EHR data and AI to predict response to antidepressant treatment

Antidepressants are frequently prescribed for adults with depression, a common and often disabling psychiatric condition. However, identifying the most effective treatment for a particular patient is often a trial-and-error process that can result in prolonged morbidity, disability, and exposure to adverse effects, as well as substantial healthcare costs. Precision psychiatry aims to optimise treatment matching using patient-specific profiles, but there are few evidence-based predictors available to clinicians initiating antidepressant treatment.

Although average response rates are similar across different antidepressant classes, individual responses can vary widely in clinical practice. Therefore, accurately and scalably guiding antidepressant selection presents specific challenges. The gold standard for characterising antidepressant response from electronic health records (EHRs) remains expert chart review, which is labor- and time-intensive.

However, advances in machine learning (ML) and the growing availability of large-scale health data, such as EHRs, offer new opportunities for developing clinical decision-support tools that may address this challenge. In a recent study published in the peer-reviewed open-access medical journal Nature Partner Journals (npc) Digital Medicine, researchers used machine learning models to accurately predict differential treatment response probabilities for patients and between antidepressant classes based on real-world EHR data. The pipeline incorporated AI and non-AI features, as well as unstructured data (i.e. clinical notes) to maximize the use of information contained in EHRs.

The study included 17,556 patients who received a new antidepressant prescription from non-psychiatrists, and data were obtained from 20 years of EHRs spanning from January 1990 to August 2018. The patients had at least one International Classification of Diseases (ICD) code for depression and at least one ICD code for non-recurrent depression during their history.

ICD codes from EHR data were obtained for adult patients (age ≥ 18 years) with at least one visit (the first visit with an antidepressant prescription is defined as the “index visit” for each patient) with a diagnostic ICD code for a depressive disorder (defined as ICD-9-CM: 296.20–6, 296.30–6, and 311; ICD-10-CM: F32.0–9, F33.0–9) co-occurring with an antidepressant prescription, and at least one ICD code for non-recurrent depression (ICD-9-CM: 296.20–6 and 311; ICD-10-CM: F32.0–9) any time during their history.

The resulting models achieved good accuracy, discrimination, and positive predictive value, which could be valuable for further efforts aiming to provide clinical decision support for prescribers. However, the researchers noted several limitations, including missing data in EHRs(e.g. patients who may receive some of their care outside of the healthcare system), and secular trends in clinician prescribing or documentation practices that may have affected model performance.

In summary, the study presents a novel computational pipeline based on real-world EHR data for predicting differential responses to commonly used classes of antidepressants. The approach demonstrated here could be adapted to a wide variety of other clinical applications for optimising and individualising treatment selection.


  1. Sheu, Yh., Magdamo, C., Miller, M. et al. AI-assisted prediction of differential response to antidepressant classes using electronic health records. npj Digit. Med. 6, 73 (2023).

World Tuberculosis Day 2023

German physician and microbiologist Dr. Robert Koch(b.1843 – d.1910) announced on the 24th March of 1882, that he had discovered the bacterium that causes tuberculosis, which opened the way towards diagnosing and curing this disease.

To commemorate this day in history, World Tuberculosis(TB) Day is observed as a Global Health Day of the World Health Organization(WHO) on 24 March, annually.

This World TB Day global event is to raise public awareness about the devastating health, social and economic consequences of tuberculosis, and to step up efforts to end the global tuberculosis epidemic.

The following DIY poster highlights this event.

To help step up efforts to end the global tuberculosis epidemic, a national health information system(NHIS) should be used to integrate TB data and ensure the data generated by the NHIS are reliable and complete and arrive rapidly enough to be used for a national tuberculosis program (NTP).

A good notification system is thus one of the key elements for the success of a national communicable disease prevention and control program, like the NTP.

The NTP as a national public health surveillance system receives TB notification that uses electronic medical record (EMR) / paper-based medical record data to provide situational awareness for TB-related events.

Although the public health surveillance system leverages the International Classification of Diseases(ICD) from abstracted information about medically coded TB inpatient medical records, ICD codes are not primarily used for public health surveillance purposes.

However, ICD codes provide one way to measure uptake in populations at increased risk of TB, and help provide public-use data files for public analysis, and the NTP to conduct their surveillance of TB through case findings lists to identify cases of reportable TB.

International Pi Day 2023

As it is Pi Day 2023, I like to dedicate this post to relate to the use of pi in Health Information Management(HIM).

We all first met the pi in elementary geometry at school and it occurs in one of the first equations that you can solve; that is to find the circumference or areas of circles.

We remember that Pi (π) is a mathematical constant that represents the ratio of the circumference of a circle to its diameter, approximately equal to 3.14159—when for pi, you substitute the fraction 22/7. Although it is a simple ratio of the circumference of a circle to its diameter, the decimal places appear to go on forever.

Pi is used extensively in mathematics and science, and its calculation has applications in various fields in healthcare too, including HIM.

For this post, I can think of three applications of pi, when pi is integral to HIM.

While the electronic medical record (EMR) is the core informational system for patient management across the healthcare system, the radiology information system (RIS) is considered the core system for the electronic management of imaging departments.

The RIS is the first example I can think of from my past inter-professional collaboration in a hospital setting, that pi is related in one way to HIM, i.e. through its use in medical imaging.

Medical imaging technologies, such as computed tomography (CT) and magnetic resonance imaging (MRI), use pi in their calculations to generate accurate images of the human body. The measurements of these images are based on the circumference and diameter of the scanned area, and pi is used to calculate these measurements accurately.

In addition to the RIS, pi is used in the calculation of various health-related metrics, such as body mass index (BMI) and blood pressure.

Obesity is a common problem worldwide that independently confers risk for chronic disease and early mortality.

HIM professionals use the International Classification of Diseases, Tenth Revision (ICD-10) codes as a tool for medical diagnoses, to medically code obesity, and also to identify obesity documentation in the electronic medical records(EMR) problem list.

Other available sources in the EMR use an individual’s body mass index (BMI) to identify and categorise obesity into the Z codes of ICD-10.

The ability to identify and manage the care of patients who meet the criteria for obesity in ambulatory settings has significantly improved with the increased use of health information technology, especially with the use of EMRs.

It is here in the EMR, that It is worthy to take note of the humble pie in the calculation of BMI involves dividing a person’s weight in kilograms by the square of their height in meters, which involves the use of pi in the calculation of the area of a circle (since the formula for the area of a circle is A = πr^2).

As HIM professionals involved in research, we surely used pi in statistical analysis and modeling in HIM.

For example, pi is used in the calculation of confidence intervals, which are used to estimate the range of values within which a population parameter (such as a mean or proportion) is likely to fall. This is important in HIM, as it allows researchers and analysts to make inferences about the health of a population based on a sample of data.

In summary, pi is used in various ways in HIM, including medical imaging, the calculation of health-related metrics, and statistical analysis and modeling.

Its precise calculation and properties make it a valuable tool in the field of HIM.

Moving to ICD-11

The International Classification of Diseases(ICD) is the international standard for the systematic recording, reporting, analysis, interpretation, and comparison of mortality and morbidity data.

The World Health Organisation (WHO) presented and released the 11th edition of ICD(ICD-11) at the World Health Assembly on May 25, 2019, for adoption by member states,

This release has since come into effect on January 1, 2022, to replace the 10th revision(ICD-10), currently in use.

While ICD-10 is still widely used, unfortunately, despite the updating process, ICD-10 is known to be clinically outdated, and structural changes are needed in some chapters.

There is also an increasing need to operate in an electronic environment, as well as the need to capture more information for morbidity-use cases.

According to the WHO, the 11th revision is a scientifically rigorous product that accurately reflects contemporary health and medical practice and represents a significant upgrade from earlier revisions.

The WHO ICD-11 revision goals include to;
Ensure that ICD-11 will function in an electronic environment by:
a. presenting a digital product
b. providing linkage with terminologies (e.g., SNOMED)
c. defining ICD Categories by “logical operational rules” on their associations and details
d. supporting electronic health records & information systems;

Provide a multi-purpose and coherent classification for mortality, morbidity, primary care, clinical care, research, and public health;

Consistency & interoperability across different uses; and

Deliver an international, multilingual reference standard for scientific comparability, i.e. in English, French, Spanish, Russian, Chinese, and Arabic.

MEDICAL CODING OF RARE DISEASES IN ICD-11: making uncommon diseases evident in health information systems through suitable coding

There are hundreds of rare diseases, disorders, and ailments; the precise number is impossible to determine because it depends on the definitions of both what qualifies as a clinical entity and what the threshold for rarity is.

In some parts of the world, policies that encourage industry investment in the development of treatments for rare diseases are used to determine the threshold for rarity.

Until recently there was no systematic effort to establish an inventory of rare disorders(diseases).

This prevented clinical research from being done and made it difficult to determine and recognise their importance for healthcare planning and budget allocation. 

This led to a lack of knowledge of their epidemiology and poor comprehension of their natural history.

In healthcare coding systems, genetic illnesses and other rare diseases had long been underrepresented because of their individual rarity.

Nonetheless, OrphaNet, first established in 1997, not only collected information on rare diseases published in the scientific literature but also classified each clinical entity being assigned an Orpha number.

Today, OrphaNet has become the reference source of information on rare diseases, providing high-quality information on rare diseases and expertise.

The World Health Organisation Rare Diseases Topic Advisory Group (TAG) was established in April 2007, to ensure that rare diseases would soon be traceable in mortality and morbidity information systems.

Orphanet was tasked with creating the fundamental data that would serve as the foundation for the ICD-11 classification of rare diseases. Given that rare diseases affect many facets of medicine, it helped with the entire ICD revision process.

The following slides show the progress toward the use of ICD-11 in the medical coding of rare diseases.