An Artificial Intelligence approach for epidemiological samples and their implementation in mental healthcare systems


   Institute of Psychiatry, Psychology and Neuroscience

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  Dr Paris Alexandros Lalousis, Prof Nikolaos Koutsouleris  No more applications being accepted  Funded PhD Project (Students Worldwide)

About the Project

Artificial Intelligence holds tremendous potential to revolutionise decision-making in mental healthcare systems. By analysing vast amounts of patient data and identifying patterns, AI algorithms can assist clinicians in accurate diagnosis, personalised treatment plans, and timely interventions. AI-powered tools can also monitor patient progress, provide real-time feedback, and offer support to both patients and healthcare providers. Indeed, such applications have been achieved in other areas of medicine. Data from medical records have been used to identify individuals at risk of intensive care use, imaging data have shown to enhance detection of diminutive adenomas and hyperplastic polyps and treatment response prediction to fifteen distinct cancer types has been achieved using advanced machine learning approaches. Such advancements applied in mental healthcare systems hold promise to enhance the quality and accessibility of services, leading to improved outcomes and a better understanding of mental illnesses.

While AI algorithms have shown remarkable potential in psychiatric research, their full impact on healthcare remains limited without translation into clinical settings. Bridging this gap is crucial to unlock AI's benefits in patient care. This PhD project will utilise existing multimodal data (neuroimaging, clinical records, genomics, blood-based biomarkers) from the EU FP-7 funded PRONIA study, the UK Biobank, the German National Cohort (NAKO) as well as the Clinical Record Interactive Search (CRIS) system within the NIHR Maudsley Biomedical Research Centre. The student will be trained in novel machine and deep learning methods to build predictive models of mental health disorders as well as examine their applicability to real-world clinical data (CRIS) and epidemiological data (UK Biobank and NAKO).

The student will be based at the Artificial Intelligence in Mental Health (AIM) lab which is co-led by the Chair of Precision Psychiatry Professor Nikolaos Koutsouleris and the Lecturer in Artificial Intelligence in Mental Health Dr Paris Alexandros Lalousis. It is a new lab based in the Department of Psychosis Studies at the Institute of Psychiatry, Psychology & Neuroscience. The student will benefit from access to a strong network of collaborators and research innovation within the Institute of Psychiatry, Psychology & Neuroscience, the NIHR Maudsley Biomedical Research Centre, as well as the Early Psychosis Studies and the Section for Precision Psychiatry in Munich. This will provide a rich and diverse research environment, helping the student develop their skills and knowledge, and a strong professional network. Specific training will include data analysis using shallow and deep learning techniques and curation/harmonization of existing phenotypic data across the aforementioned cohorts; these are essential skills in mental health research and will therefore be valuable for the student's career development. The studentship will come with a 2- to 4-month residency in Munich for deep/machine learning training in Professor Nikolaos Koutsouleris’ lab.

We are looking for candidates who have strong interpersonal skills, a willingness to learn from and teach others, a desire to be an innovative leader in the field, and strong technical and analytical abilities.

For more information, please use the following link: An Artificial Intelligence approach for epidemiological samples and their implementation in mental healthcare systems | Institute of Psychiatry, Psychology & Neuroscience | King’s College London (kcl.ac.uk)

When applyin, in the Funding section, please tick box 5 and include the following reference: (PL-IOPPN-CRIS-NK-24)

Biological Sciences (4) Computer Science (8) Mathematics (25) Psychology (31)
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 About the Project