An Electronic Medical Record-Based Prognostic Model for Inpatient Falls: Development and Internal-External Cross-Validation.

Background: Effective fall prevention interventions in hospitals require appropriate allocation of resources early in admission. To address this, fall risk prediction tools and models have been developed with the aim to provide fall prevention strategies to patients at high risk. However, fall risk assessment tools have typically been inaccurate for prediction, ineffective in prevention, and time-consuming to complete.

Evaluating automated machine learning platforms for use in healthcare.

Objective: To describe development and application of a checklist of criteria for selecting an automated machine learning (Auto ML) platform for use in creating clinical ML models.

Materials And Methods: Evaluation criteria for selecting an Auto ML platform suited to ML needs of a local health district were developed in 3 steps: (1) identification of key requirements, (2) a market scan, and (3) an assessment process with desired outcomes.

Results: The final checklist comprising 21 functional and 6 non-functional criteria was applied to vendor submissions in selecting a platform for creating a ML heparin dosing model as a use case.

Some pyrimidohexahydroquinoline candidates: synthesis, DFT, cytotoxic activity evaluation, molecular docking, and studies.

Some hexahydroquinoline candidates were prepared by reacting 2-amino-3-cyano-1-cyclohexylhexahydroquinoline with oxalyl chloride and triethyl orthoformate. The computational chemical approach agreed with the product-testing results. The produced substances were examined for their antiproliferative activity against liver carcinoma (HepG2), breast adenocarcinoma (MCF7), prostate cancer (PC3), and colon cancer (HCT116) cell lines.

The adverse inpatient medication event and frailty (AIME-frail) risk prediction model.

Background: Medication harm affects between 5 and 15% of hospitalised patients, with approximately half of the harm events considered preventable through timely intervention. The Adverse Inpatient Medication Event (AIME) risk prediction model was previously developed to guide a systematic approach to patient prioritisation for targeted clinician review, but frailty was not tested as a candidate predictor variable.

Aim: To evaluate the predictive performance of an updated AIME model, incorporating a measure of frailty, when applied to a new multisite cohort of hospitalised adult inpatients.

Removal of Toxic Lead from Wastewater by Seed Hull.

In this work, we address two concerns at once: waste reduction and the development of a lead removal adsorbent. The potential of seed hull (LSH) powder as an efficient, innovative, and economical adsorbent for Pb(II) absorption was examined in this study. Fourier transform infrared spectroscopy, energy-dispersive X-ray spectroscopy, and scanning electron microscopy investigations were used to determine the structural and morphological properties of the LSH adsorbent.