Using Artificial Intelligence for The Development

il y a 2 semaines

Lyon, France Laboratoire de Biométrie et Biologie Evolutive (LBBE), Lyon Temps plein

**Using Artificial Intelligence for the Development of a Clinical Prediction Model in Clinical Toxicology (TOX-KBS)**:

- Réf
- **ABG-110251**
- Stage master 2 / Ingénieur- Durée 6 mois- Salaire net mensuel 500- 15/01/2023- Laboratoire de Biométrie et Biologie Evolutive (LBBE), Lyon- Lieu de travail- lyon Auvergne-Rhône-Alpes France- Champs scientifiques- Science de la donnée (stockage, sécurité, mesure, analyse)
- Informatique
- Santé, médecine humaine, vétérinaire
- Mots clés- toxicology, machine learning, probability, AI,-
- 31/03/2023**Établissement recruteur**:

- Service de Pharmacotoxicology- UMR 5558/LBBE/EMETL'équipe EMET (Evaluation et Modélisation des Therapies) sont des médecins, pharmaciens, chercheurs qui travaillent dans le domaine de santé. Une équipe dynamique à l'esprit ouvert qui encadre une dizaine stagiaires de Master 2 et des doctorants par an. Réunion mensuelle 3è mardi au sein du service de Pharmacologie clinique à la faculté de médecine de de pharmacie Laennnec

Pour ce projet précis, l'encadrement conjoint entre deux toxicologues Dr Kim An NGUYEN et Dr Michael CHAY et le professeur Parisa GHODOUS (LIRIS - Université Lyon 1)

Un débouché sur une thèse de science est tout à fait possible et encourageable

**Description**:
**Goal**: Develop a probabilistic model to predict the cases with or without the need for medical facility consulting by using automatic learning algorithms trained on the database. This tool will provide a quick probability of risk evaluation based on the input data, such as age, toxin categories, quantity, clinical effects, and context of exposure (accidental or intentional). We will use Lyon poison center database to develop models.**Specific Aims/Objective**
- data preparation : extraction data from poison center of Lyon and **Choose revelant variables for the model**
- train data sat using different approaches, such as machine learning algorithms (e.g., logistic regression, random forest) and deep learning techniques or others algorithms, will be applied to predict the relationships between dependent variables (often called the 'outcome variable') and independent variables (often called 'predictors,' 'covariates,' or 'features'), such as age, substance, quantity of exposure, delays, and context
- Calibration and internal validation of the model

**Methodology**:
**Data Acquisition. **The data will be deidentified individual case records, provided by Dr. Nguyen. The data will be divided into training, testing, and validation data sets.**AI approaches t**o train data set : random forest, baysian inference, logistic regression, deep learning.**Expected results**:
Successfully developing this knowledge-based system will make pertinent information readily available to medical personnel, aiding in the rapid risk evaluation and diagnosis of toxic exposures. Correct diagnostic and prompt treatment of exposures yields a better prognosis for patients. These models would be helpful in daily use and also in the formation of toxicologists or others clinicians involved in the field. We chose cloud computing (CC) for its many advantages (no need to invest in hardware, software, and management of on-site data centers). After this model validation, a prospective study will allow us to assess the clinical impact of using this artificial intelligence algorithm for identifying patients at risk of severe intoxication.

For future direction, we would like to integrate methods of deep learning and knowledge representation with ontologies into a Markov logic network (MLN). MLNs overcome a significant disadvantage of ontologies because the ontologies cannot represent uncertainty (known as the gray zone). This allows one to compare the likelihood of two strains of reasoning, as one might perform better when creating a differential diagnosis while another performs better comparing alternative hypotheses to explain experimental results. MLNs also overcome a significant disadvantage of neural networks; namely, neural networks do not represent their latent knowledge or information processing in a way that is easily interpretable by humans or that leverages prior knowledge (Chary et al., 2019).

**References**:

- Schipper JD, Dankel DD, 2nd, Arroyo AA, Schauben JL. A knowledge-based clinical toxicology consultant for diagnosing single exposures. Artificial intelligence in medicine. 2012;55(2):87-95.
- Schipper JD, Dankel DD, 2nd, Arroyo AA, Schauben JL. A knowledge-based clinical toxicology consultant for diagnosing multiple exposures. Artificial intelligence in medicine. 2013;58(1):15-21.

**Profil**:
**Competencies of the Students.**
- Master 2 level if background in bioinformatics
- Good level in English
- Tutoring:
- The selected student will work under the supervision of Pr Parisa Ghodous, Dr Nguyen, Dr Chary- Dans le cadre d’un stage de Master 2 rémunéré pour une durée de 6 mois**Contact information ci-dessous, bouton "Postuler".**

**Prise de fonc

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