Structural and Optical Properties of Pulse Laser Deposited Ag_2O Thin Films

We deposited Ag_2O films in PLD system on glass substrate for a fixed partial oxygen gas pressure (70 mili Torr) with the variation of laser energy from 75 to 215 mJ/Pulse. The XRD patterns confirm that the films have well crystallinity and deposited as hexagonal lattice and the crystalline size increases from 26.38 nm to 27.27 nm. The FESEM images show that the particle size of the films increases from 34.84 nm to 65.83 nm. The composition of the films is analyzed from EDX spectra which show that the percentage of oxygen increases from 41.03% to 48.38% with the increment of laser energy. From the optical characterization, it is observed that the optical band gap appears in the visible optical range in an increasing order from 0.87 to 0.98 eV with the increment of laser energy. Our analysis concludes that the Ag_2O thin films, deposited with these parameters, can be considered as a good absorbent layer for solar photovoltaic application.Comment: 8 pages, 4 figures, 2 table

Relation Classification for Bleeding Events From Electronic Health Records Using Deep Learning Systems: An Empirical Study

BACKGROUND: Accurate detection of bleeding events from electronic health records (EHRs) is crucial for identifying and characterizing different common and serious medical problems. To extract such information from EHRs, it is essential to identify the relations between bleeding events and related clinical entities (eg, bleeding anatomic sites and lab tests). With the advent of natural language processing (NLP) and deep learning (DL)-based techniques, many studies have focused on their applicability for various clinical applications. However, no prior work has utilized DL to extract relations between bleeding events and relevant entities. OBJECTIVE: In this study, we aimed to evaluate multiple DL systems on a novel EHR data set for bleeding event-related relation classification. METHODS: We first expert annotated a new data set of 1046 deidentified EHR notes for bleeding events and their attributes. On this data set, we evaluated three state-of-the-art DL architectures for the bleeding event relation classification task, namely, convolutional neural network (CNN), attention-guided graph convolutional network (AGGCN), and Bidirectional Encoder Representations from Transformers (BERT). We used three BERT-based models, namely, BERT pretrained on biomedical data (BioBERT), BioBERT pretrained on clinical text (Bio+Clinical BERT), and BioBERT pretrained on EHR notes (EhrBERT). RESULTS: Our experiments showed that the BERT-based models significantly outperformed the CNN and AGGCN models. Specifically, BioBERT achieved a macro F1 score of 0.842, outperforming both the AGGCN (macro F1 score, 0.828) and CNN models (macro F1 score, 0.763) by 1.4% (P \u3c .001) and 7.9% (P \u3c .001), respectively. CONCLUSIONS: In this comprehensive study, we explored and compared different DL systems to classify relations between bleeding events and other medical concepts. On our corpus, BERT-based models outperformed other DL models for identifying the relations of bleeding-related entities. In addition to pretrained contextualized word representation, BERT-based models benefited from the use of target entity representation over traditional sequence representation

UMASS_BioNLP at MEDIQA-Chat 2023: Can LLMs generate high-quality synthetic note-oriented doctor-patient conversations?

This paper presents UMASS_BioNLP team participation in the MEDIQA-Chat 2023 shared task for Task-A and Task-C. We focus especially on Task-C and propose a novel LLMs cooperation system named a doctor-patient loop to generate high-quality conversation data sets. The experiment results demonstrate that our approaches yield reasonable performance as evaluated by automatic metrics such as ROUGE, medical concept recall, BLEU, and Self-BLEU. Furthermore, we conducted a comparative analysis between our proposed method and ChatGPT and GPT-4. This analysis also investigates the potential of utilizing cooperation LLMs to generate high-quality datasets

Associations Between Natural Language Processing (NLP) Enriched Social Determinants of Health and Suicide Death among US Veterans

Importance: Social determinants of health (SDOH) are known to be associated with increased risk of suicidal behaviors, but few studies utilized SDOH from unstructured electronic health record (EHR) notes. Objective: To investigate associations between suicide and recent SDOH, identified using structured and unstructured data. Design: Nested case-control study. Setting: EHR data from the US Veterans Health Administration (VHA). Participants: 6,122,785 Veterans who received care in the US VHA between October 1, 2010, and September 30, 2015. Exposures: Occurrence of SDOH over a maximum span of two years compared with no occurrence of SDOH. Main Outcomes and Measures: Cases of suicide deaths were matched with 4 controls on birth year, cohort entry date, sex, and duration of follow-up. We developed an NLP system to extract SDOH from unstructured notes. Structured data, NLP on unstructured data, and combining them yielded six, eight and nine SDOH respectively. Adjusted odds ratios (aORs) and 95% confidence intervals (CIs) were estimated using conditional logistic regression. Results: In our cohort, 8,821 Veterans committed suicide during 23,725,382 person-years of follow-up (incidence rate 37.18/100,000 person-years). Our cohort was mostly male (92.23%) and white (76.99%). Across the five common SDOH as covariates, NLP-extracted SDOH, on average, covered 80.03% of all SDOH occurrences. All SDOH, measured by structured data and NLP, were significantly associated with increased risk of suicide. The SDOH with the largest effects was legal problems (aOR=2.66, 95% CI=.46-2.89), followed by violence (aOR=2.12, 95% CI=1.98-2.27). NLP-extracted and structured SDOH were also associated with suicide. Conclusions and Relevance: NLP-extracted SDOH were always significantly associated with increased risk of suicide among Veterans, suggesting the potential of NLP in public health studies.Comment: Submitted to JAMA Network Ope

RESEARCH ARTICLE OPEN ACCESS Unknown Input Full Order Observer Construction Using Generalized Matrix Inverse

In this paper a design methodology is proposed to provide a constructive solution to the problem of designing a full order observer for linear time invariant systems subjected to unknown disturbances. Necessary conditions for existence of unknown input observers are stated and solved using generalized matrix inverse. The effect of unknown disturbance present in the system is eliminated from the observer by proper selection of gain parameter. Simulation is carried out and results are discussed to illustrate the proposed procedure

An Atypical Presentation of Wegener's Granulomatosis in a Child

Wegener`s granulomatosis is an autoimmune small vessel necrotising vasculitis associated with both granulomatosis and polyangiitis. While its standard form involves the upper and lower respiratory tracts and kidneys, it may essentially involve any organ. We report a case of a 14 year old girl, admitted with fever, cough, haemoptysis, nose bleeds and following admission developed hoarseness of voice.There was anemia, elevated CRP. Chest X-ray and HRCT chest showed a cavitary consolidation of left upper lobe of the lung. In view of respiratory symptoms, fever, haemoptysis and radiological ndings, the child was started on antitubercular therapy to which she did not respond. Subsequently she developed features of nephtitis and Wegener`s granulomatosis was suspected and conrmed by renal biopsy and positive c-ANCA. She was treated with steroids and cyclophosphamide to which she responded dramatically in our institution. We should suspect Wegener`s granulomatosis in any child presenting with respiratory symptoms, nose bleeds and symptoms of nephritis. Presence of cough, haemoptysis, and fever with obvious consolidation with cavitations may not always be tuberculosis

TransformEHR: transformer-based encoder-decoder generative model to enhance prediction of disease outcomes using electronic health records

Abstract Deep learning transformer-based models using longitudinal electronic health records (EHRs) have shown a great success in prediction of clinical diseases or outcomes. Pretraining on a large dataset can help such models map the input space better and boost their performance on relevant tasks through finetuning with limited data. In this study, we present TransformEHR, a generative encoder-decoder model with transformer that is pretrained using a new pretraining objective—predicting all diseases and outcomes of a patient at a future visit from previous visits. TransformEHR’s encoder-decoder framework, paired with the novel pretraining objective, helps it achieve the new state-of-the-art performance on multiple clinical prediction tasks. Comparing with the previous model, TransformEHR improves area under the precision–recall curve by 2% (p < 0.001) for pancreatic cancer onset and by 24% (p = 0.007) for intentional self-harm in patients with post-traumatic stress disorder. The high performance in predicting intentional self-harm shows the potential of TransformEHR in building effective clinical intervention systems. TransformEHR is also generalizable and can be easily finetuned for clinical prediction tasks with limited data