Natural language processing in clinical neuroscience and psychiatry: A review.

Natural language processing (NLP) is rapidly becoming an important topic in the medical community. The ability to automatically analyze any type of medical document could be the key factor to fully exploit the data it contains. Cutting-edge artificial intelligence (AI) architectures, particularly machine learning and deep learning, have begun to be applied to this topic and have yielded promising results. We conducted a literature search for 1,024 papers that used NLP technology in neuroscience and psychiatry from 2010 to early 2022. After a selection process, 115 papers were evaluated. Each publication was classified into one of three categories: information extraction, classification, and data inference. Automated understanding of clinical reports in electronic health records has the potential to improve healthcare delivery. Overall, the performance of NLP applications is high, with an average F1-score and AUC above 85%. We also derived a composite measure in the form of Z-scores to better compare the performance of NLP models and their different classes as a whole. No statistical differences were found in the unbiased comparison. Strong asymmetry between English and non-English models, difficulty in obtaining high-quality annotated data, and train biases causing low generalizability are the main limitations. This review suggests that NLP could be an effective tool to help clinicians gain insights from medical reports, clinical research forms, and more, making NLP an effective tool to improve the quality of healthcare services.

Drugs don't work in patients who don't take them: Dr. Drin, the new ICT paradigm for chronic therapies.

Poor adherence to drug therapies still represents an unsolved problem. In order to provide a useful solution to chronic patients of all ages--with particular attention to the elderly--who are subjected to complex therapeutic regimen, an innovative ICT solution, called Dr.Drin, has been designed and tested. The aim of the developed framework is to assist the patient during the therapy and to enable and support a bidirectional communication between all healthcare stakeholders (doctors, caregivers and family members) and the patient. During the screening phase, patients were interviewed to understand what are the common practices they usually adopt to remember when and how to take a drug. The solutions which they rely the most on are the list of drugs, writing on the packaging, and setting up alarms. Patients who complained about difficulties of adherence and who had a smartphone were subsequently recruited to test Dr.Drin over a three-months period. In the following, preliminary results from the first twelve patients are presented and analyzed to prove the effectiveness of Dr.Drin in supporting patients adherence to therapies.