Angus Roberts
Biographic Data
| ID | 7651949 |
|---|---|
| NAME | Angus Roberts |
| GIVEN NAMES | Angus |
| FAMILY NAME | Roberts |
| SIGNATURE | ROBERTS A |
| AFFILIATIONS | King's College London |
| ORCID | 0000-0002-4570-9801 |
| VERIFIED | Yes |
| TOTAL WORKS | 4 |
| TOTAL CITATIONS | 0 |
| AUTHOR COUNT | 4 |
| EDITOR COUNT | 0 |
| FIRST PUBLICATION YEAR | 2017 |
| LATEST PUBLICATION YEAR | 2026 |
| H-INDEX | 0 |
The association between older adult abuse and emergency department attendance and mental health service use
Applying neural network algorithms to ascertain reported experiences of violence in routine mental healthcare records and distributions of reports by diagnosis
We thus demonstrate the successful deployment of machine learning based NLP algorithms to ascertain important entities for outcome prediction in mental healthcare. The observed distributions highlight which sex, ethnicity and diagnostic groups had more records of violence victimisation. Further development of these algorithms could usefully capture broader experiences, such as differentiating more efficiently between witnessed, perpetrated and ex…
Identifying features of risk periods for suicide attempts using document frequency and language use in electronic health records
EHR documentation frequency and language use can be used to distinguish periods distal from and proximal to a suicide attempt. However, in our study 55.0% of patients with documentation, prior to their first suicide attempt, did not have a record in the preceding 30 days, meaning that there are a high number who are not seen by services at their most vulnerable point
Language, Structure, and Reuse in the Electronic Health Record
Medical language is at the heart of the electronic health record (EHR), with up to 70 percent of the information in that record being recorded in the natural language, free-text portion. In moving from paper medical records to EHRs, we have opened up opportunities for the reuse of this clinical information through automated search and analysis. Natural language, however, is challenging for computational methods. This paper examines the tension be…
No prominent works on this page.
Language, Structure, and Reuse in the Electronic Health Record
Medical language is at the heart of the electronic health record (EHR), with up to 70 percent of the information in that record being recorded in the natural language, free-text portion. In moving from paper medical records to EHRs, we have opened up opportunities for the reuse of this clinical information through automated search and analysis. Natural language, however, is challenging for computational methods. This paper examines the tension be…
Identifying features of risk periods for suicide attempts using document frequency and language use in electronic health records
EHR documentation frequency and language use can be used to distinguish periods distal from and proximal to a suicide attempt. However, in our study 55.0% of patients with documentation, prior to their first suicide attempt, did not have a record in the preceding 30 days, meaning that there are a high number who are not seen by services at their most vulnerable point
Applying neural network algorithms to ascertain reported experiences of violence in routine mental healthcare records and distributions of reports by diagnosis
We thus demonstrate the successful deployment of machine learning based NLP algorithms to ascertain important entities for outcome prediction in mental healthcare. The observed distributions highlight which sex, ethnicity and diagnostic groups had more records of violence victimisation. Further development of these algorithms could usefully capture broader experiences, such as differentiating more efficiently between witnessed, perpetrated and ex…
The association between older adult abuse and emergency department attendance and mental health service use
Health care (3 works) · Medicine (3 works) · Artificial Intelligence (2 works) · Computer Science (2 works) · Electronic health record (2 works) · Health records (2 works) · Political science (2 works) · Psychology (2 works) · Algorithm (1 works) · Artificial neural network (1 works)