Timothy Solberg

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No abstract text is available yet for this article.

No abstract text is available yet for this article.

No abstract text is available yet for this article.

Due to the high prevalence of Hepatitis C virus (HCV) infection among individuals born between 1945 and 1965, in 2012 the Centers for Disease Control and Prevention began recommending HCV screening for this birth cohort. As adherence to HCV treatment is essential for sustained virologic response, identifying factors influencing medication adherence is important. The validated Adherence to Refills and Medications Scale (ARMS) is used to study recent medication adherence in those with chronic disease. This cross-sectional pilot study assesses factors associated with reduced adherence, indicated by higher ARMS scores, among individuals in this birth cohort...

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Purpose: The aim was to develop a novel artificial intelligence (AI)-guided clinical decision support system, to predict radiation doses to subsites of the mandible using diagnostic computed tomography scans acquired before any planning of head and neck radiation therapy (RT). Methods and Materials: A dose classifier was trained using RT plans from 86 patients with oropharyngeal cancer; the test set consisted of an additional 20 plans. The classifier was trained to predict whether mandible subsites would receive a mean dose >50 Gy...

Despite widespread adoption of electronic health records (EHRs), most hospitals are not ready to implement data science research in the clinical pipelines. Here, we develop MEDomics, a continuously learning infrastructure through which multimodal health data are systematically organized and data quality is assessed with the goal of applying artificial intelligence for individual prognosis. Using this framework, currently composed of thousands of individuals with cancer and millions of data points over a decade of data recording, we demonstrate prognostic utility of this framework in oncology...

PURPOSE: We sought to investigate the tumor control probability (TCP) of vestibular schwannomas after single-fraction stereotactic radiosurgery (SRS) or hypofractionated SRS over 2 to 5 fractions (fSRS). METHODS AND MATERIALS: Studies (PubMed indexed from 1993-2017) were eligible for data extraction if they contained dosimetric details of SRS/fSRS correlated with local tumor control. The rate of tumor control at 5 years (or at 3 years if 5-year data were not available) were collated...

Purpose: To assess stereotactic radiotherapy (SRT)/stereotactic body radiotherapy (SBRT) practices by polling clinics participating in multi-institutional clinical trials. Methods: The NRG Oncology Medical Physics Subcommittee distributed a survey consisting of 23 questions, which covered general technologies, policies, and procedures used in the Radiation Oncology field for the delivery of SRT/SBRT (9 questions), and site-specific questions for brain SRT, lung SBRT, and prostate SBRT (14 questions)...

PURPOSE: To perform an in depth evaluation of current state of the art techniques in training neural networks to identify appropriate approaches in small datasets. METHOD: 112,120 frontal-view x-ray images from the NIH ChestXray14 dataset were used in our analysis. Two tasks were studied: unbalanced multi-label classification of 14 diseases, and binary classification of pneumonia vs non-pneumonia. All datasets were randomly split into training, validation and testing (70%, 10%, 20%)...

Deep learning algorithms have recently been developed that utilize patient anatomy and raw imaging information to predict radiation dose, as a means to increase treatment planning efficiency and improve radiotherapy plan quality. Current state-of-the-art techniques rely on convolutional neural networks (CNNs) that use pixel-to-pixel loss to update network parameters. However, stereotactic body radiotherapy (SBRT) dose is often heterogeneous, making it difficult to model using pixel-level loss. Generative adversarial networks (GANs) utilize adversarial learning that incorporates image-level loss and is better suited to learn from heterogeneous labels...

IMPORTANCE: Defining clinically meaningful success criteria from patient-reported outcome measures (PROMs) is crucial for clinical audits, research and decision-making. PURPOSE: We aimed to define criteria for a successful outcome 3 and 12 months after surgery for cervical degenerative radiculopathy (CDR) on recommended PROMs. STUDY DESIGN: Prospective cohort study with 12 months follow-up. PATIENT SAMPLE: Patients operated at one or two levels for cervical radiculopathy included in the Norwegian Registry for Spine Surgery (NORspine) from 2011 to 2016...

BACKGROUND: Multiple factors contribute to the etiology of addiction, including genetics, sex, and a number of addiction-related behavioral traits. One behavioral trait where individuals assign incentive salience to food stimuli ("sign-trackers", ST) are more impulsive compared to those that do not ("goal-trackers", GT), as well as more sensitive to drugs and drug stimuli. Furthermore, this GT/ST phenotype predicts differences in other behavioral measures. Recent studies have implicated the gut microbiota as a key regulator of brain and behavior, and have shown that many microbiota-associated changes occur in a sex-dependent manner...

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INTRODUCTION: In intensive care unit (ICU) patients, delirium is frequent, occurs early in ICU admission, and is associated with poor outcomes. Risk models based on clinical factors have shown variable performance in terms of predictive ability. Identification of a candidate biomarker that associates with delirium may lead to a better understanding of disease mechanism, validation biomarker studies, and the ability to develop targeted interventions for prevention and treatment of delirium...

Machine learning is proving invaluable across disciplines. However, its success is often limited by the quality and quantity of available data, while its adoption is limited by the level of trust afforded by given models. Human vs. machine performance is commonly compared empirically to decide whether a certain task should be performed by a computer or an expert. In reality, the optimal learning strategy may involve combining the complementary strengths of humans and machines. Here, we present expert-augmented machine learning (EAML), an automated method that guides the extraction of expert knowledge and its integration into machine-learned models...

OBJECTIVE: Locally recurrent disease is of increasing concern in (non-)small cell lung cancer patients [(N)SCLC]. Local re-irradiation with photons or particles may be of benefit to these patients. In this multicentre in silico trial performed within the Radiation Oncology Collaborative Comparison (ROCOCO) consortium, the doses to the target volumes and organs at risk (OARs) were compared when using several photon and proton techniques in patients with recurrent localized lung cancer scheduled to undergo re-irradiation...

Background: We investigated prognostic models based on clinical, radiologic, and radiomic feature to preoperatively identify meningiomas at risk for poor outcomes. Methods: Retrospective review was performed for 303 patients who underwent resection of 314 meningiomas (57% World Health Organization grade I, 35% grade II, and 8% grade III) at two independent institutions, which comprised primary and external datasets. For each patient in the primary dataset, 16 radiologic and 172 radiomic features were extracted from preoperative magnetic resonance images, and prognostic features for grade, local failure (LF) or overall survival (OS) were identified using the Kaplan-Meier method, log-rank tests and recursive partitioning analysis...

The expansion of machine learning to high-stakes application domains such as medicine, finance, and criminal justice, where making informed decisions requires clear understanding of the model, has increased the interest in interpretable machine learning. The widely used Classification and Regression Trees (CART) have played a major role in health sciences, due to their simple and intuitive explanation of predictions. Ensemble methods like gradient boosting can improve the accuracy of decision trees, but at the expense of the interpretability of the generated model...