AI-assisted Drug Re-purposing for Human Liver Fibrosis.

Liver fibrosis is a severe disease with few treatment options due to the poor quality of the available animal and models. To address this, we investigated whether a hypothesis generating multi-agent AI system (AI co-scientist) could assist in re-purposing drugs for treatment of liver fibrosis and direct their experimental characterization. A multi-parameter image analysis workflow, which enabled anti-fibrotic efficacy and drug toxicity to be serially assessed in multi-lineage human hepatic organoids grown in microwells (i.

Predicting cardiovascular disease risk using photoplethysmography and deep learning.

Cardiovascular diseases (CVDs) are responsible for a large proportion of premature deaths in low- and middle-income countries. Early CVD detection and intervention is critical in these populations, yet many existing CVD risk scores require a physical examination or lab measurements, which can be challenging in such health systems due to limited accessibility. We investigated the potential to use photoplethysmography (PPG), a sensing technology available on most smartphones that can potentially enable large-scale screening at low cost, for CVD risk prediction.

An intentional approach to managing bias in general purpose embedding models.

Advances in machine learning for health care have brought concerns about bias from the research community; specifically, the introduction, perpetuation, or exacerbation of care disparities. Reinforcing these concerns is the finding that medical images often reveal signals about sensitive attributes in ways that are hard to pinpoint by both algorithms and people. This finding raises a question about how to best design general purpose pretrained embeddings (GPPEs, defined as embeddings meant to support a broad array of use cases) for building downstream models that are free from particular types of bias.

Developing a Machine Learning Algorithm to Predict the Probability of Medical Staff Work Mode Using Human-Smartphone Interaction Patterns: Algorithm Development and Validation Study.

Background: Traditional methods for investigating work hours rely on an employee's physical presence at the worksite. However, accurately identifying break times at the worksite and distinguishing remote work outside the worksite poses challenges in work hour estimations. Machine learning has the potential to differentiate between human-smartphone interactions at work and off work.

Integrated biosensor platform based on graphene transistor arrays for real-time high-accuracy ion sensing.

Two-dimensional materials such as graphene have shown great promise as biosensors, but suffer from large device-to-device variation due to non-uniform material synthesis and device fabrication technologies. Here, we develop a robust bioelectronic sensing platform  composed of  more than 200 integrated sensing units, custom-built high-speed readout electronics, and machine learning inference that overcomes these challenges to achieve rapid, portable, and reliable measurements. The platform demonstrates reconfigurable multi-ion electrolyte sensing capability and provides highly sensitive, reversible, and real-time response for potassium, sodium, and calcium ions in complex solutions despite variations in device performance.

Explainable deep learning in healthcare: A methodological survey from an attribution view.

The increasing availability of large collections of electronic health record (EHR) data and unprecedented technical advances in deep learning (DL) have sparked a surge of research interest in developing DL based clinical decision support systems for diagnosis, prognosis, and treatment. Despite the recognition of the value of deep learning in healthcare, impediments to further adoption in real healthcare settings remain due to the black-box nature of DL. Therefore, there is an emerging need for interpretable DL, which allows end users to evaluate the model decision making to know whether to accept or reject predictions and recommendations before an action is taken.

Cost-effectiveness analysis of multimodal prognostication in cardiac arrest with EEG monitoring.

Objective: To determine cost-effectiveness parameters for EEG monitoring in cardiac arrest prognostication.

Methods: We conducted a cost-effectiveness analysis to estimate the cost per quality-adjusted life-year (QALY) gained by adding continuous EEG monitoring to standard cardiac arrest prognostication using the American Academy of Neurology Practice Parameter (AANPP) decision algorithm: neurologic examination, somatosensory evoked potentials, and neuron-specific enolase. We explored lifetime cost-effectiveness in a closed system that incorporates revenue back into the medical system (return) from payers who survive a cardiac arrest with good outcome and contribute to the health system during the remaining years of life.

Determining Follow-Up Imaging Study Using Radiology Reports.

Radiology reports often contain follow-up imaging recommendations. Failure to comply with these recommendations in a timely manner can lead to delayed treatment, poor patient outcomes, complications, unnecessary testing, lost revenue, and legal liability. The objective of this study was to develop a scalable approach to automatically identify the completion of a follow-up imaging study recommended by a radiologist in a preceding report.

Medical subdomain classification of clinical notes using a machine learning-based natural language processing approach.

Background: The medical subdomain of a clinical note, such as cardiology or neurology, is useful content-derived metadata for developing machine learning downstream applications. To classify the medical subdomain of a note accurately, we have constructed a machine learning-based natural language processing (NLP) pipeline and developed medical subdomain classifiers based on the content of the note.

Methods: We constructed the pipeline using the clinical NLP system, clinical Text Analysis and Knowledge Extraction System (cTAKES), the Unified Medical Language System (UMLS) Metathesaurus, Semantic Network, and learning algorithms to extract features from two datasets - clinical notes from Integrating Data for Analysis, Anonymization, and Sharing (iDASH) data repository (n = 431) and Massachusetts General Hospital (MGH) (n = 91,237), and built medical subdomain classifiers with different combinations of data representation methods and supervised learning algorithms.

Differentiating intratumoral melanocytes from Langerhans cells in nonmelanocytic pigmented skin tumors in vivo by label-free third-harmonic generation microscopy.

Morphology and distribution of melanocytes are critical imaging information for the diagnosis of melanocytic lesions. However, how to image intratumoral melanocytes noninvasively in pigmented skin tumors is seldom investigated. Third-harmonic generation (THG) is shown to be enhanced by melanin, whereas high accuracy has been demonstrated using THG microscopy for in vivo differential diagnosis of nonmelanocytic pigmented skin tumors.

The process of coping with stress by Taiwanese medical interns: a qualitative study.

Background: Internship, the transition period from medical student to junior doctor, is highly stressful for interns in the West; however, little is known about the experience of interns in coping with stress in Taiwan. This study aimed to develop a model for coping with stress among Taiwanese interns and to examine the relationship between stress and learning outcomes.

Methods: For this qualitative study, we used grounded theory methodology with theoretical sampling.

Gender differences in cardiac autonomic modulation during medical internship.

Medical internship is known to be a time of high stress and long working hours, which increases the risk of depression and cardiovascular disease. Gender differences in medical interns' cardiovascular risk have not been reported previously. Thirty-eight medical interns (29 males) were repeatedly tested for depressive symptoms using the Hospital Anxiety and Depression Scale and 5-min spectral analysis of heart rate variability (HRV) at 3-month intervals during their internship.

DNA vaccines for cervical cancer.

Human papillomavirus (HPV), particularly type 16, has been associated with more than 99% of cervical cancers. There are two HPV oncogenic proteins, E6 and E7, which play a major role in the induction and maintenance of cellular transformation. Thus, immunotherapy targeting these proteins may be employed for the control of HPV-associated cervical lesions.