Knowledge-Informed Machine Learning for Cancer Diagnosis and Prognosis: A Review.

Cancer remains one of the most challenging diseases to treat in the medical field. Machine learning (ML) has enabled in-depth analysis of complex patterns from large, diverse datasets, greatly facilitating "healthcare automation" in cancer diagnosis and prognosis. Despite these advancements, ML models face challenges stemming from limited labeled sample sizes, the intricate interplay of high-dimensionality data types, the inherent heterogeneity observed among patients and within tumors, and concerns about interpretability and consistency with existing biomedical knowledge.

A Cross-Modal Mutual Knowledge Distillation Framework for Alzheimer's Disease Diagnosis: Addressing Incomplete Modalities.

Early detection of Alzheimer's Disease (AD) is crucial for timely interventions and optimizing treatment outcomes. Integrating multimodal neuroimaging datasets can enhance the early detection of AD. However, models must address the challenge of incomplete modalities, a common issue in real-world scenarios, as not all patients have access to all modalities due to practical constraints such as cost and availability.

Weakly-Supervised Transfer Learning with Application in Precision Medicine.

Precision medicine aims to provide diagnosis and treatment accounting for individual differences. To develop machine learning models in support of precision medicine, personalized models are expected to have better performance than one-model-fits-all approaches. A significant challenge, however, is the limited number of labeled samples that can be collected from each individual due to practical constraints.

Identifying and predicting headache trajectories among those with acute post-traumatic headache.

Objectives/background: Post-traumatic headache (PTH) is a common symptom following mild traumatic brain injury (mTBI). Currently, there is no identified way to accurately predict if, when, and at what pace a person will have PTH improvement. In our prior studies, we focused on predicting headache improvement at 3 months post-mTBI.

Longitudinal analysis of pain-induced brain activations in post-traumatic headache.

BackgroundHeadache is a common symptom following mild traumatic brain injury (mTBI). Post-traumatic headache (PTH), a secondary headache disorder that develops after mTBI, often persists for months or years. To identify potential recovery mechanisms and prognostic biomarkers, the present study investigated whether longitudinal changes in pain-induced brain activation differ between healthy controls (HC) and PTH participants showing headache improvement and those without improvement.

Influencers in Policy Fields on Social Media: Global Longitudinal Study of Dietary Sodium Reduction Posts, 2006-2022.

Background: Excessive sodium intake is a major concern for global public health. Despite multiple dietary guidelines, population sodium intakes are above recommended levels. Lack of health literacy could be one contributing issue and contemporary health literacy is largely shaped by social media.

A 4D Theoretical Framework for Measuring Topic-Specific Influence on Twitter: Development and Usability Study on Dietary Sodium Tweets.

Background: Social media has emerged as a prominent approach for health education and promotion. However, it is challenging to understand how to best promote health-related information on social media platforms such as Twitter. Despite commercial tools and prior studies attempting to analyze influence, there is a gap to fill in developing a publicly accessible and consolidated framework to measure influence and analyze dissemination strategies.

Questionnaire and structural imaging data accurately predict headache improvement in patients with acute post-traumatic headache attributed to mild traumatic brain injury.

Background: Our prior work demonstrated that questionnaires assessing psychosocial symptoms have utility for predicting improvement in patients with acute post-traumatic headache following mild traumatic brain injury. In this cohort study, we aimed to determine whether prediction accuracy can be refined by adding structural magnetic resonance imaging (MRI) brain measures to the model.

Methods: Adults with acute post-traumatic headache (enrolled 0-59 days post-mild traumatic brain injury) underwent T1-weighted brain MRI and completed three questionnaires (Sports Concussion Assessment Tool, Pain Catastrophizing Scale, and the Trait Anxiety Inventory Scale).

Developing multivariable models for predicting headache improvement in patients with acute post-traumatic headache attributed to mild traumatic brain injury: A preliminary study.

Objectives/background: Post-traumatic headache (PTH) is a common symptom after mild traumatic brain injury (mTBI). Although there have been several studies that have used clinical features of PTH to attempt to predict headache recovery, currently no accurate methods exist for predicting individuals' improvement from acute PTH. This study investigated the utility of clinical questionnaires for predicting (i) headache improvement at 3 and 6 months, and (ii) headache trajectories over the first 3 months.