A Machine Learning Approach for Identifying People With Neuroinfectious Diseases in Electronic Health Records: Algorithm Development and Validation.

Background: Identifying neuroinfectious disease (NID) cases using International Classification of Diseases billing codes is often imprecise, while manual chart reviews are labor-intensive. Machine learning models can leverage unstructured electronic health records to detect subtle NID indicators, process large data volumes efficiently, and reduce misclassification. While accurate NID classification is needed for research and clinical decision support, using unstructured notes for this purpose remains underexplored.

Gel Electrolytes in the Development of Textile-Based Power Sources.

The interest in flexible and wearable electronics is increasing in both scientific research and in multiple industry sectors, such as medicine and healthcare, sports, and fashion. Thus, compatible power sources are needed to develop secondary batteries, fuel cells, supercapacitors, sensors, and dye-sensitized solar cells. Traditional liquid electrolytes pose challenges in the development of textile-based electronics due to their potential for leakage, flammability, and limited flexibility.

Harvard Electroencephalography Database: A comprehensive clinical electroencephalographic resource from four Boston hospitals.

Objective: This article presents the Harvard Electroencephalography Database (HEEDB), a large-scale, deidentified, and standardized electroencephalographic (EEG) resource supporting artificial intelligence-driven and reproducible research in epilepsy and broader clinical neuroscience.

Methods: HEEDB aggregates more than 280 000 EEG recordings from more than 108 000 patients across four Harvard-affiliated hospitals. Data are harmonized using the Brain Imaging Data Structure and hosted on the Brain Data Science Platform.

Validation of the International Parkinson and Movement Disorder Society Non-Motor Symptoms Questionnaire (MDS-NMS-Q).

Background: The Movement Disorder Society Non-Motor Rating Scale (MDS-NMS) assesses severity and frequency of non-motor symptoms (NMS) in Parkinson's disease (PD) and is rater-administered. The MDS-NMS Questionnaire (MDS-NMS-Q), developed as a briefer (i.e.

Association of Time to Continuous EEG Initiation With Outcomes in Critically Ill Patients.

Purpose: Continuous electroencephalography (cEEG) is used in the critical care setting for seizure detection and treatment, sedation management, and ischemia detection. Further evidence is needed to support whether early cEEG use can improve outcomes. We examined whether time from admission to cEEG initiation affects outcomes.

Automated phenotyping of mild cognitive impairment and Alzheimer's disease and related dementias using electronic health records.

Objectives: Unstructured and structured data in electronic health records (EHR) are a rich source of information for research and quality improvement studies. However, extracting accurate information from EHR is labor-intensive. Timely and accurate identification of patients with Alzheimer's Disease, related dementias (ADRD), or mild cognitive impairment (MCI) is critical for improving patient outcomes through early intervention, optimizing care plans, and reducing healthcare system burdens.

Identification of Patients With Congestive Heart Failure From the Electronic Health Records of Two Hospitals: Retrospective Study.

Background: Congestive heart failure (CHF) is a common cause of hospital admissions. Medical records contain valuable information about CHF, but manual chart review is time-consuming. Claims databases (using International Classification of Diseases [ICD] codes) provide a scalable alternative but are less accurate.

Piezoelectric PVDF Nanoparticles for Enhanced Antimicrobial Activity via Mechanical Stimulation: A Proof-of-Concept Study.

The alarming rise of antimicrobial resistance is a public health issue, driven by the excessive and improper use of antibiotics, which are becoming less effective against an increasing number of microorganisms. There is an urgent need to find alternative antimicrobial strategies that can bypass bacterial resistance mechanisms. Using physical stimuli to sensitize bacteria to antimicrobial action is one step toward addressing this challenge.

Acylase-Based Coatings on Sandblasted Polydimethylsiloxane-Based Materials for Antimicrobial Applications.

Indwelling medical devices, such as urinary catheters, often experience bacterial colonization, forming biofilms that resist antibiotics and the host's immune defenses through quorum sensing (QS), a chemical communication system. This study explores the development of antimicrobial coatings by immobilizing acylase, a quorum-quenching enzyme, on sandblasted polydimethylsiloxane (PDMS) surfaces. PDMS, commonly used in medical devices, was sandblasted to increase its surface roughness, enhancing acylase attachment.

Automated extraction of post-stroke functional outcomes from unstructured electronic health records.

Purpose: Population level tracking of post-stroke functional outcomes is critical to guide interventions that reduce the burden of stroke-related disability. However, functional outcomes are often missing or documented in unstructured notes. We developed a natural language processing (NLP) model that reads electronic health records (EHR) notes to automatically determine the modified Rankin Scale (mRS).

Automated Extraction of Stroke Severity From Unstructured Electronic Health Records Using Natural Language Processing.

Background: Multicenter electronic health records can support quality improvement and comparative effectiveness research in stroke. However, limitations of electronic health record-based research include challenges in abstracting key clinical variables, including stroke severity, along with missing data. We developed a natural language processing model that reads electronic health record notes to directly extract the National Institutes of Health Stroke Scale score when documented and predict the score from clinical documentation when missing.

Extracting seizure control metrics from clinic notes of patients with epilepsy: A natural language processing approach.

Objectives: Monitoring seizure control metrics is key to clinical care of patients with epilepsy. Manually abstracting these metrics from unstructured text in electronic health records (EHR) is laborious. We aimed to abstract the date of last seizure and seizure frequency from clinical notes of patients with epilepsy using natural language processing (NLP).

Predictors of follow-up care for critically-ill patients with seizures and epileptiform abnormalities on EEG monitoring.

Objective: Post-hospitalization follow-up visits are crucial for preventing long-term complications. Patients with electrographic epileptiform abnormalities (EA) including seizures and periodic and rhythmic patterns are especially in need of follow-up for long-term seizure risk stratification and medication management. We sought to identify predictors of follow-up.

Parents' experience of children with acquired brain injury undergoing neurorehabilitation: a systematic review.

Background: This systematic review aims to synthesise the qualitative evidence exploring parents' experiences of children with acquired brain injury (ABI) undergoing neurorehabilitation during the first year post-injury.

Methods: A systematic review of qualitative research was conducted using thematic synthesis with Thomas and Harden's approach. The population, exposure and outcome model was used for the search strategy.

Assessing Risk of Health Outcomes From Brain Activity in Sleep: A Retrospective Cohort Study.

Background And Objectives: Patterns of electrical activity in the brain (EEG) during sleep are sensitive to various health conditions even at subclinical stages. The objective of this study was to estimate sleep EEG-predicted incidence of future neurologic, cardiovascular, psychiatric, and mortality outcomes.

Methods: This is a retrospective cohort study with 2 data sets.

Identifying inpatient hospitalizations with continuous electroencephalogram monitoring from administrative data.

Background: Continuous electroencephalography (cEEG) is increasingly utilized in hospitalized patients to detect and treat seizures. Epidemiologic and observational studies using administrative datasets can provide insights into the comparative and cost effectiveness of cEEG utilization. Defining patient cohorts that underwent acute inpatient cEEG from administrative datasets is limited by the lack of validated codes differentiating elective epilepsy monitoring unit (EMU) admissions from acute inpatient hospitalization with cEEG utilization.

Real-World Continuous EEG Utilization and Outcomes in Hospitalized Patients With Acute Cerebrovascular Diseases.

Purpose: Continuous electroencephalography (cEEG) is recommended for hospitalized patients with cerebrovascular diseases and suspected seizures or unexplained neurologic decline. We sought to (1) identify areas of practice variation in cEEG utilization, (2) determine predictors of cEEG utilization, (3) evaluate whether cEEG utilization is associated with outcomes in patients with cerebrovascular diseases.

Methods: This cohort study of the Premier Healthcare Database (2014-2020), included hospitalized patients age > 18 years with cerebrovascular diseases (identified by ICD codes).

Brain health scores to predict neurological outcomes from electronic health records.

Background: Preserving brain health is a critical priority in primary care, yet screening for these risk factors in face-to-face primary care visits is challenging to scale to large populations. We aimed to develop automated brain health risk scores calculated from data in the electronic health record (EHR) enabling population-wide brain health screening in advance of patient care visits.

Methods: This retrospective cohort study included patients with visits to an outpatient neurology clinic at Massachusetts General Hospital, between January 2010 and March 2021.

Identifying inpatient hospitalizations with continuous electroencephalogram monitoring from administrative data.

Background: Continuous electroencephalography (cEEG) is increasingly utilized in hospitalized patients to detect and treat seizures. Epidemiologic and observational studies using administrative datasets can provide insights into the comparative and cost effectiveness of cEEG utilization. Defining patient cohorts that underwent acute inpatient cEEG from administrative datasets is limited by the lack of validated codes differentiating elective epilepsy monitoring unit (EMU) admissions from acute inpatient hospitalization with cEEG utilization.