Machine Learning Models for Pancreatic Cancer Risk Prediction Using Electronic Health Record Data-A Systematic Review and Assessment.

INTRODUCTION: Accurate risk prediction can facilitate screening and early detection of pancreatic cancer (PC). We conducted a systematic review to critically evaluate effectiveness of machine learning (ML) and artificial intelligence (AI) techniques applied to electronic health records (EHR) for PC risk prediction. METHODS: Ovid MEDLINE(R), Ovid EMBASE, Ovid Cochrane Central Register of Controlled Trials, Ovid Cochrane Database of Systematic Reviews, Scopus, and Web of Science were searched for articles that utilized ML/AI techniques to predict PC, published between January 1, 2012, and February 1, 2024. Study selection and data extraction were conducted by 2 independent reviewers. Critical appraisal and data extraction were performed using the CHecklist for critical Appraisal and data extraction for systematic Reviews of prediction Modelling Studies checklist. Risk of bias and applicability were examined using prediction model risk of bias assessment tool. RESULTS: Thirty studies including 169,149 PC cases were identified. Logistic regression was the most frequent modeling method. Twenty studies utilized a curated set of known PC risk predictors or those identified by clinical experts. ML model discrimination performance (C-index) ranged from 0.57 to 1.0. Missing data were underreported, and most studies did not implement explainable-AI techniques or report exclusion time intervals. DISCUSSION: AI/ML models for PC risk prediction using known risk factors perform reasonably well and may have near-term applications in identifying cohorts for targeted PC screening if validated in real-world data sets. The combined use of structured and unstructured EHR data using emerging AI models while incorporating explainable-AI techniques has the potential to identify novel PC risk factors, and this approach merits further study.

Identification of pancreatic cancer risk factors from clinical notes using natural language processing.

OBJECTIVES: Screening for pancreatic ductal adenocarcinoma (PDAC) is considered in high-risk individuals (HRIs) with established PDAC risk factors, such as family history and germline mutations in PDAC susceptibility genes. Accurate assessment of risk factor status is provider knowledge-dependent and requires extensive manual chart review by experts. Natural Language Processing (NLP) has shown promise in automated data extraction from the electronic health record (EHR). We aimed to use NLP for automated extraction of PDAC risk factors from unstructured clinical notes in the EHR. METHODS: We first developed rule-based NLP algorithms to extract PDAC risk factors at the document-level, using an annotated corpus of 2091 clinical notes. Next, we further improved the NLP algorithms using a cohort of 1138 patients through patient-level training, validation, and testing, with comparison against a pre-specified reference standard. To minimize false-negative results we prioritized algorithm recall. RESULTS: In the test set (n = 807), the NLP algorithms achieved a recall of 0.933, precision of 0.790, and F1-score of 0.856 for family history of PDAC. For germline genetic mutations, the algorithm had a high recall of 0.851, while precision and F1-score were lower at 0.350 and 0.496 respectively. Most false positives for germline mutations resulted from erroneous recognition of tissue mutations. CONCLUSIONS: Rule-based NLP algorithms applied to unstructured clinical notes are highly sensitive for automated identification of PDAC risk factors. Further validation in a large primary-care patient population is warranted to assess real-world utility in identifying HRIs for pancreatic cancer screening.

Machine Learning-Based Classification of Abnormal Liver Tissues Using Relative Permittivity.

The search for non-invasive, fast, and low-cost diagnostic tools has gained significant traction among many researchers worldwide. Dielectric properties calculated from microwave signals offer unique insights into biological tissue. Material properties, such as relative permittivity (εr) and conductivity (σ), can vary significantly between healthy and unhealthy tissue types at a given frequency. Understanding this difference in properties is key for identifying the disease state. The frequency-dependent nature of the dielectric measurements results in large datasets, which can be postprocessed using artificial intelligence (AI) methods. In this work, the dielectric properties of liver tissues in three mouse models of liver disease are characterized using dielectric spectroscopy. The measurements are grouped into four categories based on the diets or disease state of the mice, i.e., healthy mice, mice with non-alcoholic steatohepatitis (NASH) induced by choline-deficient high-fat diet, mice with NASH induced by western diet, and mice with liver fibrosis. Multi-class classification machine learning (ML) models are then explored to differentiate the liver tissue groups based on dielectric measurements. The results show that the support vector machine (SVM) model was able to differentiate the tissue groups with an accuracy up to 90%. This technology pipeline, thus, shows great potential for developing the next generation non-invasive diagnostic tools.

Estimating the Effect of Genital vs Extragenital Warts on Immunocompetent Indian Adult Patients; Quality of Life: A Comparative Cross-Sectional Study.

Introduction: Extragenital warts, often known as EGWs, affect between 7% and 10% of the population. Despite the plethora of research on the impact of genital warts (GWs) on "Quality Of Life", EGWs have received little attention. The purpose of this study was to conduct a cross-sectional investigation with the objective of contrasting the effects of GWs and EGWs on the health-related quality of life and other characteristics. Participants and Procedures: A cross-sectional clinical study was piloted at a tertiary care center. Participants in the study included two groups of healthy adults, each group consisting of 100 adult subjects. Those diagnosed with EGWs were included in group A, while patients diagnosed with GWs made up group B. The "Dermatology Life Quality Index" questionnaire was used to evaluate various parameters. Observations were compared for significance. Results: The majority of the subjects in both the groups were observed to have less than 10 warts. The Dermatology Life Quality Index score for the EGWs had an average of 8.66 ± 2.31 score; GWs had an average of 5.12 ± 3.25. This mean variance was statistically significant. The level of the dissatisfaction was highly significantly different among the groups and the subjects being more in the EGW group dissatisfied. Conclusion: The findings of this investigation indicate that EGWs have a significant and detrimental effect on the Quality Of Life. Medical experts must teach people how to prevent the disease's spread and recurrence due to its persistence. They must also consider the psychological and societal repercussions of the disease while discussing therapy choices.

Fall Risk Prediction in Older Adults Using Free-Text Nursing Notes and Medications in Electronic Health Records.

Nursing notes in Electronic Health Records (EHR) contain critical health information, including fall risk factors. However, an exploration of fall risk prediction using nursing notes is not well examined. In this study, we explored deep learning architectures to predict fall risk in older adults using text in nursing notes and medications in the EHR. EHR predictor data and fall events outcome data were obtained from 162 older adults living at TigerPlace, a senior living facility located in Columbia, MO. We used pre-trained BioWordVec embeddings to represent the words in the clinical notes and medications and trained multiple recurrent neural network-based natural language processing models to predict future fall events. Our final model predicted falls with an accuracy of 0.81, a sensitivity of 0.75, a specificity of 0.83, and an F1 score of 0.82. This preliminary exploratory analysis provides supporting evidence that fall risk can be predicted from clinical notes and medications. Future studies will utilize additional data modalities available in the EHR to potentially improve fall risk prediction from EHR data.

Explainable Fall Risk Prediction in Older Adults Using Gait and Geriatric Assessments.

Older adults aged 65 and above are at higher risk of falls. Predicting fall risk early can provide caregivers time to provide interventions, which could reduce the risk, potentially avoiding a possible fall. In this paper, we present an analysis of 6-month fall risk prediction in older adults using geriatric assessments, GAITRite measurements, and fall history. The geriatric assessments included were Activities of Daily Living (ADL), Instrumental Activities of Daily Living (IADL), Mini-Mental State Examination (MMSE), Geriatric Depression Scale (GDS), and Short Form 12 (SF12). These geriatric assessments are collected by staff nurses regularly in senior care facilities. From the GAITRite assessments on the residents, we included the Functional Ambulatory Profile (FAP) scores and gait speed to predict fall risk. We used the SHAP (SHapley Additive exPlanations) approach to explain our model predictions to understand which predictor variables contributed to increase or decrease the fall risk for an individual prediction. In case of a high fall risk prediction, predictor variables that contributed the most to elevate the risk could be further examined by the health providers for more personalized health interventions. We used the geriatric assessments, GAITRite measurements, and fall history data collected from 92 older adult residents (age = 86.2 ± 6.4, female = 57) to train machine learning models to predict 6-month fall risk. Our models predicted a 6-month fall with an AUC of 0.80 (95% CI of 0.76-0.85), sensitivity of 0.82 (95% CI of 0.74-0.89), specificity of 0.72 (95% CI of 0.67-0.76), F1 score of 0.76 (95% CI of 0.72-0.79), and accuracy of 0.75 (95% CI of 0.72-0.79). These results show that our early fall risk prediction method performs well in identifying residents who are at higher fall risk, which offers care providers and family members valuable time to perform preventive actions.

Efficacy of Spironolactone in Adult Acne in Polycystic Ovary Syndrome Patients an Original Research.

INTRODUCTION: This prospective clinical trial was designed to assess the effects of a long-term therapy with spironolactone, with and without dietary-induced weight-loss, on clinical features, lipid profile, and insulin levels in women with polycystic ovary syndrome (PCOS). MATERIALS AND METHODS: Twenty-five patients (range of age 16-32 year; 13 lean and 12 overweight) fulfilling formal diagnostic criteria for PCOS (oligomenorrhea and/or amenorrhea, biochemical and/or clinical evidence of hyperadrogenism) were studied at baseline and then received oral spironolactone (100 mg/die) for 12 months; association with lifestyle modifications was recommended to all overweight patients. Clinical, endocrine, and metabolic parameters (oral glucose tolerance test [OGTT], lipid profile) were measured at baseline and at the end of the antiandrogen treatment. RESULTS: The therapy was associated with a significant average decline of triglycerides in overweight subjects and with increased high-density lipoprotein-cholesterol levels in lean patients. The insulin levels at 60 min during OGTT, homeostasis model assessment-insulin resistance and area under curve of insulin were significantly lowered in overweight women after 12 months of spironolactone and weight loss and no negative changes in insulin secretion and sensitivity were observed in PCOS women after pharmacological treatment alone. CONCLUSION: The efficacy of spironolactone on the androgenic clinical aspects of PCOS has been confirmed in this study. Furthermore, our data show that long-term treatment with spironolactone exerts no negative effects on lipoprotein profile and glucose metabolism; more relevant beneficial effects on glucose and lipid metabolism were observed when the antiandrogen was associated with weight loss in overweight PCOS women.

Follicular psoriasis causing erythroderma in a child: A rare presentation.

Erythroderma in children is an uncommon, yet striking entity with an incidence of 0.11%. Psoriatic erythroderma accounts for 1.4% of psoriasis cases in children. Follicular psoriasis is an underdiagnosed variant of psoriasis, with only about 15 cases reported till date, characterized by scaly follicular papules on the trunk and extremities. Although two thirds of these reported occurred in adults, cases have been described in children under the age of 10 years. Follicular lesions may present without psoriasis vulgaris elsewhere. We report here a 13-year-old boy who presented with severe erythrodermic psoriasis that started as dark, rough, horny, discrete, follicular papules over knees and elbows, associated with nail and joint involvement. Such a presentation of follicular psoriasis causing erythroderma is uncommonly seen in children and has not yet been reported in literature.