Enhancing the Safe and Effective Use of Medicines in Hospitalized Patients: An AI Led Population Health Study
Lead Research Organisation:
Swansea University
Department Name: College of Science
Abstract
Theme 2 - Biological, Health and Clinical Science
Cancer 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.
Cancer 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.
Studentship Projects
| Project Reference | Relationship | Related To | Start | End | Student Name |
|---|---|---|---|---|---|
| NE/W502911/1 | 01/04/2021 | 31/03/2022 | |||
| 2265644 | Studentship | NE/W502911/1 | 01/10/2019 | 31/12/2023 | Jamie Duell |