A taxonomy for advancing systematic error analysis in multi-site electronic health record-based clinical concept extraction.

BACKGROUND: Error analysis plays a crucial role in clinical concept extraction, a fundamental subtask within clinical natural language processing (NLP). The process typically involves a manual review of error types, such as contextual and linguistic factors contributing to their occurrence, and the identification of underlying causes to refine the NLP model and improve its performance. Conducting error analysis can be complex, requiring a combination of NLP expertise and domain-specific knowledge. Due to the high heterogeneity of electronic health record (EHR) settings across different institutions, challenges may arise when attempting to standardize and reproduce the error analysis process. OBJECTIVES: This study aims to facilitate a collaborative effort to establish common definitions and taxonomies for capturing diverse error types, fostering community consensus on error analysis for clinical concept extraction tasks. MATERIALS AND METHODS: We iteratively developed and evaluated an error taxonomy based on existing literature, standards, real-world data, multisite case evaluations, and community feedback. The finalized taxonomy was released in both .dtd and .owl formats at the Open Health Natural Language Processing Consortium. The taxonomy is compatible with several different open-source annotation tools, including MAE, Brat, and MedTator. RESULTS: The resulting error taxonomy comprises 43 distinct error classes, organized into 6 error dimensions and 4 properties, including model type (symbolic and statistical machine learning), evaluation subject (model and human), evaluation level (patient, document, sentence, and concept), and annotation examples. Internal and external evaluations revealed strong variations in error types across methodological approaches, tasks, and EHR settings. Key points emerged from community feedback, including the need to enhancing clarity, generalizability, and usability of the taxonomy, along with dissemination strategies. CONCLUSION: The proposed taxonomy can facilitate the acceleration and standardization of the error analysis process in multi-site settings, thus improving the provenance, interpretability, and portability of NLP models. Future researchers could explore the potential direction of developing automated or semi-automated methods to assist in the classification and standardization of error analysis.

Emerging opportunities of using large language models for translation between drug molecules and indications.

A drug molecule is a substance that changes an organism's mental or physical state. Every approved drug has an indication, which refers to the therapeutic use of that drug for treating a particular medical condition. While the Large Language Model (LLM), a generative Artificial Intelligence (AI) technique, has recently demonstrated effectiveness in translating between molecules and their textual descriptions, there remains a gap in research regarding their application in facilitating the translation between drug molecules and indications (which describes the disease, condition or symptoms for which the drug is used), or vice versa. Addressing this challenge could greatly benefit the drug discovery process. The capability of generating a drug from a given indication would allow for the discovery of drugs targeting specific diseases or targets and ultimately provide patients with better treatments. In this paper, we first propose a new task, the translation between drug molecules and corresponding indications, and then test existing LLMs on this new task. Specifically, we consider nine variations of the T5 LLM and evaluate them on two public datasets obtained from ChEMBL and DrugBank. Our experiments show the early results of using LLMs for this task and provide a perspective on the state-of-the-art. We also emphasize the current limitations and discuss future work that has the potential to improve the performance on this task. The creation of molecules from indications, or vice versa, will allow for more efficient targeting of diseases and significantly reduce the cost of drug discovery, with the potential to revolutionize the field of drug discovery in the era of generative AI.

Distilling large language models for matching patients to clinical trials.

OBJECTIVE: The objective of this study is to systematically examine the efficacy of both proprietary (GPT-3.5, GPT-4) and open-source large language models (LLMs) (LLAMA 7B, 13B, 70B) in the context of matching patients to clinical trials in healthcare. MATERIALS AND METHODS: The study employs a multifaceted evaluation framework, incorporating extensive automated and human-centric assessments along with a detailed error analysis for each model, and assesses LLMs' capabilities in analyzing patient eligibility against clinical trial's inclusion and exclusion criteria. To improve the adaptability of open-source LLMs, a specialized synthetic dataset was created using GPT-4, facilitating effective fine-tuning under constrained data conditions. RESULTS: The findings indicate that open-source LLMs, when fine-tuned on this limited and synthetic dataset, achieve performance parity with their proprietary counterparts, such as GPT-3.5. DISCUSSION: This study highlights the recent success of LLMs in the high-stakes domain of healthcare, specifically in patient-trial matching. The research demonstrates the potential of open-source models to match the performance of proprietary models when fine-tuned appropriately, addressing challenges like cost, privacy, and reproducibility concerns associated with closed-source proprietary LLMs. CONCLUSION: The study underscores the opportunity for open-source LLMs in patient-trial matching. To encourage further research and applications in this field, the annotated evaluation dataset and the fine-tuned LLM, Trial-LLAMA, are released for public use.

Large language models for biomedicine: foundations, opportunities, challenges, and best practices.

OBJECTIVES: Generative large language models (LLMs) are a subset of transformers-based neural network architecture models. LLMs have successfully leveraged a combination of an increased number of parameters, improvements in computational efficiency, and large pre-training datasets to perform a wide spectrum of natural language processing (NLP) tasks. Using a few examples (few-shot) or no examples (zero-shot) for prompt-tuning has enabled LLMs to achieve state-of-the-art performance in a broad range of NLP applications. This article by the American Medical Informatics Association (AMIA) NLP Working Group characterizes the opportunities, challenges, and best practices for our community to leverage and advance the integration of LLMs in downstream NLP applications effectively. This can be accomplished through a variety of approaches, including augmented prompting, instruction prompt tuning, and reinforcement learning from human feedback (RLHF). TARGET AUDIENCE: Our focus is on making LLMs accessible to the broader biomedical informatics community, including clinicians and researchers who may be unfamiliar with NLP. Additionally, NLP practitioners may gain insight from the described best practices. SCOPE: We focus on 3 broad categories of NLP tasks, namely natural language understanding, natural language inferencing, and natural language generation. We review the emerging trends in prompt tuning, instruction fine-tuning, and evaluation metrics used for LLMs while drawing attention to several issues that impact biomedical NLP applications, including falsehoods in generated text (confabulation/hallucinations), toxicity, and dataset contamination leading to overfitting. We also review potential approaches to address some of these current challenges in LLMs, such as chain of thought prompting, and the phenomena of emergent capabilities observed in LLMs that can be leveraged to address complex NLP challenge in biomedical applications.

An Empirical Evaluation of Prompting Strategies for Large Language Models in Zero-Shot Clinical Natural Language Processing: Algorithm Development and Validation Study.

BACKGROUND: Large language models (LLMs) have shown remarkable capabilities in natural language processing (NLP), especially in domains where labeled data are scarce or expensive, such as the clinical domain. However, to unlock the clinical knowledge hidden in these LLMs, we need to design effective prompts that can guide them to perform specific clinical NLP tasks without any task-specific training data. This is known as in-context learning, which is an art and science that requires understanding the strengths and weaknesses of different LLMs and prompt engineering approaches. OBJECTIVE: The objective of this study is to assess the effectiveness of various prompt engineering techniques, including 2 newly introduced types-heuristic and ensemble prompts, for zero-shot and few-shot clinical information extraction using pretrained language models. METHODS: This comprehensive experimental study evaluated different prompt types (simple prefix, simple cloze, chain of thought, anticipatory, heuristic, and ensemble) across 5 clinical NLP tasks: clinical sense disambiguation, biomedical evidence extraction, coreference resolution, medication status extraction, and medication attribute extraction. The performance of these prompts was assessed using 3 state-of-the-art language models: GPT-3.5 (OpenAI), Gemini (Google), and LLaMA-2 (Meta). The study contrasted zero-shot with few-shot prompting and explored the effectiveness of ensemble approaches. RESULTS: The study revealed that task-specific prompt tailoring is vital for the high performance of LLMs for zero-shot clinical NLP. In clinical sense disambiguation, GPT-3.5 achieved an accuracy of 0.96 with heuristic prompts and 0.94 in biomedical evidence extraction. Heuristic prompts, alongside chain of thought prompts, were highly effective across tasks. Few-shot prompting improved performance in complex scenarios, and ensemble approaches capitalized on multiple prompt strengths. GPT-3.5 consistently outperformed Gemini and LLaMA-2 across tasks and prompt types. CONCLUSIONS: This study provides a rigorous evaluation of prompt engineering methodologies and introduces innovative techniques for clinical information extraction, demonstrating the potential of in-context learning in the clinical domain. These findings offer clear guidelines for future prompt-based clinical NLP research, facilitating engagement by non-NLP experts in clinical NLP advancements. To the best of our knowledge, this is one of the first works on the empirical evaluation of different prompt engineering approaches for clinical NLP in this era of generative artificial intelligence, and we hope that it will inspire and inform future research in this area.

Clinical Information Retrieval: A Literature Review.

Clinical information retrieval (IR) plays a vital role in modern healthcare by facilitating efficient access and analysis of medical literature for clinicians and researchers. This scoping review aims to offer a comprehensive overview of the current state of clinical IR research and identify gaps and potential opportunities for future studies in this field. The main objective was to assess and analyze the existing literature on clinical IR, focusing on the methods, techniques, and tools employed for effective retrieval and analysis of medical information. Adhering to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, we conducted an extensive search across databases such as Ovid Embase, Ovid Medline, Scopus, ACM Digital Library, IEEE Xplore, and Web of Science, covering publications from January 1, 2010, to January 4, 2023. The rigorous screening process led to the inclusion of 184 papers in our review. Our findings provide a detailed analysis of the clinical IR research landscape, covering aspects like publication trends, data sources, methodologies, evaluation metrics, and applications. The review identifies key research gaps in clinical IR methods such as indexing, ranking, and query expansion, offering insights and opportunities for future studies in clinical IR, thus serving as a guiding framework for upcoming research efforts in this rapidly evolving field. The study also underscores an imperative for innovative research on advanced clinical IR systems capable of fast semantic vector search and adoption of neural IR techniques for effective retrieval of information from unstructured electronic health records (EHRs). Supplementary Information: The online version contains supplementary material available at 10.1007/s41666-024-00159-4.

Mining Clinical Notes for Physical Rehabilitation Exercise Information: Natural Language Processing Algorithm Development and Validation Study.

BACKGROUND: The rehabilitation of a patient who had a stroke requires precise, personalized treatment plans. Natural language processing (NLP) offers the potential to extract valuable exercise information from clinical notes, aiding in the development of more effective rehabilitation strategies. OBJECTIVE: This study aims to develop and evaluate a variety of NLP algorithms to extract and categorize physical rehabilitation exercise information from the clinical notes of patients who had a stroke treated at the University of Pittsburgh Medical Center. METHODS: A cohort of 13,605 patients diagnosed with stroke was identified, and their clinical notes containing rehabilitation therapy notes were retrieved. A comprehensive clinical ontology was created to represent various aspects of physical rehabilitation exercises. State-of-the-art NLP algorithms were then developed and compared, including rule-based, machine learning-based algorithms (support vector machine, logistic regression, gradient boosting, and AdaBoost) and large language model (LLM)-based algorithms (ChatGPT [OpenAI]). The study focused on key performance metrics, particularly F1-scores, to evaluate algorithm effectiveness. RESULTS: The analysis was conducted on a data set comprising 23,724 notes with detailed demographic and clinical characteristics. The rule-based NLP algorithm demonstrated superior performance in most areas, particularly in detecting the "Right Side" location with an F1-score of 0.975, outperforming gradient boosting by 0.063. Gradient boosting excelled in "Lower Extremity" location detection (F1-score: 0.978), surpassing rule-based NLP by 0.023. It also showed notable performance in the "Passive Range of Motion" detection with an F1-score of 0.970, a 0.032 improvement over rule-based NLP. The rule-based algorithm efficiently handled "Duration," "Sets," and "Reps" with F1-scores up to 0.65. LLM-based NLP, particularly ChatGPT with few-shot prompts, achieved high recall but generally lower precision and F1-scores. However, it notably excelled in "Backward Plane" motion detection, achieving an F1-score of 0.846, surpassing the rule-based algorithm's 0.720. CONCLUSIONS: The study successfully developed and evaluated multiple NLP algorithms, revealing the strengths and weaknesses of each in extracting physical rehabilitation exercise information from clinical notes. The detailed ontology and the robust performance of the rule-based and gradient boosting algorithms demonstrate significant potential for enhancing precision rehabilitation. These findings contribute to the ongoing efforts to integrate advanced NLP techniques into health care, moving toward predictive models that can recommend personalized rehabilitation treatments for optimal patient outcomes.

Leveraging generative AI for clinical evidence synthesis needs to ensure trustworthiness.

Evidence-based medicine promises to improve the quality of healthcare by empowering medical decisions and practices with the best available evidence. The rapid growth of medical evidence, which can be obtained from various sources, poses a challenge in collecting, appraising, and synthesizing the evidential information. Recent advancements in generative AI, exemplified by large language models, hold promise in facilitating the arduous task. However, developing accountable, fair, and inclusive models remains a complicated undertaking. In this perspective, we discuss the trustworthiness of generative AI in the context of automated summarization of medical evidence.

Material and microbial perspectives on understanding the role of biochar in mitigating ammonia inhibition during anaerobic digestion.

With the increasing adoption of carbon-based strategies to enhance methanogenic processes, there is a growing concern regarding the correlation between biochar properties and its stimulating effects on anaerobic digestion (AD) under ammonia inhibition. This study delves into the relevant characteristics and potential mechanisms of biochar in the context of AD system under ammonia inhibition. The introduction of optimized biochar, distinguished by rich CO bond, abundant defect density, and high electronic capacity, resulted in a significant reduction in the lag period of anaerobic digestion system under 5.0 g/L ammonia stress, approximately by around 63 % compared to the control one. Biochar helps regulate the community structure, promotes the accumulation of acetate-consuming bacteria, in the AD system under ammonia inhibition. More examinations show that biochar promotes direct interspecies electron transfer in AD system under ammonia inhibition, as evidenced by diminished levels of bound electroactive extracellular polymeric substances, increased abundance of electroactive bacteria, and notably, the up-regulation of direct interspecies electron transfer associated genes, including the conductive pili and Cytochrome C genes, as revealed by meta-transcriptomic analysis. Additionally, gene expression related to proteins associated with ammonium detoxification were found to be up-regulated in systems supplemented with biochar. These findings provide essential evidence and insights for the selection and potential engineering of effective biochar to enhance AD performance under ammonia inhibition.

Foundation metrics for evaluating effectiveness of healthcare conversations powered by generative AI.

Generative Artificial Intelligence is set to revolutionize healthcare delivery by transforming traditional patient care into a more personalized, efficient, and proactive process. Chatbots, serving as interactive conversational models, will probably drive this patient-centered transformation in healthcare. Through the provision of various services, including diagnosis, personalized lifestyle recommendations, dynamic scheduling of follow-ups, and mental health support, the objective is to substantially augment patient health outcomes, all the while mitigating the workload burden on healthcare providers. The life-critical nature of healthcare applications necessitates establishing a unified and comprehensive set of evaluation metrics for conversational models. Existing evaluation metrics proposed for various generic large language models (LLMs) demonstrate a lack of comprehension regarding medical and health concepts and their significance in promoting patients' well-being. Moreover, these metrics neglect pivotal user-centered aspects, including trust-building, ethics, personalization, empathy, user comprehension, and emotional support. The purpose of this paper is to explore state-of-the-art LLM-based evaluation metrics that are specifically applicable to the assessment of interactive conversational models in healthcare. Subsequently, we present a comprehensive set of evaluation metrics designed to thoroughly assess the performance of healthcare chatbots from an end-user perspective. These metrics encompass an evaluation of language processing abilities, impact on real-world clinical tasks, and effectiveness in user-interactive conversations. Finally, we engage in a discussion concerning the challenges associated with defining and implementing these metrics, with particular emphasis on confounding factors such as the target audience, evaluation methods, and prompt techniques involved in the evaluation process.

Investigations of a 1018 nm gain-switched Yb-doped fiber oscillator.

In this paper, we investigate a 1018 nm gain-switched ytterbium-doped fiber oscillator at a low repetition rate in terms of theory and experiment. Theoretically, a numerical model applicable to a 1018 nm gain-switched ytterbium-doped fiber laser was established. The influence of the pump peak power and active fiber lengths on the 1018 nm gain-switched ytterbium-doped fiber laser was numerically simulated. Experimentally, a compact 1018 nm all-fiber-structured pulsed laser oscillator is constructed, in which a pulse width of 110 ns and a single-pulse energy of 0.1 mJ were obtained. Moreover, the experimental results are in agreement with the numerical simulation ones. To the best of our knowledge, this is the first time that gain-switching technology has been applied to 1018 nm fiber lasers to generate nanosecond pulsed lasers. The model and experimental results can provide a reference for the engineering design of the same type of low repetition rate fiber lasers below the kilohertz level.

Enhancing post-traumatic stress disorder patient assessment: Leveraging Natural Language Processing for Research of Domain Criteria Identification using electronic medical records.

Background: Extracting research of domain criteria (RDoC) from high-risk populations like those with post-traumatic stress disorder (PTSD) is crucial for positive mental health improvements and policy enhancements. The intricacies of collecting, integrating, and effectively leveraging clinical notes for this purpose introduce complexities. Methods: In our study, we created an NLP workflow to analyze electronic medical record (EMR) data, and identify and extract research of domain criteria using a pre-trained transformer-based natural language model, allmpnet-base-v2. We subsequently built dictionaries from 100,000 clinical notes and analyzed 5.67 million clinical notes from 38,807 PTSD patients from the University of Pittsburgh Medical Center. Subsequently, we showcased the significance of our approach by extracting and visualizing RDoC information in two use cases: (i) across multiple patient populations and (ii) throughout various disease trajectories. Results: The sentence transformer model demonstrated superior F1 macro scores across all RDoC domains, achieving the highest performance with a cosine similarity threshold value of 0.3. This ensured an F1 score of at least 80% across all RDoC domains. The study revealed consistent reductions in all six RDoC domains among PTSD patients after psychotherapy. Women had the highest abnormalities of sensorimotor systems, while veterans had the highest abnormalities of negative and positive valence systems. The domains following first diagnoses of PTSD were associated with heightened cue reactivity to trauma, suicide, alcohol, and substance consumption. Conclusions: The findings provide initial insights into RDoC functioning in different populations and disease trajectories. Natural language processing proves valuable for capturing real-time, context dependent RDoC instances from extensive clinical notes.

Constructing mesoporous biochar derived from waste carton: Improving multi-site adsorption of dye wastewater and investigating mechanism.

The development of cost-efficient biochar adsorbent with a simple preparation method is essential to constructing efficient wastewater treatment system. Here, a low-cost waste carton biochar (WCB) prepared by a simple two-step carbonization was applied in efficiently removing Rhodamine B (RhB) in aqueous environment. The maximum ability of WCB for RhB adsorption was 222 mg/g, 6 and 10 times higher than both of rice straw biochar (RSB) and broadbean shell biochar (BSB), respectively. It was mainly ascribed to the mesopore structure (3.0-20.4 nm) of WCB possessing more spatial sites compared to RSB (2.2 nm) and BSB (2.4 nm) for RhB (1.4 nm✕1.1 nm✕0.6 nm) adsorption. Furthermore, external mass transfer (EMT) controlled mass transfer resistance (MTR) of the RhB sorption process by WCB which was fitted with the Langmuir model well. Meanwhile, the adsorption process was dominated by physisorption through van der Waals forces and π-π interactions. A mixture of three dyes in river water was well removed by using WCB. This work provides a straightforward method of preparing mesoporous biochar derived from waste carton with high-adsorption capacity for dye wastewater treatment.

Adopting and expanding ethical principles for generative artificial intelligence from military to healthcare.

In 2020, the U.S. Department of Defense officially disclosed a set of ethical principles to guide the use of Artificial Intelligence (AI) technologies on future battlefields. Despite stark differences, there are core similarities between the military and medical service. Warriors on battlefields often face life-altering circumstances that require quick decision-making. Medical providers experience similar challenges in a rapidly changing healthcare environment, such as in the emergency department or during surgery treating a life-threatening condition. Generative AI, an emerging technology designed to efficiently generate valuable information, holds great promise. As computing power becomes more accessible and the abundance of health data, such as electronic health records, electrocardiograms, and medical images, increases, it is inevitable that healthcare will be revolutionized by this technology. Recently, generative AI has garnered a lot of attention in the medical research community, leading to debates about its application in the healthcare sector, mainly due to concerns about transparency and related issues. Meanwhile, questions around the potential exacerbation of health disparities due to modeling biases have raised notable ethical concerns regarding the use of this technology in healthcare. However, the ethical principles for generative AI in healthcare have been understudied. As a result, there are no clear solutions to address ethical concerns, and decision-makers often neglect to consider the significance of ethical principles before implementing generative AI in clinical practice. In an attempt to address these issues, we explore ethical principles from the military perspective and propose the "GREAT PLEA" ethical principles, namely Governability, Reliability, Equity, Accountability, Traceability, Privacy, Lawfulness, Empathy, and Eutonomy, for generative AI in healthcare. Furthermore, we introduce a framework for adopting and expanding these ethical principles in a practical way that has been useful in the military and can be applied to healthcare for generative AI, based on contrasting their ethical concerns and risks. Ultimately, we aim to proactively address the ethical dilemmas and challenges posed by the integration of generative AI into healthcare practice.

Fair patient model: Mitigating bias in the patient representation learned from the electronic health records.

OBJECTIVE: To pre-train fair and unbiased patient representations from Electronic Health Records (EHRs) using a novel weighted loss function that reduces bias and improves fairness in deep representation learning models. METHODS: We defined a new loss function, called weighted loss function, in the deep representation learning model to balance the importance of different groups of patients and features. We applied the proposed model, called Fair Patient Model (FPM), to a sample of 34,739 patients from the MIMIC-III dataset and learned patient representations for four clinical outcome prediction tasks. RESULTS: FPM outperformed the baseline models in terms of three fairness metrics: demographic parity, equality of opportunity difference, and equalized odds ratio. FPM also achieved comparable predictive performance with the baselines, with an average accuracy of 0.7912. Feature analysis revealed that FPM captured more information from clinical features than the baselines. CONCLUSION: FPM is a novel method to pre-train fair and unbiased patient representations from the EHR data using a weighted loss function. The learned representations can be used for various downstream tasks in healthcare and can be extended to other domains where fairness is important.

Less Likely Brainstorming: Using Language Models to Generate Alternative Hypotheses.

A human decision-maker benefits the most from an AI assistant that corrects for their biases. For problems such as generating interpretation of a radiology report given findings, a system predicting only highly likely outcomes may be less useful, where such outcomes are already obvious to the user. To alleviate biases in human decision-making, it is worth considering a broad differential diagnosis, going beyond the most likely options. We introduce a new task, "less likely brainstorming," that asks a model to generate outputs that humans think are relevant but less likely to happen. We explore the task in two settings: a brain MRI interpretation generation setting and an everyday commonsense reasoning setting. We found that a baseline approach of training with less likely hypotheses as targets generates outputs that humans evaluate as either likely or irrelevant nearly half of the time; standard MLE training is not effective. To tackle this problem, we propose a controlled text generation method that uses a novel contrastive learning strategy to encourage models to differentiate between generating likely and less likely outputs according to humans. We compare our method with several state-of-the-art controlled text generation models via automatic and human evaluations and show that our models' capability of generating less likely outputs is improved.

Nonlinear spectral compression in high-power narrow-linewidth polarization maintaining fiber amplifiers for SBS suppression.

In this paper, we propose a method for narrowing the spectrum in high-power narrow-linewidth polarization-maintaining (PM) fiber amplifiers and investigate its potential for suppressing the stimulated Brillouin scattering (SBS). In this method, in addition to common phase modulation to suppress SBS, precisely designed amplitude modulation is induced to generate self-phase modulation in a high-power PM fiber amplifier. In this co-modulation way, the spectrum can be gradually compressed along the fiber. Compared to phase modulation alone or fiber-Bragg-gratings (FBGs) based narrow-linewidth fiber oscillator schemes, in which the spectrum remains the same or broadens, this scheme can achieve a higher SBS threshold for the same output spectral linewidth. Experiments on a ∼ 3 kW peak power quasi-continuous wave (QCW) fiber amplifier show that the co-modulation scheme can compress the spectrum from 0.25 nm to 0.084 nm as output peak power increases from 13 W to 3.2 kW and enhances the SBS threshold by ∼1.7 times compared to traditional FBGs-based fiber oscillator schemes, and by ∼1.4 times compared to common phase modulation schemes. This co-modulation scheme has the potential for mitigating SBS in high-power fiber amplifiers.

ReDWINE: A clinical datamart with text analytical capabilities to facilitate rehabilitation research.

Rehabilitation research focuses on determining the components of a treatment intervention, the mechanism of how these components lead to recovery and rehabilitation, and ultimately the optimal intervention strategies to maximize patients' physical, psychologic, and social functioning. Traditional randomized clinical trials that study and establish new interventions face challenges, such as high cost and time commitment. Observational studies that use existing clinical data to observe the effect of an intervention have shown several advantages over RCTs. Electronic Health Records (EHRs) have become an increasingly important resource for conducting observational studies. To support these studies, we developed a clinical research datamart, called ReDWINE (Rehabilitation Datamart With Informatics iNfrastructure for rEsearch), that transforms the rehabilitation-related EHR data collected from the UPMC health care system to the Observational Health Data Sciences and Informatics (OHDSI) Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) to facilitate rehabilitation research. The standardized EHR data stored in ReDWINE will further reduce the time and effort required by investigators to pool, harmonize, clean, and analyze data from multiple sources, leading to more robust and comprehensive research findings. ReDWINE also includes deployment of data visualization and data analytics tools to facilitate cohort definition and clinical data analysis. These include among others the Open Health Natural Language Processing (OHNLP) toolkit, a high-throughput NLP pipeline, to provide text analytical capabilities at scale in ReDWINE. Using this comprehensive representation of patient data in ReDWINE for rehabilitation research will facilitate real-world evidence for health interventions and outcomes.

Conversion and impact of dissolved organic matters in a heterogeneous catalytic peroxymonosulfate system for pollutant degradation.

Dissolved organic matters (DOM) are widely present in different water sources, causing significant effects on water treatment processes. Herein, the molecular transformation behavior of DOM during peroxymonosulfate (PMS) activation by biochar for organic degradation in a secondary effluent were comprehensively analyzed. Evolution of DOM was identified and inhibition mechanisms to organic degradation were elucidated. DOM underwent oxidative decarbonization (e.g., -C2H2O, -C2H6, -CH2 and -CO2), dehydrogenation (-2H) and dehydration reactions by ·OH and SO4·-. N and S containing compounds witnessed deheteroatomisation (e.g., -NH, -NO2+H, -SO2, -SO3, -SH2), hydration (+H2O) and N/S oxidation reactions. Among DOM, CHO-, CHON-, CHOS-, CHOP- and CHONP-containing molecules showed moderate inhibition while condensed aromatic compounds and aminosugars exhibited strong and moderate inhibition effects on contaminant degradation. The fundamental information could provide references for the rational regulation of ROS composition and DOM conversion process in a PMS system. This in turn offered theoretical guidance to minimize the interference of DOM conversion intermediates on PMS activation and degradation of target pollutants.

Linearly polarized fiber amplifier with narrow linewidth of 5†kW exhibiting a record output power and near-diffraction-limited beam quality.

In this work, a narrow-linewidth polarization-maintaining (PM) all-fiber amplifier with near-diffraction-limited beam quality and record output power is presented. First, a 4.45-kW PM fiber amplifier with a 3-dB linewidth of 0.08 nm and root mean square (rms) linewidth of 0.22 nm is achieved based on optimized phase modulation. However, the sideband of the spectrum broadens significantly during the amplification process, which is mainly caused by the additional intensity variation of the injected signal. Meanwhile, an up to 5.04-kW linearly polarized fiber laser with a relatively stable spectral bandwidth is achieved by effectively suppressing spectral broadening. At the maximum output power, the rms linewidth is 0.2 nm, the beam quality factor M2 is less than 1.3, the polarization extinction ratio (PER) is 16.5 dB, and the signal-to-noise ratio (SNR) is approximately 53 dB. The further power scaling of the amplifier is mainly limited by the pump power. To the best of our knowledge, this is the maximum output power of a narrow linewidth linearly polarized fiber amplifier to date.