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README.md

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-# Data for *Advancing Italian Biomedical Information Extraction with Large Language Models: Methodological Insights and Multicenter Practical Application*
+🤗 + 🧑‍⚕️🖊️📚🩺🇮🇹  =  **PsyNIT**
 
-Manuscript available at [arxiv.org/abs/2306.05323](https://arxiv.org/abs/2306.05323)
+From this repository you can download the **[PsyNIT](https://www.sciencedirect.com/science/article/pii/S1532046423002782)** (Psychiatric Ner for ITalian) dataset. 
 
-## Abstract
+**PsyNIT** is a native Italan **NER** (Named Entity Recognition) dataset, composed by [Italian Research Hospital Centro San Giovanni Di Dio Fatebenefratelli](https://www.fatebenefratelli.it/strutture/irccs-brescia).
+It was created starting from 100 electronic medical reports, manually anonymized (removing personal patient data, physicians’ references, dates, and locations). The anonymized documents were annotated by a psychologist with 10 years of experience.
+The electronic medical reports contained various information about patients: demographic variables, medical history, results of tests and medical examinations, reports from medical exams, and more.
+Four sections of such documents were extracted:
+- **Pharmacological history**, usually a structured list of medications that the patient is taking and their dosages.
+- **Remote pathologic history and active disease**, usually a list of past and current relevant diseases.
+- **Cognitive proximate pathological history**, typically unstructured, includes medical examinations the patient has undergone. It also includes information about the patient’s personal life, such as marital status, daily habits, sleep disorders, and any relevant aspects of his/her behavior.
+- **Psychological evaluation**, typically unstructured, reports the result of (neuro)psychological examinations, together with comments from the attending physician.
 
-The introduction of computerized medical records in hospitals has reduced burdensome activities like manual writing and information fetching. However, the data contained in medical records are still far underutilized, primarily because extracting data from unstructured textual medical records takes time and effort. Information Extraction, a subfield of Natural Language Processing, can help clinical practitioners overcome this limitation by using automated text-mining pipelines. In this work, we created the first Italian neuropsychiatric Named Entity Recognition dataset, PsyNIT, and used it to develop a Large Language Model for this task. Moreover, we collected and leveraged three external independent datasets to implement an effective multicenter model, with overall F1-score 84.77%, Precision 83.16%, Recall 86.44%. The lessons learned are: (i) the crucial role of a consistent annotation process and (ii) a fine-tuning strategy that combines classical methods with a "few-shot" approach. This allowed us to establish methodological guidelines that pave the way for Natural Language Processing studies in less-resourced languages.
+The class of entities in PsyNIT are:
+- **Diagnosis and comorbidities** (779 examples, 13.23% of the dataset), including medical concepts that encompass and identify a disease with a clinically classified definition. For our purposes, this class has been used to annotate both the main disease for which the medical report was written, and any other disease or medical condition, pre-existing or coexisting, from which the patient suffers. Examples are : “Neoplasia vescicale” (bladder neoplasia), “Ipoacusia” (hearing loss), “Ipofolatemia” (hypopholatemia).
 
-*Keywords*: Natural Language Processing | Deep Learning | Biomedical Text Mining | Large Language Model | Transformer
+- **Cognitive symptoms** (2386 examples, 40.52% of the dataset), that reflect the individual’s abilities in different cognitive domains. These are various aspects of high-level intellectual functioning, such as processing speed, reasoning, judgment, attention, memory, knowledge, decision-making, planning, language production and comprehension and visuospatial abilities. In neuropsychiatric or cognitive disorders, various cognitive symptoms can be observed, showing the cognitive impairment of patients in different cognitive domains. Examples include: “Anomia” (anomie), “Capacità introspettiva” (introspective ability), “Organizzazione e pianificazione visuospaziale” (visuospatial organization and planning).
+- 
+- **Neuropsychiatric symptoms** (707 examples, 12.01% of the dataset), that refer to a set of non-cognitive symptoms that occur in the majority of patients with dementia during the course of the disease. These symptoms are referred to behavioral changes (such as mood disorders, anxiety, sleep problems, apathy, delusions, hallucinations), behavioral problems (like disinhibition, irritability or aggression), aberrant motor behavior and changes in eating behavior. Examples include: “Apatico” (apathetic), “Sintomi depressivi” (depressive symptoms), “Irritabile” (irritable).
+- 
+- **Drug treatment** (162 examples, 2.75% of the dataset), including any substance used to prevent or treat a medical problem, without dosage. Examples include: “Madopar”, “Urorec”.
+- 
+- **Medical assessment** (1854 examples, 31.49% of the dataset), used to obtain an objective measure or information about a medical condition or disease. Examples include: “EEG” (ElectroEncephaloGram), “MMSE” (Mini-Mental State Examination), “RM encefalo” (brain magnetic resonance imaging).
 
-*Correspondence*: ccrema@fatebenefratelli.eu
+[Check the full paper](https://www.sciencedirect.com/science/article/pii/S1532046423002782) for further details, and feel free to contact us if you have some inquiry!