The Quality Application of Deep Learning in Clinical Outcome Predictions Using Electronic Health Record Data: A Systematic Review

Introduction: Electronic Health Record (EHR) is a significant source of medical data that can be used to develop predictive modelling with therapeutically useful outcomes. Predictive modelling using EHR data has been increasingly utilized in healthcare, achieving outstanding performance and improving healthcare outcomes. Objectives: The main goal of this review study is to examine different deep learning approaches and techniques used to EHR data processing. Methods: To find possibly pertinent articles that have used deep learning on EHR data, the PubMed database was searched. Using EHR data, we assessed and summarized deep learning performance in a number of clinical applications that focus on making specific predictions about clinical outcomes, and we compared the outcomes with those of conventional machine learning models. Results: For this study, a total of 57 papers were chosen. There have been five identified clinical outcome predictions: illness (n=33), intervention (n=6), mortality (n=5), Hospital readmission (n=7), and duration of stay (n=1). The majority of research (39 out of 57) used structured EHR data. RNNs were used as deep learning models the most frequently (LSTM: 17 studies, GRU: 6 research). The analysis shows that deep learning models have excelled when applied to a variety of clinical outcome predictions. While deep learning's application to EHR data has advanced rapidly, it's crucial that these models remain reliable, offering critical insights to assist clinicians in making informed decision. Conclusions: The findings demonstrate that deep learning can outperform classic machine learning techniques since it has the advantage of utilizing extensive and sophisticated datasets, such as longitudinal data seen in EHR. We think that deep learning will keep expanding because it has been quite successful in enhancing healthcare outcomes utilizing EHR data

Nutrient Diagnosis Norms for Date Palm (Phoenix dactylifera L.) in Tunisian Oases

Several studies have pointed out the promising use of nutritional diagnosis methods for the determination of optimum nutrient contents in plant tissues. The present investigation was carried out in different oases in Southern Tunisia to determine reference values for the interpretation of leaf analyses of date palm (Phoenix dactylifera) Deglet Nour cultivar with the Critical Value Approach (CVA) and the Compositional Nutrient Diagnosis (CND). A database (n = 100) of yield and mineral concentrations taken from date palm leaflets in October, at the maturity stage of dates, was used. The yield cut-off between low-yield and high-yield subpopulations, selected from cumulative variance ratio functions across survey data, was 76 kg palm−1 and the global nutrient imbalance index (CNDr2) was 10.06. Critical CND nutrient indices were found to be symmetrical around zero as follows: (1.59; +1.59) for IN, (−0.44, +0.44) for IP, (−0.63, +0.63) for IK, (−0.94, +0.94) for ICa, (−1.05, +1.05) for IMg, (−0.80, +0.80) for IFe, (−0.74, +0.74) for ICu, (−0.80, +0.80) for IB, (−0.93, +0.93) for IZn, (−1.04, +1.04) for IMn, and (−1.03, +1.03) for the residual value. Compared to CND, the CVA approach shows weak detection of the nutrients that cause nutritional imbalance. CND indices revealed, except for N, the presence of nutrient imbalances and the necessity to correct the mineral nutrition of date palm in the Kebeli oases

Transcutaneous canine breast cancer detection in Tunisia: a pilot study

Abstract Background Breast cancer in Tunisia is often diagnosed at a late stage with long delay in time to consultation and to diagnosis.The aim of this study is to estimate the sensitivity and specificity of the transcutaneous breast cancer detection by canine olfactionin Tunisian women and to identify the potential confounding factors. Methods This is a diagnostic case control study that took place from October 2021 to November 2022 in the Department of Medical Oncology at the University Hospital Farhat Hached of Sousse and in the security and training dog center located in Sousse (K9 Dog Center Security & Training). A two-year-old male Belgian Malinois was trained to detect breast cancer on skin secretion samples in compresses that had been worn overnight by women on their breast and then a double-blind testing was performed. There was no contact between women and the dog. From the mentioned responses of the dog, four parameters were calculated: sensitivity, specificity, Positive Predictive Value (PPV) and Negative Predictive Value (NPV). Results Two hundred women were included in this trial: 100 breast cancer (BC) patients recruited from Farhat Hached University Hospital of Sousse and 100 healthy volunteers (HV).The calculated sensitivity was 84% (95% CI 78–89%) and the calculated specificity was 81% (95% CI 75–86%). The calculated predictive values were: PPV = 83,51% (95% CI 78,37–88,65%) and NPV = 81,55% (95% CI 76.17–86.93%). In the multivariate study, only four confounding factors of test’s sensitivity were retained: age (OR = 1.210 [95% CI = 1.085–1.349]; p = 0.001), history of diabetes(OR = 0.017 [95% CI = 0.001–0.228]; p = 0.002), sampling at hospital (OR = 0.010 [95% CI = 0.003–0.464]; p = 0.010) and testing during chemotherapy courses (OR = 0.034 [95% CI = 0.003–0.404]; p = 0.007).For test’s specificity, we retained the three following confounding factors: age (OR = 1,104 [95% CI = 1.021–1.195]; p = 0.014), history of benign mastopathy (OR = 0.243 [95% CI = 0.074–0.805]; p = 0.021)and history of arterial hypertension (OR = 0.194 [95% CI = 0.053–0.707]; p = 0.013). Conclusion This is a pilot study that opens new avenues in developing a reliable cancer diagnostic tool that integrates the dog's olfactory ability to detect breast cancer using a transcutaneous sampling method. It could be a pre-test to select patients who are eligible to a screening mammogram, especially in low-income countries where there is no national mammography screening program. Pactr.org identifier PACTR202201864472288, registration date 11/01/2022