Enhancing the Safe and Effective Use of Medicines in Hospitalized Patients: An AI Led Population Health Study

主题2--生物、健康和临床科学癌症患者因用药错误而受到伤害的风险增加，因为化疗即使在治疗性剂量下也可能具有显著的毒性，而且癌症患者可能会合并影响他们的耐受性，并通过多药联用增加药物相互作用的可能性。该项目的目的是开发具有电子健康记录和报告的先进机器学习和计算方法，以使癌症治疗中安全有效地使用药物，为住院患者实现可能的最佳结果。这将使癌症药剂师能够被释放，承担更多面对患者的角色，并有更多的时间专注于支持高风险药物的管理和使用。这项研究的目标是-开发可扩展的机器学习方法，用于使用高维数据进行大数据分析。-从与药物安全和患者安全相关的临床报告中提取表型因素。-识别包括药物不良反应在内的用药错误的预测因素，同时考虑并存、当前疾病、联合处方因素、患者相关因素、抗肿瘤药物和医院组织。-整合这些因素，以确定哪组患者在某些药物治疗中更有效。

Theme 2 - Biological, Health and Clinical ScienceCancer patients are at an increased risk of harm from medication errors as chemotherapy can have significant toxicity, even at therapeutic doses, and cancer patients can have co-morbidities which affect their tolerance and increase likelihood of drug interactions through polypharmacy. The aim of this project is to develop advanced machine learning and computational methods with electronic health records and reports to enable the safe and effective use of medicines in cancer treatments to achieve the best possible outcomes for hospitalized patients. This will enable the Specialist Cancer Pharmacist to be released to undertake more patient facing roles, and have additional time to focus on supporting the management and usage of high risk drugs.The objectives of this study are- To develop scalable machine learning methods for big data analytics with high dimensional data.- To extract phenotypic factors from clinical reports associated with drug safety and patient safety.- To identify the predictors of medication errors including adverse drug reactions, while considering co-morbidities, inter-current illness, co-prescribing factors, patient-related factors, antineoplastic drugs and hospital organisation.- To integrate these factors to identify which group of patients are more effective in certain drug treatments.