïve individuals with MDD, who were divided into an MDD +…
Intensive longitudinal & sensing
Reciprocal association between rest-activity rhythm and depression in young adults: An ecological momentary assessment study
J Affective Disorders · 0.82
EMA rest‑activity rhythm depression young adults ecological momentary
Ecological momentary assessment study testing the reciprocal association between rest‑activity rhythm and depression in young adults.
Dissociation as a marker of emotion dysregulation: An examination in adolescent psychiatric inpatients using ecological momentary assessment
J Affective Disorders · 0.82
EMA dissociation emotion dysregulation adolescent psychiatric inpatients
Ecological momentary assessment examination of dissociation as a marker of emotion dysregulation in adolescent psychiatric inpatients.
Time of day affects the relation between physical activity and feeling energetic in depression: Two real-life studies.
J Psychopathol Clin Sci (PubMed) · 0.70 · 2026-09-14
ILD physical activity depression time of day real-life
Uses intensive longitudinal data from two samples (N=106 and N=68) to examine how time of day moderates the link between physical activity and feeling energetic in depression and healthy groups.
Abstract snippet
Short bouts of physical activity enhance feelings of energy and improve well-being. Circadian rhythm strongly influences mood, but its impact on the association of physical activity and mood is unknown. Intensive longitudinal data were obtained from two independent samples (Ns = 106 and 68), each comprising two groups characterized by distinct manifestations of physical activity and energy: participants with depression (nStudyA = 53; nStudyB = 32) and healthy participants (nStudyA = 53; nStudyB…
Digital Phenotyping of Lifestyle Profiles and Mental Well-Being in German Adults: Prospective Longitudinal Cohort Study.
J Med Internet Res (PubMed) · 0.70 · 2026-09-14
digital phenotyping smartphone sensing lifestyle profiles mental well‑being German adults
Prospective longitudinal cohort using passively collected smartphone‑sensing data to derive lifestyle profiles and relate them to mental well‑being in German adults.
Abstract snippet
BACKGROUND: Digital phenotyping uses passively collected smartphone-sensing data to characterize everyday behavior in naturalistic settings, and has become an important approach for studying mental well-being. Most previous studies have examined associations between individual sensing variables and mental health. However, mental well-being is likely reflected not by isolated behaviors but by combinations of co-occurring daily behaviors that together form lifestyles. Person-centered approaches…
Clinical Implementation of Wearable-Derived Sleep and Activity Reporting for Inpatient Psychiatric Monitoring
JMIR Formative Res · 0.70 · 2026-09-21
wearable‑derived sleep activity reporting inpatient psychiatric monitoring
Describes clinical implementation of wearable‑derived sleep and activity reporting for objective monitoring of inpatients with psychiatric conditions.
Abstract snippet
Background: Sleep is a core component of psychiatric assessment, yet inpatient monitoring typically relies on brief observational checks that are subjective, variable, and sometimes disruptive. Wearable devices offer a means of capturing continuous, objective sleep and activity data without disturbing patients. Although digital health technologies are increasingly used in psychiatric research, little is known about how wearable-derived data can be integrated into routine inpatient workflows or…
Agreement between smartphone-based mobile sensing and actigraphy sleep metrics in young people with bipolar disorder
Psychiatry Research · 0.70
smartphone sensing actigraphy sleep metrics young people bipolar disorder
Examines agreement between smartphone‑based mobile sensing and actigraphy sleep metrics in young people with bipolar disorder.
Patients’ and clinicians’ perspectives on a transdiagnostic experience sampling and ecological momentary intervention protocol.
J Ment Health (PubMed) · 0.68 · 2026-09-16
EMA EMI transdiagnostic service user perspectives clinician perspectives
Explores service users’ and clinicians’ perspectives on a 30‑day ESM monitoring period with on‑demand EMI content in a transdiagnostic sample.
Abstract snippet
BACKGROUND: Experience Sampling Method (ESM) and Ecological Momentary Interventions (EMI) are promising tools for person-centered mental health care, yet evidence on prolonged transdiagnostic monitoring remains limited. AIMS: This study explored service users’ and clinicians’ perspectives on the usability and clinical utility of a prolonged transdiagnostic ESM protocol with on-demand EMI content. METHODS: Within the DISCONNECT project, 89 service users initiated a 30-day ESM monitoring period,…
Multi-Subject Pretraining Enables Short-Calibration Personalization for Closed-Corpus Surface EMG Speech Decoding
arXiv cs.LG · 0.68 · 2026-09-21
surface EMG biosensor speech decoding pretraining calibration
Shows how multi‑subject pretraining reduces calibration needs for surface EMG‑based silent speech interfaces.
Abstract snippet
arXiv:2609.21288v1 Announce Type: new Abstract: Surface electromyography (sEMG)-based silent speech interfaces are limited by cross-user variability and calibration burden. We study a limited-data setting in which each of 27 speech-typical participants contributed less than 0.5 h of data (21.3 min on average) across Aloud and Mimed speech. Within a closed 50-sentence corpus, we used leave-one-subject-out evaluation, initializing from a released single-subject checkpoint, pretraining on…
Emotion regulation and state body image during daily body-exposure situations: A pilot ecological momentary assessment study
J Affective Disorders · 0.65
EMA emotion regulation body image body‑exposure pilot
Pilot ecological momentary assessment study of emotion regulation and state body image during daily body‑exposure situations.
Chronic intracranial neural dynamics forecast treatment response and therapeutic engagement during deep brain stimulation for OCD
medRxiv Psychiatry · 0.65 · 2026-09-21
intracranial neural dynamics deep brain stimulation OCD treatment response forecasting
Shows that chronic intracranial neural dynamics forecast treatment response and therapeutic engagement during DBS for OCD.
Abstract snippet
Clinical improvement following psychiatric neuromodulation often requires weeks to months, creating prolonged uncertainty during treatment optimization. Neural biomarkers could reduce this uncertainty by forecasting treatment outcome, providing early evidence of therapeutic engagement, and objectively tracking clinical response. We analyzed nearly 120,000 patient-hours of chronic intracranial recordings from 24 patients undergoing ventral capsule/ventral striatum deep brain stimulation for…
ML & dynamical systems methods
Identifying key risk factors of adolescent Internet Gaming Disorder using explainable machine learning and network analysis
J Affective Disorders · 0.78
machine learning network analysis adolescent Internet Gaming Disorder explainable AI
Applies explainable machine learning and network analysis to identify risk factors of adolescent Internet Gaming Disorder.
External validation of AI assisted colposcopy using WHO dataset for cervical precancer and cancer detection
npj Digital Medicine · 0.78 · 2026-09-19
AI colposcopy external validation WHO dataset cervical cancer
Provides external validation of an AI‑assisted colposcopy system using a WHO dataset for cervical precancer and cancer detection.
Abstract snippet
npj Digital Medicine, Published online: 19 September 2026; doi:10.1038/s41746-026-02961-3 External validation of AI assisted colposcopy using WHO dataset for cervical precancer and cancer detection
M2G-LLM: Enhancing Clinical Prediction via Multimodal Graph Reasoning and LLM Context Injection
arXiv cs.LG · 0.78 · 2026-09-21
multimodal graph reasoning LLM clinical prediction machine learning
Introduces M2G‑LLM, integrating clinical notes, lab results, and imaging via multimodal graph reasoning and LLM context injection to enhance clinical prediction.
Abstract snippet
arXiv:2609.21164v1 Announce Type: new Abstract: Integrating diverse data modalities — such as clinical notes, laboratory results, and medical imaging — is essential for advancing clinical decision-making. While Large Language Models (LLMs) have shown remarkable performance in processing unstructured clinical text, their limited capacity to incorporate non-text modalities hinders their broader utility in healthcare applications. Here, we introduce M2G-LLM (Multimodal MedGraph-LLM), a novel…
Did you miss me? Making the most of digital phenotyping data by imputing missingness with point process models: observational study.
BMJ Health Care Inform (PubMed) · 0.75 · 2026-09-18
digital phenotyping missingness imputation point process models observational study
Observational study proposing point‑process models to impute missingness in smartphone‑based digital phenotyping data for mental disorder monitoring.
Abstract snippet
OBJECTIVES: Smartphone-based digital phenotyping can provide low-burden behavioural measures for mental disorder monitoring. However, progress in making inferences from these data is challenged by the common occurrence of missing data. We propose a method to impute missingness using non-homogeneous Poisson point process models (PPPMs), where activities (overall phone, social media, communication app usage, outgoing/incoming calls) are modelled as ‘points’. METHODS: We evaluate personalised…
Computational phenotyping and predictive modeling of outcomes using multimodal objective measures in psychiatry
Biol Psychiatry CNNI · 0.75
computational phenotyping predictive modeling multimodal measures psychiatry outcomes
Describes computational phenotyping and predictive modeling of psychiatric outcomes using multimodal objective measures.
Large language model linguistic perplexity in childhood onset psychosis: unique features and developmental trends
medRxiv Psychiatry · 0.75 · 2026-09-20
large language model psychosis childhood onset linguistic perplexity
Uses large language model linguistic perplexity to study childhood onset psychosis and its developmental trends.
Abstract snippet
Objective: Child and early adolescent onset psychosis (COP) is associated with subtle changes in language linked to thought disorder, a key contributor to functional impairment. Large language models (LLM) can detect deviations from expected language patterns by jointly analyzing sentence structure and word choice. This integrated information is captured by measures such as (pseudo)-perplexity, which quantify how difficult it is for an LLM to predict individual words given the surrounding…
Machine learning-assisted plasma PEA proteomics enables differential diagnosis of melancholic depression and bipolar disorder
J Affective Disorders · 0.72
machine learning proteomics depression bipolar disorder differential diagnosis
Uses machine learning on plasma PEA proteomics to differentiate melancholic depression from bipolar disorder.
Machine learning combined with fMRI identifies dynamic brain network alterations in post-stroke depression: A dual-center cross-sectional study
J Affective Disorders · 0.72
machine learning fMRI post-stroke depression brain network dual-center
Combines machine learning with fMRI to detect dynamic brain network alterations in post‑stroke depression.
Bayesian network analysis uncovers physical activity-mood dynamics: Insights from the DiAPAson study.
Psychol Med (PubMed) · 0.70 · 2026-09-15
Bayesian network physical activity mood schizophrenia DiAPAson
Applies Bayesian network analysis to multicenter observational data (DiAPAson) to uncover bidirectional physical activity‑mood dynamics in schizophrenia spectrum disorder.
Abstract snippet
BACKGROUND: Individuals with schizophrenia spectrum disorders (SSDs) frequently exhibit low levels of physical activity (PA) and mood disturbances, both of which contribute to functional impairment and poorer long-term outcomes. Despite growing evidence linking PA and affective regulation, little is known about the real-time, bidirectional relationship between these domains in SSD. METHODS: In this multicenter observational study (DiAPAson project), 120 patients with SSD and 113 age- and…
Pilot of a Micro-Randomized Factorial Trial of Self-Monitoring Feedback in a Behavioral Weight Loss Intervention.
Obes Sci Pract (PubMed) · 0.70 · 2026-09-13
micro‑randomized factorial trial self‑monitoring feedback weight loss behavioral intervention
Pilot micro‑randomized factorial trial evaluating the feasibility and acceptability of different self‑monitoring feedback schedules in a behavioral weight‑loss intervention.
Abstract snippet
BACKGROUND: The provision of self-monitoring feedback has been demonstrated to improve weight loss program outcomes; however, there is little evidence to support how this feedback should be delivered. Micro-randomized factorial trial designs can examine the proximal impact of intervention components, but their feasibility and acceptability in the context of behavioral weight management programs remain unknown. METHODS: The current pilot study evaluated the feasibility and acceptability of…
From monitoring to intervention: a closed-loop digital health framework for gaming disorder.
Front Public Health (PubMed) · 0.70 · 2026-09-04
closed‑loop digital health framework gaming disorder digital phenotyping AI
Proposes a closed‑loop digital health framework that moves from monitoring to intervention for gaming disorder using digital phenotyping and AI.
Abstract snippet
Gaming disorder has become a significant public health concern and represents a complex, dynamic condition that requires continuous risk monitoring throughout its progression. However, conventional assessment methods, which rely primarily on questionnaires and clinical interviews, are inherently static and retrospective, limiting dynamic monitoring and early risk identification. Although digital phenotyping, artificial intelligence (AI), and digital therapeutics have emerged as promising…
Machine learning for predicting unmet mental health treatment need among untreated adults with any mental illness: Evidence from a national survey
Psychiatry Research · 0.70
machine learning mental health treatment need prediction national survey adults
Applies machine learning to predict unmet mental health treatment need using national survey data.
Reliability-Centered Evaluation of Sparse Longitudinal CT Lesion-Size Forecasting with Conformal Interval Calibration and Gompertz-Inspired Regularization
arXiv stat.ME · 0.70 · 2026-09-21
longitudinal forecasting conformal calibration CT lesion sparse data reliability
Evaluates sparse longitudinal CT lesion‑size forecasting using conformal calibration and Gompertz‑inspired regularization.
Abstract snippet
arXiv:2609.21197v1 Announce Type: cross Abstract: Sparse longitudinal CT follow-up limits lesion-size forecasting when only a few prior observations are available. We constructed a five-visit DLT-derived same-lesion trajectory benchmark from DeepLesion and Deep Lesion Tracker (DLT), yielding 205 trajectories from 129 patients. We compared an exploratory conventional sparse-to-final analysis with a primary fixed visit-index horizon design predicting the common log change from T3 to T4 while…
Randomized Principal Component Ensembles for High-Dimensional Calibration
arXiv stat.ME · 0.70 · 2026-09-21
principal component ensembles calibration high-dimensional survey sampling randomization
Proposes randomized principal component ensembles to improve high‑dimensional calibration in survey sampling.
Abstract snippet
arXiv:2512.09505v2 Announce Type: replace Abstract: Calibration is a widely used method in survey sampling to adjust weights so that estimated totals of some chosen calibration variables match known population totals or totals obtained from other sources. When a large number of auxiliary variables are included as calibration variables, the variance of the total estimator can increase, and the calibration weights can become highly dispersed. To address these issues, we propose an ensemble method…
Continuous Delayed-Memory Stochastic Gradient Descent and Continuous-Time Reinforcement Learning from History of Astrophysical Time Series Studies
arXiv cs.LG · 0.70 · 2026-09-21
stochastic differential equations neural networks reinforcement learning astrophysics time series
Adapts stochastic differential equations with neural network parameters to model astrophysical light curves.
Abstract snippet
arXiv:2609.20906v1 Announce Type: new Abstract: Quasars are luminous objects in the universe that exhibit stochastic brightness variations encoding information about the supermassive black holes powering them, and modeling these variations from ground-based survey data time series, known as light curves, is a statistical challenge. This paper reviews how stochastic differential equations (SDEs) have been adapted with neural network parameterizations to overcome this challenge in history. We…