Spatiotemporal Forecasting of Opioid-Related Fatal Overdoses: Towards Best Practices for Modeling and Evaluation.

To inform public health interventions, researchers have developed models to forecast opioid-related overdose mortality. These efforts often have limited overlap in the models and datasets employed, presenting challenges to assessing progress in this field. Furthermore, common error-based performance metrics, such as root mean squared error (RMSE), cannot directly assess a key modeling purpose: the identification of priority areas for interventions.

Detecting Heart Disease from Multi-View Ultrasound Images via Supervised Attention Multiple Instance Learning.

Aortic stenosis (AS) is a degenerative valve condition that causes substantial morbidity and mortality. This condition is under-diagnosed and under-treated. In clinical practice, AS is diagnosed with expert review of transthoracic echocardiography, which produces dozens of ultrasound images of the heart.

Automated Detection of Aortic Stenosis Using Machine Learning.

Background: Aortic stenosis (AS) is a degenerative valve condition that is underdiagnosed and undertreated. Detection of AS using limited two-dimensional echocardiography could enable screening and improve appropriate referral and treatment of this condition. The aim of this study was to develop methods for automated detection of AS from limited imaging data sets.

Predicting In-Hospital Mortality After Acute Myeloid Leukemia Therapy: Through Supervised Machine Learning Algorithms.

Purpose: Despite careful patient selection, induction chemotherapy for acute myeloid leukemia (AML) is associated with a considerable risk for treatment-related mortality (5%-20%). We evaluated machine learning (ML) algorithms trained using factors available at the time of admission for AML therapy to predict death during the hospitalization.

Methods: We included AML discharges with age > 17 years who received inpatient chemotherapy from State Inpatient Database from Arizona, Florida, New York, Maryland, Washington, and New Jersey for years 2008-2014.

Enzyme Promiscuity Prediction Using Hierarchy-Informed Multi-Label Classification.

Motivation: As experimental efforts are costly and time consuming, computational characterization of enzyme capabilities is an attractive alternative. We present and evaluate several machine-learning models to predict which of 983 distinct enzymes, as defined via the Enzyme Commission (EC) numbers, are likely to interact with a given query molecule. Our data consists of enzyme-substrate interactions from the BRENDA database.

A Framework for Sensorimotor Cross-Perception and Cross-Behavior Knowledge Transfer for Object Categorization.

From an early age, humans learn to develop an intuition for the physical nature of the objects around them by using exploratory behaviors. Such exploration provides observations of how objects feel, sound, look, and move as a result of actions applied on them. Previous works in robotics have shown that robots can also use such behaviors (e.

Predicting change in diagnosis from major depression to bipolar disorder after antidepressant initiation.

We aimed to develop and validate classification models able to identify individuals at high risk for transition from a diagnosis of depressive disorder to one of bipolar disorder. This retrospective health records cohort study applied outpatient clinical data from psychiatry and nonpsychiatry practice networks affiliated with two large academic medical centers between March 2008 and December 2017. Participants included 67,807 individuals with a diagnosis of major depressive disorder or depressive disorder not otherwise specified and no prior diagnosis of bipolar disorder, who received at least one of the nine antidepressant medications.

Assessment of a Prediction Model for Antidepressant Treatment Stability Using Supervised Topic Models.

Importance: In the absence of readily assessed and clinically validated predictors of treatment response, pharmacologic management of major depressive disorder often relies on trial and error.

Objective: To assess a model using electronic health records to identify predictors of treatment response in patients with major depressive disorder.

Design, Setting, And Participants: This retrospective cohort study included data from 81 630 adults with a coded diagnosis of major depressive disorder from 2 academic medical centers in Boston, Massachusetts, including outpatient primary and specialty care clinics from December 1, 1997, to December 31, 2017.

Predicting intervention onset in the ICU with switching state space models.

The impact of many intensive care unit interventions has not been fully quantified, especially in heterogeneous patient populations. We train unsupervised switching state autoregressive models on vital signs from the public MIMIC-III database to capture patient movement between physiological states. We compare our learned states to static demographics and raw vital signs in the prediction of five ICU treatments: ventilation, vasopressor administra tion, and three transfusions.

Recurrent abdominal pain and childhood depression: clinical observations of 23 children and their families.

Children with recurrent abdominal pain without demonstrable organic etiology are studied with their mothers during pediatric hospitalization. Major childhood depressive illness is found which is not masked by the somatic complaint. The mother-child relationship is shown to reflect mutual themes of depression and loss within families where experience with illness and death contributes to a hypochondriacal response to psychic pain.