Izzet Turkalp Akbasli

Pediatric Specialist

Hacettepe University

Biography

Dr. Izzet Turkalp Akbaslı is a pioneering figure in pediatric critical care and artificial intelligence, blending clinical expertise with technological innovation to enhance healthcare outcomes. Educated at İnönü and Hacettepe Universities, with specializations in medicine, pediatrics, and autoinflammatory diseases, he practices at Polatlı Duatepe State Hospital and conducts AI research at Hacettepe University. Dr. Akbaslı’s work focuses on developing AI-driven solutions for pediatric care, such as personalized transfusion strategies and intelligent triage applications, demonstrating his proficiency in machine learning, deep learning, and cloud technologies. As an editor for the Journal of Pediatric Critical Care, he actively contributes to advancing the field through scientific publications and projects.

Interests

Pediatric Critical Care
Transfusion Medicine
Artificial Intelligence
Clinical Informatics
Data Science
Machine Learning
Time Series Analysis
NLP & Large Language Models
Cloud Infrastructures Services

Education

Fellowship in Pediatric Emergency Medicine, 2024
Hacettepe University
Residency in Pediatrics, 2018
Hacettepe University
Doctor of Medicine, 2010
Inonu University

Skills

Technical

Python

Data Science

Cloud Operations

Hobbies

Home Barista

Cats

Guitar

Experience

Pediatric Emergency Medicine Fellowship

Hacettepe University

May 2024 – Present Ankara/Turkey

Engaged in a Pediatric Emergency Medicine Fellowship, focusing on leveraging artificial intelligence to enhance the management of emergency department workflows and improve patient care efficiency.

Pediatric Specialist

Polatlı Duatepe State Hospital

May 2023 – Present Ankara/Turkey

Responsibilities include:

Patient Evaluation and Diagnosis
Treatment Planning and Implementation
Health Education and Prevention

Pediatric Resident

Hacettepe University

May 2018 – May 2023 Ankara/Turkey

Trained in pediatric care and researched pediatric critical care and transfusion medicine

General Practitioner

Taksim First Aid Research and Training Hospital

October 2017 – April 2018 Istanbul/Turkey

Served in an emergency department that admits an average of 2500 patients daily.

Publications

Quickly discover relevant content by filtering publications.

Serin 0, Akbasli IT, Bocutcu Cetin S, Koseoglu B, Deveci AF, Ugur MZ, Ozsurekci Y (2024). Advancing primary care for childhood pneumonia: a machine learning-based approach to prognosis and case management. medRxiv.

PDF Cite DOI

Akbasli IT, Serin O (2023). Digital Response to Physical Crises: The Role of an E-Health Platform in the 2023 Southern Turkey Earthquakes. SDMPH.

PDF Cite DOI

Akbasli IT, Bayrakci B (2023). Balancing caution and ınnovation: exploring the potential of large language models in critical decision-making. CC.

PDF Cite DOI

Akbasli IT, Saritas Nakip O, Keisici S, Topaloglu R, Kutluk T, Bayrakci B (2022). TREATMENT OF METHOTREXATE TOXICITY WITH CONVENTIONAL HEMODIALYSIS: A PEDIATRIC CASE SERIES. PCCM.

PDF Cite DOI

Ünlü A, Yılmaz S, Akbasli IT, Karaagac Akyol T, Akkapulu N (2020). MATRA-A: A study on massive transfusion. VOX.

Cite DOI

Projects

Forecasting the Crowding of the PEDER

In our hospital, the high volume of pediatric emergency room (PEDER) admissions was a serious problem. With our project, we aim to accurately forecast pediatric emergency room admissions in advance. By doing so, we can adjust doctors’ schedules to allocate more staff during peak times. This will reduce patient waiting times while lowering the doctors’ immediate workload, ultimately enhancing the quality of patient care. Approximately four hundred seventy thousand records covering eight years were analyzed for this model. We’re analyzing past visit trends, seasonal fluctuations, local events, and other relevant data that typically influence emergency room visit volumes. This real-time model, which generates daily, weekly, and monthly forecasting reports about the admissions number of patients, helps optimize physician staffing by predicting overcrowding.

SpectroHeart

In this project, I built a deep-learning model using audio data collected from early preterm neonates in the NICU. Using labels obtained from Echocardiography for the diagnosis of PDA. This project can help in early, non-invasive detection of PDA, leading to timely interventions and better outcomes for preterm neonates. Subsequently, we converted these audio data into spectrograms and classified them using deep learning algorithms as either having PDA or not PDA. Currently, we have reached two hundred patients and the data collection process is ongoing. We will continue expanding our dataset to improve the model’s robustness and reliability.

Personalized Transfusion Strategy for PICU patients

During my residency, my most significant project involved building a personalized transfusion strategy for PICU patients. We analyzed historical patient data using machine learning algorithms, focusing on variables that impact transfusion requirements, to develop a predictive model. Our model exhibits high performance, as demonstrated by the ROC curve plot. It allowed for more precise and timely transfusions, reducing unnecessary procedures and potential complications, thereby improving patient care.

EchoPLAX-Seg

A deep learning project for segmenting echocardiography (ECHO) parasternal long-axis (PLAX) view images into six primary heart structures using a U-Net-inspired full convolutional network (FCN) architecture.